Patent ID: 12189047

DETAILED DESCRIPTION

Attention is directed toFIG.5, which illustrates a functional view of a positioning system500in accordance with an embodiment. As is discussed below, the system500includes a UE510configured to generate positioning assistance data526based upon a relatively small subset of a base station almanac (BSA)512accessible to a BSA server511within a network514. The BSA512may reside on a server in communication with the BSA server511or may be included on the BSA server511. In one embodiment information from the BSA512is provided to the UE510by a micro-BSA cloud assist server516within the network514.

The functional elements of the UE510include one or more micro-BSA(s)520configured to store information corresponding to the subset of the BSA512received by the UE510from the micro-BSA cloud assist server516. The micro-BSA(s)520may be computed with information of the UE510serving cell ECGI to provide a rough estimate of the UE location. A positioning assistance data calculator524is configured to receive cell parameters from the micro-BSA(s)520for use in generating the assistance data526. The positioning assistance data calculator524may use the ECGI of the serving cell to derive a rough estimate of the UE location from which to calculate the assistance data526. As shown, the assistance data526is provided to a power and timing measurements module530and a position estimator540. As is discussed below, the calculator524is further configured to be responsive to measurement feedback532and position estimate feedback534in providing cell selection feedback536useful in intelligently updating the contents of the micro-BSA(s)520. The position estimator540is configured to provide position estimates and the position estimate feedback534based upon the assistance data526and upon measurements542received from the power and timing measurements module530. The power and timing measurements module530generates the measurements542and the measurement feedback532based upon the assistance data526and feedback546received from the position estimator540.

In one embodiment the position estimator540performs OTDOA calculations based upon the assistance data526and measurements by the UE510of the time of arrival (TOA) of positioning reference signals (PRS) received from the base stations (e.g., eNodeBs) of multiple cells associated with the assistance data526. The position estimator540subtracts the TOA of a reference cell (which may be selected by the UE510using known techniques) from the measured TOAs corresponding to such multiple cells in order to form reference signal time difference (RSTD) or time difference of arrival (TDOA) measurements. These TDOA measurements may be used together with the assistance data526to constrain the position of the UE510to a set of hyperbolas. If the TOA measurements made by the UE510were completely lacking in noise and interference, these hyperbolas would intersect at a single point corresponding to the position of UE510. In practice, however, such noise and interference limits the accuracy at which the position of the UE510may be estimated. As is discussed herein, the use of micro-BSA(s)520in accordance with the disclosure improves the accuracy and efficiency with which the position of estimates of the position of the UE510.

In terms of scale, in an exemplary embodiment the BSA512may contain information corresponding to hundreds of thousands of cells, the micro-BSA(s)520may contain information for hundreds of cells, and the assistance data526may pertain to tens of cells.

This BSA512is typically managed by a mobile network operator (MNO) and includes a database containing the cell parameters defining the network layout. Each cell in the database of the BSA512is typically characterized by a unique cell identifier (ECGI), a latitude and longitude of the cell transmission point, a physical cell index (PCI), antenna aperture and orientation details, transmission power, and various other parameters. The cloud assist server516interacts with the BSA512to provide the UE510with a small subset of the contents of the BSA512. As noted above, the resulting micro-BSA520, from which the assistance data526is derived, may consist of several hundred cells close to the serving cell of the UE510. The storage and download requirements associated with even a 1000 cell micro-BSA520are modest. For example, assuming roughly 120 bits are required to represent the cell parameters for a given cell, only approximately 15 kB is required for a 1000-cell micro-BSA520(i.e., 1000 cells×120 bits/cell×1 kB/8000 bits=15 kB).

The information comprising this 15 kB, 1000-cell micro-BSA may be transferred to the UE510in a few seconds over the wireless link while the UE is in LTE connected mode. A smaller micro-BSA can be requested for shorter download times and less storage, and a larger micro-BSA can be requested for greater coverage and less overall interaction with the cloud assist server516or otherwise with the network514. As a point of reference, for a typical cell density of 1 cell/km2, a 1000-cell micro-BSA520provides coverage for a 1000 km2 area. With a single micro-BSA520many position fixes can be obtained. Therefore, once the information for the micro-BSA520is downloaded, the UE510requires minimal additional interaction with elements of the network514.

Accordingly, during operation of the UE510the position estimator540will be able to generate many position estimates even when the UE510is in motion based solely upon the measurements542and the assistance data526derived from information within the micro-BSA(s)520. This advantageously improves battery life of the UE510and reduces network congestion. This is because almanac information is not provided by the BSA512nor is assistance data otherwise provided to the UE510by network in connection with each position estimate generated by the position estimator. Moreover, positioning accuracy is enhanced relative to the case in which such almanac information and/or assistance data is provided to the UE510to facilitate each position measurement since the UE510may, in some embodiments, employ filtering and other techniques to average or otherwise smooth the position estimates locally generated by the position estimator540. The current state of the art UE-assisted method is considered a “single shot” estimate where assistance data is provided to the UE from the location server, the UE then reports measurements, and the location server estimates location with a single set of measurements. In this approach, the estimation algorithms cannot practically benefit from filtering since continuous measurement reporting is not feasible both from a UE battery drain and network congestion perspective. The measurements542are more efficiently supplied to the position estimator540for enhanced estimation processing.

Since the UE510is aware of its current location, the UE510may be configured to sense when new BSA information is required. For example, when the position estimator540is deriving high-quality position estimates, then no new BSA information is required from the micro-BSA cloud assist server516. The position estimator540can determine if the estimates are of high quality by studying the contours of the likelihood or a posteriori function surface, or by calculating a TDOA residual error vector. The TDOA residual error vector is denoted by e and is given by:
e=r−h({circumflex over (x)})
where r is a TDOA measurement vector for one of the additional position estimates and wherein each element of r includes a TDOA measurement associated with one of the set of cells included in the assistance data526, and where h({circumflex over (x)}) is a TDOA vector for a position estimate {circumflex over (x)} and is given by:

hm(x^)=1c⁢(x^-xm-x^-x1)
where xmis the location of the mth cell and x1is the location of a TDOA reference cell which is included in the assistance data526and selected by the UE510. If the elements in e are relatively small, then the position estimator540has greater confidence that the position estimates are of high quality.

In one embodiment the position estimator540develops position estimates using a TDOA hyperbolic location signal model.

TDOA Hyperbolic Location Signal Model

In embodiments in which the UE510implements downlink time-difference of arrival (TDOA) hyperbolic location estimation, the UE510performs time of arrival (TOA) estimates on surrounding cells. In what follows the term “cells” is used interchangeably with “base stations” or “transmission points”. In addition, the term “RSTD” (i.e., “reference signal time difference” as defined in the 3GPP standards) is used interchangeably with TDOA. The surrounding cells are assumed to be time-synchronized and transmitting positioning reference signals (“pilots”) at some time near τ seconds. More specifically, the kth cell transmits at time
τk=τ+αk,
where αkis a relatively small transmit synchronization term. The TOA of the kth cell is

tk=1c⁢x-xk+τk+βk,
where c is the speed of light in a vacuum, x=[k,y]Tis the Cartesian coordinates of the unknown UE location, xk=[xk, yk]Tis the known Cartesian coordinates of the kth cell, and

βk={0,in⁢a⁢line⁢‐⁢of⁢‐⁢sight⁢(LOS)⁢channel,>0,otherwise,
is a non-line-of-sight (NLOS) bias. Note that the above specifies two dimensions with x and y components, and the formulation extension to three dimensions is done by simply adding a third z component term.

Assume the UE510is synchronized to some serving cell that may or may not be the cell closest to the UE510. Without loss of generality, this serving cell is indexed with k=0, and each of the other cells are indexed with k=1, 2, . . . , K. To synchronize, the UE510estimates the TOA of the serving cell to be
{tilde over (t)}0=t0+γ0′,
where γ0′ is a synchronization error term. The UE510then uses this time estimate to form a relative local time. Adjusting for the serving cell synchronization, the relative TOA of the kth cell becomes

t~k=tk-t~0=1c⁢x-xk+τ+αk+βk-(1c⁢x-x0+τ+α0+β0+γ0′)=1c⁢(x-xk-x-x0)+(αk-α0)+(βk-β0)-γ0′.
The term

1c⁢(x-xk-x-x0)
is the TDOA between the kth cell and the serving cell. Thus, {tilde over (t)}kis a TDOA measure corrupted by cell transmit synchronization error (αk−α0), NLOS bias
(βk−β0), and serving cell synchronization error γ0′. Notice that the process of synchronizing with the serving cell removes the transmission time τ from the relative TOAs.

Next, the UE510performs estimates of the relative TOAs:

t^k=t~k+γk=1c⁢(x-xk-x-x0)+(αk-α0)+(βk-β0)+(γk-γ0′),
k=0, 1, . . . , K, where γkis due to estimation error.

The unknown location of the UE x may now be estimated from the relative TOA estimates {{circumflex over (t)}k}k=0Kand the known cell locations {xk}k=0K. However, a more robust approach may be to first form the TDOA estimates:
rm={circumflex over (t)}i(m)−{circumflex over (t)}j(m),
m=1, 2, . . . , M. The subtraction of the relative TOA estimates removes the synchronization error term γ0′:

rm=t^i⁡(m)-t^j⁡(m)=1c⁢(x-xi⁡(m)-x-xj⁡(m))+(αi⁡(m)-αj⁡(m))+(βi⁡(m)-βj⁡(m))+(γi⁡(m)-γj⁡(m)).
Removal of the synchronization error term may be beneficial in the event that the UE510is not well synchronized to the network. This is the method employed by the 3GPP specification.

It is convenient to represent the TDOA measurements in vector form:

r︸measurement⁢vector=h⁡(x)︸TDOA⁢ground⁢‐⁢truth⁢vector+n︸TDOA⁢noise⁢vector,
where the mth element of r is rm, the mth element of h(x) is

hm(x)=1c⁢(x-xi⁡(m)-x-xj⁡(m)),
and the mth element of the noise vector n is
nm=ηi(m)−nj(m),
with
ηk=αk+βk+γk
being the individual TOA noise component.

The i(m)th cell is considered the RSTD neighbor cell of the mth measurement, and the j(m)th cell is considered the RSTD reference cell of the mth measurement. In the 3GPP specification, a common RSTD reference cell is used: j(m)=kreffor some kref∈{0, 1, . . . , K}. The remaining cells are candidate RSTD neighbor cells. Other strategies for pairing candidate RSTD neighbor cells to reference cells are possible. For example, one such potential pairing strategy may be characterized as all “N choose 2” cell-pairs, with N=K+1. See, e.g., F. Gustafsson and F. Gunnarsson, “Positioning using time-difference of arrival measurements,” 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), Hong Kong, China, 2003). Another possibility is a neighbor index approach such that i(m)=j(m)+1 and j(m)=m−1 for m=1, 2, . . . , M=K. These listed strategies do not preclude the possibility of others, and the ones described have their benefits and costs. For example, the single reference cell is advantageous if a low-error reference cell is selected, but could underperform the nearest-neighbor approach if a high-error reference cell is selected. The exhaustive “N choose 2” may offer the location estimation algorithm with richer information at the cost of higher computational complexity.

Attention is now directed toFIGS.13-15, which illustrate exemplary potential strategies for forming cell-pairs comprised of reference and neighbor cells. Specifically,FIG.13depicts a single cell reference cell pairing strategy;FIG.14illustrates a neighbor index cell pairing strategy; andFIG.15illustrates an N-choose-2 cell pairing strategy.

TDOA Position Estimators

The TDOA position estimator540functions to determine a good estimate for x given the measurements in r. The position estimator540may utilize various different methods in making this determination including, for example, least squares, weighted least squares, and Gaussian maximum likelihood. The methods described below do not preclude the use of other possibilities.

Suppose the position estimator540has estimated the location of the UE510to be at {circumflex over (x)}. At this location the ground-truth TDOA between the i(m)th and j(m)th cell is hm({circumflex over (x)}) while the estimated TDOA is rm. Their difference
em=rm−hm({circumflex over (x)})
is called the residual error. This term is useful since it is computationally realizable while the statistical error nmis unknowable at the receiver of the UE510. The sum of the squared residual error components is:

J⁡(x^)=∑m=1Mem2=∑m=1M(rm-hm(x^))2=(r-h⁡(x^))τ⁢(r-h⁡(x^)).
To the extent the position estimator540is configured to find {circumflex over (x)} to minimize J({circumflex over (x)}), the position estimator540may be characterized as a least-squares (LS) estimator:

x^LS=argminxJ⁡(x)=arg⁢minx(r-h⁡(x))T⁢(r-h⁡(x)).

Suppose some of the measurements in r to be of higher quality than others. It may be beneficial, therefore, for the position estimator540to put more weight on the higher quality estimates and less weight on the lower quality estimates. The weighted least squares (WLS) estimator does this:

x^WLS=argminx∑m=1Mwm⁢em2=argminx∑m=1Mwm(rm-hm(x^))2=argminx(r-h⁡(x))T⁢DWLS(r-h⁡(x)),
where
DWLS=diag(w1,w2, . . . ,wM)
is a diagonal weighting matrix. WLS is equivalent to LS when the weights are all the same.

Now suppose statistical information is available about the measurement vector r. Let p(r|x) be the probability of a measurement vector r when the location of the UE510is at x. This is known as the likelihood function and the estimate

x^ML=argmaxxp⁡(r❘x)
is known as the maximum likelihood (ML) estimate.

A special case of the ML estimator is the Gaussian maximum likelihood (GML) estimator where the likelihood function is expressed as:

p⁡(r❘x)=1(2⁢π)M/2⁢❘"\[LeftBracketingBar]"R❘"\[RightBracketingBar]"1/2⁢exp⁡(-1/2⁢(r-h⁡(x))T⁢R-1(r-h⁡(x))),
where
R=E((n−E(n))(n−E(n))T)
is the M by M covariance matrix of the TDOA noise, E( ) is the expectation operator, |R| denotes the determinate of R, and the superscript −1 denotes matrix inverse. The GML estimator simplifies to

x^GML=argmaxxp⁡(r❘x)=argmaxx1(2⁢π)M/2⁢❘"\[LeftBracketingBar]"R❘"\[RightBracketingBar]"1/2⁢exp⁡(-1/2⁢(r-h⁡(x))T⁢R-1(r-h⁡(x)))=arg⁢minx(r-h⁡(x))T⁢R-1(r-h⁡(x)).
This shows the GML estimator is a type of WLS: GML is WLS where the weighting matrix is the noise covariance inverse.

A generalized weighted least squares estimator is expressed as

x^=arg⁢minx(r-h⁡(x))T⁢W⁡(r-h⁡(x)),
where W is a weighting matrix. For the three estimators identified above:

W={I,for⁢least⁢squaresDWLS,for⁢weighted⁢least⁢squaresR-1,for⁢Gaussian⁢maximum⁢likelihood
with I being the identify matrix.

The implementation of the position estimator540as the above GML estimator assumes the TOA to be drawn from a Gaussian distribution. A known generalization of this framework is to assume the TOA is drawn from a Gaussian Mixture Model (GMM). See, e.g., F. Perez-Cruz, C. Lin and H. Huang, “BLADE: A Universal, Blind Learning Algorithm for ToA Localization in NLOS Channels,” 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, 2016. The GMM framework better accounts for the multipath nature of the cellular radio frequency (RF) environment. Also, with prior statistical information about the location of the UE510, the ML estimator implemented by the position estimator540can be generalized into the maximum a posteriori (MAP) estimator.

The weighted least squares estimator derived above minimizes the quadratic cost function
Q(x)=(r−h(x))TW(r−h(x)).
The minimization can be performed with numerical sampling of a rectangular or hexagonal grid, or by statistical sampling methods like Markov Chain Monte Carlo (MCMC) where the Metropolis Hastings algorithm is one example. Alternatively, it can be solved analytically through Taylor series expansion See, e.g., Torrieri, D. J. “Statistical Theory of Passive Location Systems,”IEEE Trans. on Aerospace and Electronic SystemsAES-20, 2 (March 1984). In the case of Gaussian Maximum Likelihood this cost function is the log of the likelihood function, known as the log-likelihood. Similarly, for maximum a posteriori (MAP) estimation, a similar formulation is derived by incorporating the so-called priori probability.
Adaptive Generation of Assistance Data from Micro-BSA Information

The BSA coherence time may be defined to be the time duration in which the BSA information remains relatively static and useful for positioning. The BSA coherence time is large relative to the position measurement update rate. For example, the BSA coherence time can be on the order of days or months while the position measurements might be updated once an hour. This allows for the same BSA information within the micro-BSA(s)520to be used across multiple position measurement events.

Consider the use case of geofencing. The owner of a valuable asset attaches to it a geofencing tracker device (which could be a simplified implementation of the UE510). The owner wishes to be notified if the asset moves beyond a specified region. For days or months the asset may stay in the specified region. Over this duration of time the contents of the micro-BSA(s)520would be practically static as well, and the position estimator540generates high-quality position estimates. Under these conditions, an implementations of the UE510as a tracker device would require no additional BSA information and therefore would require no interaction with the BSA server511or other cloud server hosting the BSA512.

Notably, in the current cellular positioning state of the art the UE is not location aware. Therefore this network relieving feature is not possible. The current state of the art is not efficient in that assistance data must be downloaded to the UE and measurements uploaded to a location server for each position update. This causes the problem of network congestion and compromises the battery life of the device. In contrast, the UE510is “location aware” so as to better, and more efficiently, enable applications like geofencing. This location awareness also allows for a faster positioning fix, reducing latency and improving time-to-first fix (TTFF). These features allow for battery-efficient breadcrumbing applications where the UE is mostly in a low power sleep mode. It momentarily wakes up, updates its position estimate, then returns to a lower power state. The faster the position updates the more efficient the solution.

As noted in the Background, in the current state of the art the assistance data is comprised of 10s of cells used for positioning and a conventional location server derives a “best set” of cells using a globally unique identifier of a cell. However, this provides the location server with only a very rough seed estimate of the UE location when deriving the set of cells to be used in generating assistance data.

Attention is now directed toFIG.6, which is a flow chart of a sequence of operations600performed by the UE510which highlight one way in which intelligent and adaptive generation of assistance data526within the UE510may be utilized to address this shortcoming in the current state of the art. Again, in one embodiment the micro-BSA(s)520include information pertaining to more cells than the cells represented in the assistance data526. This larger collection of cells in the micro-BSA(s)520provides improved flexibility in generating a good set of cells for the assistance data526. Referring toFIG.6, an initial set of assistance data cells is generated (stage608) using a basic cell ID604for the seed estimate. Then, an initial position estimate is derived with a position estimate that is better than the basic cell ID estimate (stage612). This new estimate is used to re-derive the assistance data and thereby generate improved assistance data (stage616). This is done efficiently on the UE510using the local micro-BSA(s)520without having to interact with the micro-BSA cloud assist server516or otherwise having to interact with the network514. An improved position estimate may then be generated using the improved assistance data (stage620). This method600of improved seed estimates to generate better assistance data can of course iterate across time.

Device Initialization and Population of Micro-BSA(s)

The BSA512is controlled by the network operator and has the parameters for all the cells in the network (e.g., ˜700,000 cells for the largest operators). A micro-BSA520typically includes a tiny subset of the information within the BSA512for cells in the vicinity of the UE510. For example, a micro-BSA520could include parameters for 1,000 cells that make up a metropolitan area including an urban downtown and surrounding areas. For UE-based OTDOA the micro-BSA520has parameters needed to perform the OTDOA algorithms. The OTDOA algorithms consist of, for example: (i) generating assistance data (AD) from the micro-BSA520, (ii) using assistance data (AD) to perform TOA/TDOA measurements, and using TOA/TDOA measurements plus assistance data (e.g., cell latitude/longitude) to estimate the UE's location.

The AD is a subset of the micro-BSA520. For example, it may consist of the parameters of 50 cells.
BSA→micro-BSA→AD
700,000 cells→1,000 cells→50 cells

The UE510may have one or more micro-BSAs520. For example, the UE510may have more than one micro-BSA520to provide service for a few different areas around a town that the UE510frequents. To initialize the device, the UE510communicates with a BSA server511via the micro-BSA cloud assist server516. It informs the BSA server511of the ECGI of the serving cell, and possibly the ECGI's or PCI's of neighbor cells. For example, the UE510might inform the BSA server511via the micro-BSA cloud assist server516that the serving cell ECGI is “xyz”, and that the UE510would like a micro-BSA520of 200 cells. The parameters that make up a cell is roughly 120 bits, so a 200-cell micro-BSA520would be 200*120/8/1000=3 kilobytes. These 3 kBs of BSA information are retrieved from the BSA512by the BSA server511and then communicated by the micro-BSA cloud assist server516in the downlink channel to the UE510and stored.

With the micro-BSA520instantiated on the UE510, the UE510effectively has access to a “mini-map” of those 200 cells. Since the UE510is aware of its location, the UE510knows if it remains in the service area of these 200 cells. If the UE510roams outside of these cells it might want to request a new micro-BSA520. If the UE510is stationary and not detecting as many cells as expected given the contents of the current micro-BSA520, it might be the case that the cell topology has changed and it might be a good time to get the micro-BSA520refreshed.

The ECGI of the serving cell can provide a seed estimate of the UE510from which to derive a set of micro-BSA cells. That seed estimate can simply be the transmission point of the serving cell. With additional neighbor information, the server can derive a better seed estimate, like the centroid of the serving and surrounding cells.

To determine a good set of AD cells, the seed estimate can be something similar as the seed estimate used to get the micro-BSA520from the BSA server511; that is, something akin to a cell ID. Or, suppose the UE510is roaming. The serving cell may change from one ECGI to another with hand over. At this point, the reference timing on the UE510will likely change as the UE510synchronizes to some new serving cell. This time change can be logged to adjust the current set of timing measurements for the current set of cells being monitored. The positioning assistance data calculator524on the UE510will likely want to obtain a new set of AD cells from the micro-BSA520. The seed estimate for new AD can be the most-recent UE location estimate (using OTDOA, for example). If the new serving cell is in the micro-BSA520, then no further action is needed. If not, the UE510will need to retrieve a new micro-BSA520from the BSA server511, which hosts or has access to the BSA512. If the UE510is on the edge of the serving area of the micro-BSA520being utilized for AD, the positioning assistance data calculator524and/or position estimator540may cause the UE510to retrieve, from the BSA server511, BSA information corresponding to a new micro-BSA520.

It may be desired for the BSA server511to keep track of the cells for which information is stored in the micro-BSA520of the UE510. That way the BSA server511can give the UE510information for new cells that are not duplicates of the current micro-BSA. It may be advantageous to send just differences from the prior micro-BSA when populating a new micro-BSA.

Again referring toFIGS.5and6, the improved assistance data616is made possible by the improved seed estimate in612. The feedback532also allows for improved assistance data. For example, consider the case when the serving cell is significantly farther from the UE510than other surrounding cells. This can happen, for instance, when the serving cell is transmitting at high power on top of a hill that is in a line of sight with the receiver of the UE510. The receiver of the UE510may sense this hilltop cell to be of the highest signal-to-noise-plus-interference ratio (SINR) of all its surrounding cells and use it for its serving cell. In this example, there may be other closer-by cells, possibly transmitting at lower power, or not in a line of sight. Setting the initial seed estimate to the serving cell location604may be suboptimal in this case since the serving cell is relatively far from the target receiver of the UE510. This condition could exclude cells that are indeed much closer than the serving cell.

The current state of the art will suffer from this scenario since the assistance data is derived at a location server, not adaptively on a device such as the UE510configured with the micro-BSA(s)520, which is a superset of the assistance data. In embodiments of the present system, measurements530are provided as feedback532to the positioning assistance data calculator524, offering an efficient adaptation and improved location accuracy.

In this described scenario of a far-away serving cell, the timing measurements in530may detect closer-by cells (with a delay negative relative to the serving cell timing). For example, a high quality (PAPR, or SINR, low variance, etc.) negative TOA of 1000 meters can be present in the list of detected cells. This implies that the negative TOA cell is 1000 meters closer to the UE than the serving cell. Re-seeding the assistance data calculation by incorporating this information can be beneficial. For example, the closer-by cell latitude/longitude coordinates can be used as the new assistance data seed estimate612.

Similarly, the timing advance (TA) in the receiver can be used to detect a far-away serving cell. In a cellular system, the TA is used to signal a far-away receiver to transmit early so the far-away and closer-by device uplink transmissions arrive at the base station receiver around the same time. The assistance data calculator can therefore use TA information available in the host modem1224inFIG.12to improve the assistance data generation strategy. For example, if the TA is high, implying a far-away serving cell, the number of cells in the assistance data for TOA measurements may be expanded to farther distances from the serving cell. Then, as closer-by TOAs are detected, the assistance data can be recalculated as described above.

Multiple Onboard Micro-BSAs and Tracking Use Cases

The present system advantageously allows a device such as the UE510to specialize in location services for a broader range of use cases. The current state of the art only supplies 10s of cells in the assistance data, intended primarily for the single use case of emergency services (e911). This current state of the art is not well suited for roaming use cases, for example. The present system solves this problem with the use of micro-BSA(s)520stored on the UE510. For a UE implemented as a specialized location device, more memory may be allocated on the UE510to storing the micro-BSA(s)520. This allows for roaming use cases and minimizes interaction with the micro-BSA cloud assist server516or other elements of the network514. For example, 15 bytes per cell is sufficient to represent the cell parameters in the assistance data. This representation includes parameters such as, for example, physical cell ID (PCI), cell latitude/longitude coordinates, etc. Instead of storing 24 cells as is done in the current state of the art, the UE510may store 1000 cells using 15 kilobytes of memory. Assuming a cell density of 1 cell per square kilometer, the micro-BSA(s)520may have a location service area of 1000 square kilometers, thus allowing the UE510to roam. An example use case here is the tracking of rental scooters where the devices roam around a city. The operator of these scooters may desire to track their location both indoor and outdoor and with the present system this feature can be delivered at a low cost.

Turning now toFIG.7, an illustration is provided of the geographic footprints associated with multiple micro-BSAs520stored on the UE510. For some use cases it may be desired that the UE510store multiple micro-BSAs to span a greater geographical region. As shown, information concerning cells located in a first geographic footprint710corresponding to a region of high cell density is stored within a first micro-BSA5201. Similarly, information concerning cells located in a second geographic footprint720corresponding to a region of medium cell density is stored within a second micro-BSA5202and information concerning cells located in a third geographic footprint730corresponding to a region of low cell density is stored within a third micro-BSA5203.

In this use case exemplified byFIG.7, the UE510is known to commonly travel across the first geographic footprint710, the second geographic footprint720and the third geographic footprint730. By storing a micro-BSA5201,5202,5203for each geographic footprint710,720,730, the UE510has all the needed cell information to perform location functions without interacting with the micro-BSA cloud assist server516or other elements of the network514. And this can be done using less storage than storing the superset of the geographic footprints710,720,730, which is represented by circle750. As the UE510roams throughout the geographic footprints710,720,730, the UE510intelligently derives its assistance data by making cell selections across the multiple locally-stored micro-BSAs5201,5202,5203.

A specific use case exemplified byFIG.7involves a company's tracking of tools used on different job sites. For example, a construction company may have a high-valued generator or reciprocating saw that travels from job site to job site. Assume the company has three job sites (respectively located within geographic footprints710,720,730) and a manager has misplaced a tool. In this case the manager may use a small tracker (an implementation of the UE510) attached to the tool to determine its location.

Artificial Intelligence (AI) Assisted Micro-BSA Management

For use cases where a device, such as the UE510, is traveling across large distances (e.g., while attached or associated with a container in a truck traversing interstate highways in the United States), the UE510can sense high mobility (with Doppler estimation, for example) and the cell parameter information downloaded to the device micro-BSA520can be accordingly adapted. For example, in this case it may be advantageous to provide parameter information in the micro-BSA520for cells that are on the expected route of the truck.

In use cases such as this a micro-BSA “artificial intelligence” (AI) management module550can assist in the management of the information included in the micro-BSA(s)520. For example, the micro-BSA AI management module550can implement pattern recognition algorithms capable of identifying with high likelihood that when the UE510is located on an interstate highway and traveling at a certain velocity it will best benefit from a certain set of micro-BSA cells. Similarly, when the UE510is determined to be stationary in a city center the UE510will likely benefit from a different strategy. In this latter case, the UE510may be attached to a smart meter, traffic sign, or Automatic Teller Machine (ATM) cash machine that is not intended to travel for the life of the UE510. For these application the micro-BSA download management effected by the AI management module550will be different than for the high-velocity interstate traveling use case.

This management of micro-BSA information using AI can benefit subterranean use cases. For example, if the device serving cell is underground in a metropolitan subway system, then the AI management module550may consider only providing underground cells in the micro-BSA520. Considering another use case, the AI management module550can learn from patterns in commuting. For example, a commuter line will have a finite number of transfer routes. The download of information to a micro-BSA520of a UE510being transported by the line may benefit by including cells in the most common transfer routes, and this can depend on the time of day/week. As another example, the AI management module550may be able to “learn” that devices traveling at speed on a particular highway during a particular time (e.g., on Interstate 8 at 9 am on a Tuesday 20 miles east of El Centro) have a 90% likelihood of ending up in Glendale, AZ This knowledge may then be utilized to download information to the micro-BSA520pertaining to cells more likely to be utilized by the UE510when transiting such a highway at the particular time.

In other embodiments the AI management module550within the BSA server511may be complemented by an optional AI management module552disposed within the UE510. The an optional AI management module may be configured to perform at least some of the processing otherwise performed by the AI management module550.

Bad Cell Detector

FIG.8illustrates a Bad Cell Detection process800implemented by a Bad Cell Detector544(FIG.5) for improving quality of position estimates produced by the position estimator540. As described above, the TDOA residual error vector provides insights into the quality of the position estimate and these insights may be leveraged in the process800. First, a position estimate is computed using the TDOA measurements in a first TDOA measurement vector r1(stage810). The resulting position estimate is used to construct the TDOA residual error vector e1(stage820). Cell5is detected to be, and labeled, a “Bad Cell” because of its relatively high error value (stage830). Cell5is then removed to construct a new TDOA measurement vector r2(stage840). The resulting position estimate using this measurement vector is improved (stage850). This Bad Cell Detection process800can be coupled with other criteria to determine the quality the position estimate. For example, a minimum number of cells may be required. For 2D hyperbolic TDOA positioning, measurements from at least three distinct cell sites are required. It may be advantageous to require more than three distinct cells sites for extra redundancy and added robustness in the position calculation. Moreover, the GDOP between the estimated UE location and the cells used for positioning can be calculated. If the GDOP raises above a certain threshold it may be determined that the environment is not well suited for hyperbolic TDOA. In this case the position estimate may return no result, and error result, or may fall back to another positioning method like E-CID. The threshold setting for the residual error, the minimum number of cells, and the minimum GDOP can be dynamically determined, or multiple static configurations can operate independently in parallel. For example, config A may be a strict configuration, config B may be a less strict configuration, and config C may be E-CID. At reporting time, the position estimate of config A is used if available, otherwise config B is used if available, otherwise config C is used as a fall back. Alternatively the thresholds can be set dynamically. For dense cellular environments where many cells are measured, the minimum threshold can be raised, for example. For coastal environments where cells locations are skewed in one direction (many on land, with few if any at sea) the GDOP threshold maybe start low, but gradually increase to loosen the requirements for this given environment. E-CID can incorporate power measurements per v-shift to obtain RSRP for multiple surrounding cells, and cell TOA detectability rates can be incorporated to obtain a determination of the UE angle relative to the serving cell.

The UE510may be configured to leverage feedback from the Bad Cell Detector544to improve the assistance data526. In the specific case ofFIG.8, the fact that Cell5is deemed poor by the Bad Cell Detector544is useful information that may be included in the feedback534to the positioning assistance data calculator524. In this way information relating to Cell5may be excluded from inclusion in future assistance data526.

Again considering the example ofFIG.8, “Cell5” may be deemed of low quality due to challenging multipath channel conditions where the TOA estimation is compromised. Alternatively, Cell5may be relatively out of synchronization relative to other cells in the group. In the case of the later, the Bad Cell Detector544provides an algorithmic means of dealing with networks that are not well synchronized. Specifically, by excluding a few cells in the list that are relatively out-of-sync with the other cells, improved performance is achieved. Moreover, the level of asynchronization can be a relatively static quantity to be estimated and compensated for in the position estimator540.

Another feature of the present system is to exclude cells in the assistance data526that are rarely or never detected. Attempting to detect cells that are not detectable wastes computing resources of the UE510. Therefore there are efficiency gains to be had by ignoring cells that are difficult to detect. The estimation algorithms executed by the position estimator540can monitor which cells are being detected and which cells are not being detected. This information can be included in the feedback534provided to the positioning assistance data calculator524in order to enable incremental efficiency improvements.

Good Cell Selector

In alternative embodiments a position estimation method may be performed by a Good Cell Selector (GCS)560of the position estimator540in lieu of the method performed by the Bad Cell Detector544. First, the Good Cell Selector560ranks the estimated TOA of the surrounding cells in terms of quality. The quality metric might be based on signal-to-noise-plus-interference ratio (SINR) or peak-to-average-power ratio (PAPR) of the correlator output. See, e.g., Thompson et al., “Communication System Determining Time of Arrival Using Matching Pursuit,” U.S. Pat. No. 10,749,778. Alternatively, the estimated TOA of the surrounding cells may be ranked by peak-to-average power ratio of the pseudospectrum in the multiple signal classification (MUSIC) super resolution algorithm. See, e.g., X. Li and K. Pahlavan, “Super-Resolution TOA Estimation With Diversity for Indoor Geolocation,”IEEE Transactions on Wireless Communications, vol. 3, no. 1, January 2004.

It is advantageous to use the highest quality TOA as the RSTD reference cell, and the remaining TOAs as the RSTD neighbor cells. This sets the index i(m) to the highest quality TOA and the RSTD neighbor cell indices j(m) to the remaining cells. Of the M available TDOA measurements, the objective of the Good Cell Selector (GCS) is to select a subset of P≤M good measurements. This assumes some of the measurements are poor, and this can be attributed to a variety of error sources, including transmit timing error, non-line-of-sight (NLOS) bias, and TOA estimation error. One procedure to identify good cells is to start with the first few highest quality measurements to form an initial location estimation by minimizing the cost function Q(x). For 2D hyperbolic estimation, at least two TDOA measurements are required from 3 geographically distinct cells. So for the initial estimate, at least P=2 high quality TDOA measurements are required that correspond to three geographically distinct cells.

With the initial estimate established, the (P+1)st TDOA measurement is included in r and the new Q(x) is formed and minimized. With the inclusion of the new cell, the updated position estimate is studied to determine if including the new cell is beneficial. This can be done a variety of ways. For example, the quantity c√{square root over (Q({circumflex over (x)}))}/P is a measure of the minimum residual error in meters. If this measure exceeds an established threshold with the inclusion of the new cell, the new call can be excluded from the list of used cells. Another method is to study the contour of Q(x). If a clear global minimum is identified with a small minimum region, the new cell may be deemed good. On the other hand, if a secondary local minimum is present (thus making the overall minimum less distinct), the new cell may be identified as not good.

As the Good Cell Selector560trials new candidate cells (i.e., TDOA measurements), it is advantageous to use the cells in their ranked order of quality. It may also be beneficial to select the next trial cell that improves the estimate geometry (i.e., reduces the geometric dilution of precision (GDOP)). Given the current UE estimated location, surrounding cells can be categorized in terms of circular sectors. Cells in underrepresented sectors can be prioritized for improved geometry. This circular sector method is similar to the selection of assistance data cells using the Circular Sector Assistance Data (CSAD) described with reference toFIGS.9-11. The estimated UE location is placed at the origin of the circular sectors, and, for example, six sectors are established around this origin. Suppose cells in r are from Sectors 0 through 3, but there are not cells in r from Sectors 4 and 5. In this case, it may be beneficial to introduce the next cell candidates from the underrepresented Sectors 4 and 5.

The Good Cell Selector560continues execution until a desired number of cells are included in the position calculation. This stopping criteria may be established with a threshold on P, or once a desired GDOP level is attained, or once an uncertainty region from the contours in c√{square root over (Q({circumflex over (x)}))}/P is confined to a desirable level, or by some other means. The stopping rule is not limited to these criteria, of course, and a mix of different criteria may be effective.

Micro-BSA Refinements Responsive to Measurements

In one embodiment the on-device micro-BSA520can be refined and pruned with feedback532from the radio condition measurements and position estimates performed by the module530. For example, if a particular cell is not detectable it may be beneficial to remove the cell from the micro-BSA520to free memory. Likewise, if a cell is consistently deemed “bad” in the Bad Cell Detector544, it may also be removed.

BSA Request Optimizations

Additional BSA information is not required if the position estimator540is generating reliable estimates, as stated above. Other methods for determining if new BSA information is required include:

Cell statistics tracking (power received, timing measurements, etc.). If these do not change significantly over time, the UE510can assume that the network configurations have not changed, and no BSA update is needed.

Cell scanning. The UE510can scan for all cells (PCIs, PRS IDs). If cells are detected that are not in the micro-BSA520, this can trigger an update of the micro-BSA520in which additional information is requested from the BSA512.

BSA updates responsive to UE mobility. When the UE510is detected as being highly mobile, with a Doppler estimator, for example, or with a high rate of serving cell changes, the efficiency gains may be had by pausing BSA updates until lower speeds are achieved. This depends on the number of store cells in the micro-BSA520. For example, if 1000 cells are stored and the cell density is 1 cell per squared kilometer, then high mobility is supported in the 1000 square kilometer serving area. However, if 100 cells are stored and the cell density is 10 cells per squared kilometer, high mobility could result in the information in the micro-BSA520becoming dated. If a position update is required by the application then the micro-BSA520can be updated to provide a positioning fix during the time of high mobility. However, if the application is not requesting a position update, it may be advantageous to pause updates of the content of the micro-BSA520until the UE510returns to a stationary state.

Reduced Latency

In existing positioning applications such as, for example, e911, requests are made regarding the location of the UE510. Such requests conventionally trigger the full process described in the Background section; that is, assistance data delivery from the network to the UE, measurements on the UE, transfer of measurements from the UE to the network, and, finally, location estimation and delivery to the application. The latency involved in such a conventional approach may be 10s of seconds.

The present system reduces the time latency between the application position request and position delivery. Since the UE510is location aware, the delivery can be “instantaneous”: on the order of 10s of milliseconds if the application is hosted by the micro-BSA cloud assist server516or is otherwise cloud-based in the network514. If the application is executed by the UE410, the time required to transmit the UE location latitude and longitude coordinates in the uplink may be on the order of 10s of nanoseconds. The location awareness of the UE510is possible with the present system due to the UE-based positioning method. The intelligent handling of the micro-BSA520allows for the UE510to require no interaction with the micro-BSA cloud assist server516or other elements of the network514in the event of a position request. The UE510can thus periodically update its position estimate with no interaction with the micro-BSA cloud assist server516or other elements of the network514in between position requests. This type of location awareness on the part of a mobile device between positioning requests to a network is not made possible by existing approaches. For example, it is possible that the position of a conventional UE traveling at high velocity could change substantially between the times of position requests made to a network.

When either a cloud-based application or an application executed on the UE510requests a position, the “instantaneous” position estimate is immediately available by the present system and delivered to the application. With the estimate, a time stamp may also be supplied, signaling to the application when this last position update was performed. For example, the position estimate may be updated in the background once per hour. Consider the case of a manager of a tool company wishing to find the location of a company generator. In this case a breadcrumbing application may inform the manager that the device was at job site ten minutes prior. This may be a sufficient amount of information for this use case and the device requires no additional interaction with the network. Or, the manager may wish to know the location of the device at the present time, so the tracking device (e.g., a simplified implementation of the UE510) may update the location estimate accordingly.

Geometry Optimizations in Micro-BSA and Assistance Data Selection

Attention is now directed toFIGS.9-11, to which reference will be made in describing geometry optimizations in micro-BSA and assistance data selection in accordance with the disclosure. In one embodiment the positioning assistance data calculator utilizes intelligence in the selection of micro-BSA information and assistance data526. Cellular position estimation is sensitive to the geometric dilution of precision (GDOP). Accordingly, in one implementation the assistance data526is generated using a seed estimate of the location of the UE510as described above. However, simply selecting the N nearest cells to the UE seed estimate may be a suboptimal strategy in terms of geometry. It can be advantageous to select cells that improve GDOP and the overall stability of the position calculation.

FIG.9illustrates a method900of Circular Sector Assistance Data Generation useful in lowering GDOP in accordance with the disclosure. Using the assistance data seed as the center, six circular sectors904are formed with equal central angles of 60°. The assistance data526is built starting in sector 0 and (9040) rotating counter-clockwise, successively adding 1 cell from each sector. In the example ofFIG.9, the first cell9101included in the assistance data is in Sector 0 (9040). The second cell9102is in Sector 2 (9042) even though there is a closer cell (9103) in Sector 0. The cell9102in Sector 2 is prioritized since it provides a more geometrically diverse set. After the Sector 2 cell is added, then the third cell9103is in Sector 0.

Turning toFIG.10, there is illustrated a screenshot capture of a map1000of a portion of New York City. The map1000shows a location1002of a UE, which is a challenging location from the perspective of positioning since there are no immediate cell sites on the west and south side of the UE. In this case a suboptimal method would be to simply add all the information associated with cells closest to the location1002to the assistance data seed. However, since these closest cells are only to the north and east of the location1002, this approach does not provide geometric diversity. As a result the GDOP associated with this approach is poor and the resulting position estimate error1004is approximately 280 meters.

FIG.11illustrates screenshot capture of a map1100which corresponds to the same screenshot capture illustrated inFIG.10. However, in this case the circular sector assistance data generation method of the present disclosure is used to select the cells for which assistance data will be used in determining the location1102of the UE. The number of cells in the assistance data is the same as in the example ofFIG.10, but the GDOP is lowered since the cell set is more geometrically diverse and includes cells1110relatively distant from the location1102of the UE. The resulting position estimate error1114is reduced from 280 meters to approximate 3 meters.

Additional Network Congestion Relief

The current state of the art assistance data method of LPP/SUPL can add unneeded congestion to the network. For example, in 3GPP Rel-14 the number of muting bits per cell can be as many as 1024. The exchange of 1024 bits per cell may not be required since the number of unique bit sequences will be less than 2{circumflex over ( )}1024. The unique sequences can be stored on the device and a fewer number of bits can be transmitted over the network. In another Rel-13 example using 16-bit muting sequences, there may be only 70 unique sequences. These 70 sequences can be stored in a 70*16=1120 bit lookup table on the device, and 2{circumflex over ( )}(ceil(log 2(70)))=7 bits per cell is required to be accessed from the cloud server. This saves 9 bits (56%) in the muting bit sequence download.

Another example is the 3GPP ECGI described in the LPP consisting of MCC (mobile country code), MNC (mobile network code) and a 28-bit cell identity. The MCC and MNC require 24 bits, but those can be common to a network operator. So instead of using the full 52 bits for the 3GPP ECGI, 28 bits for the cell identity can be used for a particular deployment, a 46% reduction.

Data compression algorithms like Lemple-Ziv may also be used to reduce network congestion. The entire micro-BSA bit sequence can be concatenated and bit patterns will be compressed using the compression algorithm since the entropy of the sequence will likely be less than 1.

Exemplary UE Implementation

Attention is now directed toFIG.12, which includes a block diagram representation of a particular implementation of the UE1200, in this case a mobile or cellular phone, configured in accordance with the disclosure. It will be apparent that certain details and features of the UE1200have been omitted for clarity, however, in various implementations, various additional features of a mobile device as are known will be included. In addition, those skilled in the art will appreciate that the UE1200need not be implemented as a personal communications device, such as a mobile or cellular phone, and in other implementations may comprise a tracking device or the like lacking certain features and characteristics of the implementation ofFIG.12.

Referring toFIG.12, the UE1200includes a processor1220operatively coupled to a touch-sensitive display1204configured to present a user interface1208. In other embodiments the user interface1208may include a physical keypad or keyboard, audio input device and/or any other device capable of receiving user input or instructions. The UE1200includes a memory1240comprised of one or more of, for example, random access memory (RAM), read-only memory (ROM), flash memory and/or any other media enabling the processor1220to store and retrieve data. As shown, the memory1240stores the micro-BSA(s)520and programs or including instructions executable by the processor1220. These modules include the positioning assistance data calculator524, the power and timing measurements module530, the position estimator540and the micro-BSA AI management module552.

The UE1200includes a wireless transceiver and modem1224for communication with a network, such as the network514, which may include, for example, the Internet, and/or a wireless network such as a cellular network and/or other wired or wireless networks. The UE1200may also include a camera228and other ancillary modules.

Uncertainty Calculation for UE-Based Positioning

A common approach to estimate the quality of a location estimate is to study many position estimates and form a confidence ellipse. See, e.g., Chew, V. “Confidence, Prediction, and Tolerance Regions for the Multivariate Normal Distribution,” Journal of the American Statistical Association. 61, 315 (September 1966) 605-617; and Owens, T., and McConville, D. “Geospatial Application: Estimating the Spatial Accuracy of Coordinates Collected Using the Global Positioning System,” Tech. rep., National Biological Service, Environmental Management Technical Center, Onalaska, Wisconsin (April 1996). For example,FIG.16shows an example in which multiple (i.e., 50) position estimates (observations1610) are used to form a confidence ellipse1602.

Turning now toFIG.17, there is illustrated an exemplary contour of error surface1700generated to establish a confidence region in accordance with an embodiment. In this method the contours of c√{square root over (Q({circumflex over (x)}))}/P are evaluated as shown inFIG.17. The darkest blue region1704tightly surrounds the true location1710of the UE and the estimated location1720of the UE. The uncertainty in the estimated location of the UE can therefore be determined by finding the area of the darkest blue region.

The onboard micro-BSA positioning method described herein allows for the confidence region (location estimation uncertainty) to be computed on the UE510, and this has technical advantages over the state of the art UE-assisted location estimation. For example, performing multiple estimates to form a confidence ellipse can be done using the presently disclosed method without interacting with the network514, with the UE510in a receive-only mode. This method can be performed while the UE510is technically in RRC idle, eDRX, or PSM from a data communication aspect. Likewise, the contour of error surface method can be performed on the UE510with a single set of TDOA measurements, which in contrast to the method of forming a confidence ellipse advantageously does not require multiple observations.

Hybrid Power-Timing-Based Positioning

In the TDOA positioning method discussed above, timing measurements are used to estimate the location of the UE. As has been explained herein, TDOA is a hyperbolic location method, with each cell pair providing a hyperbola on a two dimensional map. The intersection between multiple hyperbolas is the estimated location of the device. When the number of detected cells is low, a limited number of hyperbolas can increase the uncertainty in the estimate. If only two cells are detected, then only a single hyperbola is draw, greatly increasing the uncertainty in the UE estimated location.

To reduce the uncertainty of location estimates obtained using timing measurements alone, a hybrid power-time positioning method is described in this section. It may be appreciated that a power-measurement-to-distance relationship is typically less clear than a time-measurement-to-distance relationship. As a consequence, a power-only positioning method will typically underperform a timing-only measurement positioning method—so long as there are a sufficient number of detected cells. However, when only two cells are detected, a power-only method can outperform a time-only method, and a hybrid power-time method can perform best.

Suppose the UE performs power measurements on Mpsurrounding transmitters. The received signal power measurement for the mth cell can be modeled as follows:
rp,m=PTX,m+Am(θm)−PLavg,m(∥x−xm∥))+Xm,

dBm, where m=1, 2, . . . , Mp, PTX,mis the mth transmitter power in dBm, Am(θm) is the mth transmitter attenuation factor in dB, PLavg,m(∥x−xm∥) is the average path loss in dB experienced by the mth transmitted signal traveling ∥x−xm∥ meters, x is the unknown UE location, xmis the known location of the mth transmitter, and xmis a zero-mean Gaussian random variable with standard deviation σp,m.

A common path loss model is the so-called log-distance model, where

PLavg,m(x-xm)=PLavg,close(d0)+10⁢n⁢log10(x-xmd0),

See, e.g., Rappaport, T. S. Wireless Communications: Principles and Practices. Prentice Hall, 2002).

PLavg,close(d0) is the close-in path loss in dB at some close-in distance d0meters, and n is the path loss exponent. The term xm˜N(0, σp,m) characterizes large-scale shadow fading between the transmitter and receiver. The parameter set {PLavg,close, d0, n, σp,m} fully characterizes the model, and they can be fit to empirically obtained data set.

FIG.18illustrates an attenuation profile associated with a commonly used attenuation model. See 3GPP TR 36.814: Table A.2.1.1-2 “Further advancements for E-UTRA physical layer aspects”. This model may be represented as:

Am(θm)=-1×min(12⁢(θmB3⁢dB)2,Amax),
where θmis the angle between the direction of interest (azimuth angle) and the boresight of the antenna, B3dBis the 3 dB beamwidth of the antenna aperture, and Amaxis the maximum attenuation in decibels.FIG.18sets B3dB=70 degrees and Amax=25 dB. Now, defining
rp,m=hp,m(x)+np,m,
wherehp,m(x)=PTX,m+Am(θm)−PLavg,m(∥x−xm∥) is the average received power, andnp,m=xmis the measured power error term due to shadow fading. This formulation allows for the vector form
rp=hp(x)+np
where the mth element is rp,m. This vector form allows for the direct application of the least-squares estimator described above in the timing-based context. A power-based least-squares estimator is therefore:

x^LS,power=arg⁢minx(rp-hp(x))T⁢(rp-hp(x)),

Next, to incorporate timing measurements for a power-timing hybrid method, the subscript “t” is introduced where the mth TDOA measurement is
rt,m=ht,m(x)+nt,m,
where ht,m(x) is the ground-truth TDOA component and nt,mis the TDOA noise component. The vector form is
rt=ht(x)+nt.

Since the power measurements and the TDOA measurements have different units (dBm for the power measurements and seconds for the timing measurements), exactly how to combine the measurements is unclear and not suggested by prior positioning approaches. In order to circumvent this issue a maximum likelihood method, such as a Gaussian maximum likelihood method, is pursued.

Specifically, modeling the shadow fading term xmas N(0, σp,m2) makes the received power measurement term a Gaussian random variable with variance σp,m2, mean hp,m(x), and a conditional probability density function

p⁡(rp,m❘x)=1σp,m⁢2⁢π⁢exp(-(rp,m-hp,m(x))22⁢σp,m2),m=1,2,…,Mp.

Similarly, modeling nt,mas N(0, σt,m2) makes rt,ma Gaussian random variable with variance σt,m2and mean ht,m(x) with conditional probability density function

p⁡(rt,m❘x)=1σt,m⁢2⁢π⁢exp(-(rt,m-ht,m(x))22⁢σt,m2),m=1,2,…,Mt.

Assuming statistical independence across the power measurements and across the time measurements, the joint conditional probability of all the measurement is

p⁡(rp,1,rp,2,…,rp,Mp,rt,1,rt,2,…,rt.Mt❘x)=[⊓mp=1Mpp⁡(rp,mp❘x)]×[⊓mt=1Mtp⁡(rt,mt❘x)]
A power-time hybrid Gaussian maximum likelihood estimator then becomes

x^GML,hybrid=argmaxxp⁡(rp,1,rp,2,…,rp,Mp,rt,1,rt,2,…,rt,Mt❘x)=arg⁢maxx[⊓mp=1Mpp⁡(rp,mp❘x)]×[⊓mt=1Mtp⁡(rt,mt❘x)]=arg⁢minx[∑mp=1Mp(rp,mp-hp,mp(x))22⁢σp,mp2]+[∑mt=1Mt(rt,mt-ht,mt(x))22⁢σt,mt2]=arg⁢minx[(rp-hp(x))T⁢∑p(rp-hp(x))]+[(rt-ht(x))T⁢∑t(rt-ht(x))],where∑p=diag(12⁢σp,12,12⁢σp,22,…,12⁢σp,Mp2)and∑t=diag⁢(12⁢σt,12,12⁢σt,22,…,12⁢σt,Mt2)
are diagonal weighting matrices in the power and timing measurements respectively.

Although use of a Gaussian maximum likelihood (GML) estimator to combine power measurements and TDOA measurements within a position estimator may be generally preferred, other implementations of a position estimator may be suitable for use in alternative embodiments. For example, since the GML estimator discussed above assumes the TOA to be drawn from a Gaussian distribution, a generalization of this approach assumes the TOA is drawn from a Gaussian Mixture Model (GMM). As noted above, the GMM framework better accounts for the multipath nature of the cellular radio frequency (RF) environment. Alternatively, the position estimator may be implemented as an ML estimator such as the maximum a posteriori (MAP) estimator. In yet other embodiments the power and TDOA measurements may be modeled as non-Gaussian impulsive noise.

Attention is directed toFIGS.19A-19C, which depict the results of simulations demonstrating that positioning using the power-time hybrid approach disclosed herein may be advantageous in environments with a limited number of cells.FIG.19Aillustrates results of using power only to estimate the position of a UE within an environment in which the UE receives signals from two cells.FIG.19Billustrates results of using time only to estimate the position of the UE within the same two-cell environment asFIG.19A.FIG.19Cillustrates results of using the power-time hybrid approach to estimate the position of the UE within the same two-cell environment. As may be appreciated by comparing the errors associated with the estimated positions inFIGS.19A-19C, in this example with two cells the power-time hybrid approach demonstrates a benefit relative to the power-only and time-only methods.

Turning now toFIG.20, a block diagrammatic view is provided of a first embodiment of a power-time hybrid positioning system2000in accordance with the disclosure. The system2000includes a UE2010configured to generate positioning assistance data based upon a relatively small subset of a base station almanac (BSA)2012accessible to a BSA server2011within a network2014. The BSA2012may reside on a server in communication with the BSA server2011or may be included on the BSA server2011. In one embodiment information from the BSA2012is provided to the UE2010by a micro-BSA cloud assist server2016within the network2014.

As shown, the UE2010includes a processor2020operatively coupled to a memory2040and to a wireless transceiver and modem2024. The memory2040is comprised of one or more of, for example, random access memory (RAM), read-only memory (ROM), flash memory and/or any other media enabling the processor2020to store and retrieve data. As shown, the memory2040stores a micro-BSA(s)2022and programs or including instructions executable by the processor2020. These modules include a positioning assistance data calculator2026, a power and timing measurements module2030, a power-time hybrid position estimator2042and a micro-BSA AI management module2052. The power-time hybrid position estimator2042may be configured to implement the power-time hybrid positioning techniques described herein.

It will be apparent that certain details and features of the UE2010have been omitted for clarity, however, in various implementations, various additional features of a mobile device as are known will be included (e.g., a display, user interface elements, and the like). In addition, although the UE2010may be implemented as a personal communications device, such as a mobile or cellular phone, in other implementations the UE2010may comprise a tracking device or the like lacking certain features and characteristics of such personal communication devices.

The one or more micro-BSA(s)2022are configured to store information corresponding to the subset of the BSA2012received by the UE2010from the micro-BSA cloud assist server2016. The micro-BSA(s)2022may be computed with information of the UE2010serving cell ECGI to provide a rough estimate of the UE location. The positioning assistance data calculator2026is configured to receive cell parameters from the micro-BSA(s)2022for use in generating positioning assistance data. The positioning assistance data calculator2026may use the ECGI of the serving cell to derive a rough estimate of the UE location from which to calculate the positioning assistance data, which is provided to the power and timing measurements module2030and to the power-time hybrid position estimator2042.

As shown, the power-time hybrid position estimator2042may include a Bad Cell Detector2044configured to improve quality of position estimates produced by the estimator2042by identifying cells causing relatively high errors to arise in residual error vectors. The power-time hybrid position estimator2042may alternatively include a Good Cell Selector (GCS)2060in lieu of the of the Bad Cell Detector2044. As discussed above, the Good Cell Selector2060first ranks the estimated TOA of the surrounding cells in terms of quality and may select the highest quality TOA as the RSTD reference cell. The Bad Cell Detector2044or the Good Cell Selector2060may be useful in situations in which the UE2010is within range of an appreciable number of different cells. In other cases (e.g., when the UE2010is only within range of 2 or 3 cells), the Bad Cell Detector2044and the Good Cell Selector2060may not be utilized.

FIG.21provides a block diagrammatic view of a second embodiment of a power-time hybrid positioning system2100in accordance with the disclosure. As may be appreciated by comparingFIGS.20and21, the power-time hybrid positioning system2100is substantially similar to the system2000with the exception that the memory2140of the UE2110within the system2100includes a TDOA position estimator2142. In one embodiment the TDOA position estimator2142is implemented substantially similarly or identically to the position estimator540. As noted previously, embodiments of the power-time hybrid positioning system are of particular utility when a UE is within range of a relatively small number of cells. To the extent the UE2110is moved to a location that is within range of a relatively larger number of cells, it may be the case that positioning uncertainty is minimized by utilizing the TDOA position estimator2142to generate position estimates rather than the power-time hybrid position estimator2042.

Attention is now directed toFIG.22, which illustrates a third embodiment of a power-time hybrid positioning system2200in accordance with the disclosure. The system2200includes a UE2210in communication with a server arrangement. The server arrangement includes a BSA server2211within a network2214. A BSA2212may reside on a server in communication with the BSA server2011or may be included on the BSA server2011. In one embodiment information from the BSA2212is provided to the UE2210by a location assistance server2250within the network2214.

As may be appreciated with reference toFIGS.20and22, the UE2210is similar to the UE2010ofFIG.20but lacks a micro-BSA and a micro-BSA AI management module. The memory2240of the UE2210includes the positioning assistance data calculator2026, the power and timing measurements module2030and the power-time hybrid position estimator2042. In the embodiment ofFIG.22, cell parameters from the BSA2212are provided by the location server2250to the UE2210as needed or scheduled. The positioning assistance data calculator2026derives positioning assistance data from the received cell parameters in order to facilitate the timing measurements effected by the power and timing measurements module2030and the position estimates generated by the power-time hybrid position estimate2042. Although the lack of a micro-BSA within the UE2210may tend to increase network traffic and power consumption as a result of the continuing need to obtain cell parameters from the location assistance server2250, there may be cases in which either the server2250is not configured to populate a micro-BSA or the UE is not configured with such a micro-BSA. However, in these situations the UE2210may nonetheless be configured with a power-time hybrid position estimator2042capable of operating in the absence of a micro-BSA or other locally-stored repository of cell parameters.

FIG.23illustrates a fourth embodiment of a power-time hybrid positioning system2300in accordance with the disclosure. As may be appreciated by comparingFIGS.22and23, the systems2200and2300are substantially similar with the exception that the memory2340of a UE2310within the system2300further includes a TDOA position estimator2340. As noted above, the power-time hybrid position estimator2042may be of particular utility in environments in which the UE2310is within range of a relatively small number of cells. To the extent the UE2310is moved to a location that is within range of a relatively larger number of cells, it may be the case that positioning uncertainty is minimized by utilizing the TDOA position estimator2342to generate position estimates rather than the power-time hybrid position estimator2042.

Where methods described above indicate certain events occurring in certain order, the ordering of certain events may be modified. Additionally, certain of the events may be performed concurrently in a parallel process when possible, as well as performed sequentially as described above. Although various modules in the different devices are shown to be located in the processors of the device, they can also be located/stored in the memory of the device (e.g., software modules) and can be accessed and executed by the processors. Accordingly, the specification is intended to embrace all such modifications and variations of the disclosed embodiments that fall within the spirit and scope of the appended claims.

The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the claimed systems and methods. However, it will be apparent to one skilled in the art that specific details are not required in order to practice the systems and methods described herein. Thus, the foregoing descriptions of specific embodiments of the described systems and methods are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the claims to the precise forms disclosed; obviously, many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the described systems and methods and their practical applications, they thereby enable others skilled in the art to best utilize the described systems and methods and various embodiments with various modifications as are suited to the particular use contemplated. It is intended that the following claims and their equivalents define the scope of the systems and methods described herein.

The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

Examples of computer code include, but are not limited to, micro-code or micro-instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. For example, embodiments may be implemented using imperative programming languages (e.g., C, Fortran, etc.), functional programming languages (Haskell, Erlang, etc.), logical programming languages (e.g., Prolog), object-oriented programming languages (e.g., Java, C++, etc.) or other suitable programming languages and/or development tools. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code.

In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded into one or more different computers or other processors to implement various aspects of the present invention as discussed above.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.