Patent Publication Number: US-2010117897-A1

Title: Method for position determination with measurement stitching

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a Divisional of U.S. patent application Ser. No. 11/682,830, filed on Mar. 6, 2007, which claims priority to U.S. provisional patent application Ser. No. 60/779,935 entitled, “Measurement Stitching for Improved Position Location in Wireless Communication System,” filed on Mar. 6, 2006, which is assigned to the assignee hereof, and which is incorporated herein by reference. 
    
    
     FIELD 
     This disclosure relates to positioning systems and, more particularly, to the computation of position solutions for mobile receivers. 
     BACKGROUND 
     The Global Positioning System (GPS) is a satellite navigation system, or satellite positioning system, designed to provide position, velocity and time information almost anywhere in the world. GPS was developed by the Unites States Department of Defense, and currently includes a constellation of twenty-four operational satellites. Other types of satellite navigation systems include the Wide Area Augmentation System (WAAS), the Global Navigation Satellite System (GLONASS) deployed by the Russian Federation, and the Galileo system planned by the European Union. As used herein, “satellite positioning system” (SPS) will be understood to refer to GPS, Galileo, GLONASS, NAVSTAR, GNSS, a system that uses satellites from a combination of these systems, pseudolite systems, or any SPS developed in the future. 
     A variety of receivers have been designed to decode the signals transmitted from the satellites to determine position, velocity or time. In general, to decipher the signals and compute a final position, the receiver must acquire signals from the satellites in view, measure and track the received signals, and recover navigational data from the signals. By accurately measuring the distance from three different satellites, the receiver triangulates its position, i.e., solves for a latitude, longitude and altitude. In particular, the receiver measures distance by measuring the time required for each signal to travel from the respective satellite to the receiver. This requires precise time information. For this reason, measurements from a fourth satellite are typically required to help resolve common time measurement errors, e.g., errors created by the inaccuracies of timing circuits within the receiver. 
     In certain locations, e.g., urban environments with tall buildings, the receiver may only be able to acquire signals from three or less satellites. In these situations, the receiver will be unable to resolve all four variables of the position solution: latitude, longitude, altitude, and time. If the receiver is able to acquire signals from three satellites, for example, the receiver may forego an altitude calculation to resolve latitude, longitude and time. Alternately, if altitude is obtained via external means, all four variables may be resolved from three satellite signals. If less than three signals are available, the receiver may be unable to calculate its position. 
     To address this limitation, many receivers employ hybrid location technology that makes use of signals from base stations of a wireless communication system. As with satellite signals, the hybrid receivers measure time delays of the wireless signals to measure distances from the base stations of the network. The hybrid receivers utilize the signals from the base stations, as well as any acquired signals from GPS satellites, to resolve the position and time variables. The hybrid location technique often allows a receiver to compute a position solution in a wide variety of locations where conventional positioning techniques would fail. In code division multiple access (CDMA) mobile wireless systems, for example, this base station measurement portion of this hybrid technique is referred to as Advanced Forward Link Trilateration (AFLT). 
     The accuracy of the location solution determined by the receiver is affected by the degree of time precision within the system. In synchronized systems, such as existing CDMA systems, the timing information communicated by the cellular base stations is synchronized with the timing information from the GPS satellites, allowing precise time to be available throughout the system. In some systems, such as the Global System for Mobile Communications (GSM), the timing information is not synchronized between the base stations and the GPS satellites. In these systems, Location Measurement Units (LMUs) are added to the existing infrastructure to provide precise timing information for the wireless network. 
     Another technique that is commonly used in position determining systems and algorithms is the use of Kalman filters. As is well known, a Kalman filter (KF) is an optimal recursive data estimation algorithm. It is frequently used to model attributes of moving entities such as aircraft, people, vehicles etc. These attributes can include both velocity and position, for example. The current state of the system and a current measurement are used to estimate a new state of the system. In practice, a Kalman filter combines all available measurement data, plus prior knowledge about the system, measuring devices, and error statistics to produce an estimate of the desired variables in such a manner that the error is minimized statistically. 
     In the past, a Kalman filter used within a mobile telecommunications device typically required certain initialization parameters from an accompanying position system receiver. For example, when a GPS receiver was used, it was typical that simultaneous measurements from at least three different satellite vehicles were obtained before the Kalman filter could be initialized. This means that in one measurement epoch, signals from at least three different satellite vehicles are received and successfully processed by the mobile communications device. This requirement degrades performance of the mobile device because it may take on the order of tens of seconds to acquire signals from three satellite vehicles, especially in urban environments. If the necessary signals are not acquired or are not acquired in a timely manner, then the position determining portion of the mobile device may fail to initialize and may not operate properly or efficiently. 
     Thus, the typical initialization of a Kalman filter used for position determination of a mobile unit requires that the complete initial state at some time t 0  be obtained first before updated position state information can be estimated for times t&gt;t 0 . This restriction implies that for mobile GPS receivers in marginal signal environments, for example, with time varying obstructions to the line of sight to the satellites, it may difficult or time consuming to acquire simultaneous (i.e., within the same epoch) range measurements from at least 3 GPS satellites needed for Kalman filter initialization. It is highly desirable to improve position determination performance for mobile GPS receivers in harsh signal environments where simultaneity of range measurements may not occur in a timely fashion. 
     Accordingly, a need remains to improve the position determining capabilities of mobile communications devices and to do so in a timely and efficient way. 
     SUMMARY 
     One aspect of the present invention relates to a method for estimating the position of a mobile communications device, comprising: seeding a positioning filter with an approximate position; updating the positioning filter with a first measurement set acquired during a first measurement epoch from a first subset of reference stations, wherein said first subset includes less than three different reference stations; updating the positioning filter with a second measurement set acquired during a second measurement epoch from a second subset of reference stations; and determining a position estimate for the mobile communications device based on the updated positioning filter. 
     Another aspect of the present invention relates to a method for estimating the position of a mobile communications device, comprising: seeding a positioning filter with an approximate position; updating the positioning filter with a first measurement set acquired during a first measurement epoch from a first subset of pseudoranging sources, wherein the first subset includes less than three different pseudoranging sources; updating the positioning filter with a second measurement set acquired during a second measurement epoch from a second subset of pseudoranging sources; and determining a position estimate for the mobile communications device based on the updated positioning filter. 
     Another aspect of the present invention relates to a method for estimating the position of a mobile communications device, comprising: storing a set of pseudoranging measurements from a set of reference stations, timestamped with the local clock time; later establishing a relationship between local clock time and satellite vehicle system time; determining the satellite vehicle system time of the stored pseudoranging measurement set; and using the store pseudoranging measurement set, and the satellite vehicle system time of that measurement set to determine the position of the mobile device. 
     Another aspect of the present invention relates to a method for estimating the position of a mobile communications device, comprising:storing a set of pseudoranging measurements from a set of reference stations; later determining the ephemeris information for the reference stations; and using the stored pseudoranging measurement set, and the newly determined ephemeris information to determine the position of the mobile device. 
     Another aspect of the present invention relates to a method for estimating the position of a mobile communications device which includes the steps of seeding a positioning filter with an approximate position, updating the positioning filter with a first pseudoranging measurement acquired during a first measurement epoch from a first subset of reference stations, wherein said first subset includes less than three different reference stations; updating the positioning filter with a second pseudoranging measurement acquired during a second measurement epoch from a second subset of reference stations; determining a position estimate for the mobile communications device based on the updated positioning filter; and using back propagation, determine time for the first subset and the second subset. 
     Yet another aspect of the present invention relates to a mobile communications device comprising a first receiver configured to receive signals related to a satellite positioning system; a second receiver configured to receive signals related to a communications network; a processor in communications with the first and second receiver, the processor configured to: a) seed a positioning filter with a first pseudoranging measurement acquired during a first measurement epoch from a first subset of reference stations of the satellite positioning system, wherein said first subset includes less than three different reference stations; b) update the positioning filter with a second pseudoranging measurement acquired during a second measurement epoch from a second subset of reference stations of the satellite positioning system; and c) determine a position estimate for the mobile communications device based on the updated positioning filter. 
     It is understood that other embodiments will become readily apparent to those skilled in the art from the following detailed description, wherein it is shown and described various embodiments by way of illustration. The drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a general conceptual view of a mobile device that communicates with a cellular telephone network and a satellite-based positioning system. 
         FIG. 2  illustrates portions of a mobile communications device in accordance with the principles of the present invention. 
         FIG. 3  depicts timeline of measurements received from various vehicles of a satellite position system. 
         FIG. 4  depicts a flowchart of an exemplary method for determining a position of a mobile unit in accordance with the principles of the present invention. 
         FIG. 5  summarizes the performance improvement using Monte Carlo simulation aggregated across multiple sites. 
         FIG. 6  shows further elaboration of the improved Kalman filter stitch method. 
         FIG. 7  shows a hypothetical example where the session has a timeout of 16 seconds. 
         FIG. 8  illustrates a hypothetical situation where after only 2 satellites have been acquired, an improved seed position can be obtained prior to having 3 different satellite measurements. 
         FIG. 9  shows a hypothetical case where GPS time is not acquired until about 20 seconds after session start. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below in connection with the appended drawings is intended as a description of various embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. Each embodiment described in this disclosure is provided merely as an example or illustration of the present invention, and should not necessarily be construed as preferred or advantageous over other embodiments. The detailed description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the present invention. Acronyms and other descriptive terminology may be used merely for convenience and clarity and are not intended to limit the scope of the invention. In addition, for the purposes of this disclosure, the term “coupled” means “connected to” and such connection can either be direct or, where appropriate in the context, can be indirect, e.g., through intervening or intermediary devices or other means. 
     As depicted in  FIG. 1 , mobile unit  104  may take the form of any one of a variety of mobile receivers capable of receiving navigation signals (e.g., satellite navigation signals  110  or wireless communication signals  112 ) from reference stations such as satellite vehicles  106  and/or from base stations  108 , for computing a position solution. Examples include a mobile phone, a handheld navigation receiver, a receiver mounted within a vehicle, such as an airplane, automobile, truck, tank, ship, and the like. Base stations  108  may communicate with mobile unit  104  in accordance with any one of a number of wireless communication protocols. One common wireless communication protocol is code division multiple access (CDMA) in which multiple communications are simultaneously conducted over a radio-frequency (RF) spectrum. In a CDMA environment, the techniques may be viewed as a mechanism for enhanced Advanced Forward Link Trilateration (AFLT). Other examples include Global System for Mobile Communications (GSM), which uses narrowband time-division multiple access (TDMA) for communicating data, and General Packet Radio Service (GPRS). In some embodiments, mobile unit  104  may integrate both a GPS receiver and a wireless communication device for voice or data communication. Thus, although the specific example of a GPS system may be described within this document, the principles and techniques of the present invention are applicable to any satellite positioning system or terrestrial positioning system such as a wireless network. 
     Mobile unit  104  employs techniques to compute a positioning solution based on signals  110 ,  112  received from satellites  106  and base stations  108 , respectively. Mobile unit  104  acquires signals  110  from satellites  106  in view, and measures distance from each satellite by measuring the time required for each signal to travel from the respective satellite to mobile unit  104  to determine the pseudoranging measurement. Similarly, mobile unit  104  may also receive signals  112  from base stations  108  of wireless communication system  107 , and measures distances from base stations  108  based on the time required for each wireless signal to travel from the base stations to the mobile unit. Mobile unit  104  typically resolves position and time variables based on the measurements. 
       FIG. 2  depicts a block diagram of portions of a mobile communications device  104 , in accordance with the principles of the invention, that relate to position determination for the mobile unit  104 . The mobile unit  104  may include an antenna  220  configured to receive signals from a satellite navigation system or satellite positioning system and another antenna  206  configured to receive signals from a terrestrial communications network. These signals are provided to a processor  202  that includes both software and hardware components to provide signal processing functionality with respect to the signals. Of particular interest, a Kalman filter  204  is implemented as part of the mobile unit  104  to assist with the position determining functions of the mobile unit  104 . 
     As is well known in the art, a positioning filter, such as a Kalman filter  204 , receives input measurements and implements an algorithm for estimating desired variables based on the input measurements and the historical state of the system. A memory, although not shown, is often utilized to store state information, and covariance matrix values for the Kalman filter that provide a measure of error, or certainty, of the state estimates provided by the Kalman filter. 
     The mobile unit  104  may be, for example, a cellular telephone or similar mobile communications device. Accordingly, there are additional functional blocks and devices which are part of the mobile unit  104  that are not depicted in  FIG. 2 . These additional blocks and/or devices typically relate to processing signals received from the antennas  206 ,  220 ; providing a user interface, providing speech communications; providing data communications; and other similar capabilities. Many of these functional blocks and devices are not directly related to position determination and, therefore, are not included so as not to obscure the principles of the present invention. 
     As explained briefly earlier, signals are typically received from satellite vehicles by the antenna  220 . These signals are then decoded and processed into position information using well known algorithms and techniques. In the past, signals from at least three satellite vehicles were required during a single measurement epoch in order to generate a position fix, using a weighted least squares (WLS) model, that could be used to initialize the Kalman filter  204 . Once the Kalman filter is initialized, then it can continue producing position estimates based on later occurring GPS measurements.  FIG. 3  depicts this scenario in which GPS measurements  302  (from 1, 2 or 3 satellites) are received during individual measurement epochs  300  and none of the earliest measurements include simultaneous signals from three different satellite vehicles. Thus, even though signals are constantly being acquired that include position information, the Kalman filter of the past has not been able to be initialized until GPS measurements are acquired from three different satellites during a single measurement epoch (which occurs at time  306 ). 
     In contrast, embodiments of the present invention use positioning information acquired during different measurement epochs to initialize a Kalman filter. Thus, three different measurements from multiple, non-simultaneous measurement epochs are available at time  304  (much earlier than time  306 ) and the Kalman filter is able to provide a good quality fix at this earlier point instead. The previous explanation relied on the assumption that only three satellite measurement signals are needed to generate a position fix of a receiver. This assumption rests on altitude information being available from alternative sources such as the communications network or the like. Alternatively, if no altitude information is available, then the same principle applies with four satellites, instead of three. 
     Even before three satellite positioning measurements are available, embodiments of the present invention can use two measurements to significantly improve upon an initial position. For example, using the measurements from two satellites can provide a horizontal position estimate that is typically at least 30% more accurate than the initial position, often within 100-500 meters. 
       FIG. 4  depicts a flowchart of an exemplary method of using different satellite measurements to provide position information according to the principles of the present invention. In step  402 , the mobile unit starts by acquiring any position assistance information that is available from the communications network or from memory. For example, altitude within 50 meters may be available as could position within a few hundred meters if there is an assisted-GPS system present. Next, in step  404 , this information is used to seed the Kalman filter state and covariance matrix. The Kalman filter is designed to provide a prediction of position and velocity as well as correct a previous prediction so as to provide a current position and velocity. Thus, the communications network, device memory, or other sources could provide the initial position and error estimates that seed the Kalman filter. 
     Next, in step  406 , the Kalman filter state and covariance matrix are updated with any position information acquired from any satellite vehicle. For example, if the position of the mobile unit within a relatively small portion of the earth (e.g., a wireless network cell sector) is known, then the pseudoranging information from two satellites can be used to identify a relatively short straight line segment on which the mobile unit is located. As part of the inherent operation of the Kalman filter, the co-variance matrices are automatically updated to reflect a new error estimate for the predicted values. Thus, the Kalman filter provides an estimate, for example in step  408 , of the latitude and the longitude of the mobile unit along with an estimate of the error or uncertainty. The altitude of the mobile unit is provided as well by the Kalman filter. Step  409  provides a test to determine if the estimated errors meet the application requirement. If yes, proceed to step  410  and provide the estimated latitude, longitude and altitude to the application. If no, return to step  406 . One of ordinary skill will recognize that various mathematical manipulations and coordinate transformations may be performed to ensure that information loaded and updated in the state and co-variance matrices are in an appropriate format. 
       FIG. 5  summarizes the performance improvement using Monte Carlo simulation aggregated across multiple sites. The horizontal error (HE) for the 68th percentile improves from 333 m for WLS to 124 m for KF stitch. The HE for the 95th percentile improves from 942 m for WLS to 838 m for KF stitch. 
     Returning briefly to  FIG. 3 , the GPS measurements from subsequent epochs can be used to refine the estimate (via the Kalman filter) even when data from other satellites are unavailable. Thus, for example, two adjacent measurements from satellite vehicle “1” may be used by the Kalman filter even though no information from another satellite vehicle is available. Eventually, when information from additional satellites are acquired, the estimate from the Kalman filter can be updated accordingly even though such measurements are not received during the same measurement epoch. Ultimately, after enough updates, the Kalman filter will be able to predict position and velocity within an application-acceptable uncertainty level. 
     Further elaboration of the improved Kalman filter stitch method is shown in  FIG. 6 . The top diagram illustrates a conventional GPS measurement timeline scenario for a mobile receiver where the Kalman filter cannot be initialized until at least 3 simultaneous GPS measurements are available. A WLS position fix using 3 satellites is needed to start the KF estimation process which in this hypothetical example occurs nearly 30 seconds after the session start. Subsequently, the KF continues updating position fixes, even with less than 3 satellite measurements available in a given epoch. In contrast, the bottom diagram shows the invention GPS measurement timeline scenario where the Kalman filter can produce a typical-GPS-quality position solution with 3 non-simultaneous GPS measurements using the “stitching” capabilities of this invention. In this case, the KF estimation process starts about 10 seconds after the session start when at least 3 satellites have been successfully observed, albeit at different epochs. Moreover, after this successful initialization, the KF continues updating position fixes, even with less than 3 satellite measurements available in a given epoch. 
     Thus, the improved KF stitch method, illustrated above, provides the potential for greatly reducing the time to first fix for mobile GPS receivers in disadvantaged signal environments. Also, as discussed earlier, improved horizontal positioning accuracy may been attained as well. 
     Another advantage of this invention is improved solution yield in harsh signal environments. For example,  FIG. 7  shows the same hypothetical example as in  FIG. 6 , then adding the hypothetical session timeout of 16 seconds. The conventional position fix based on a WLS estimate will not achieve a valid position before the timeout fix due to its delay of nearly 30 seconds. On the other hand, the initial position fix based on the KF stitch of this invention can achieve a valid fix in less than the timeout limit. Thus, this method can yield a greater probability of successful position fix for mobile GPS receivers in difficult signal environments. 
     Another aspect of this invention is the improvement in seed position uncertainty using 2-GPS line-of-position.  FIG. 8  illustrates a hypothetical situation where after only 2 satellites have been acquired, an improved seed position can be obtained prior to having 3 different satellite measurements. This feature is based on the geometric property that in 3 dimensional positioning, having two valid pseudoranging measurements plus altitude results in a one-dimensional line of position solution. This solution has only one residual degree of freedom compared to the complete position fix, which results in a reduced linear uncertainty, and a substantially reduced area uncertainty, as compared to the seed position. 
     Another example of the benefits of this invention is that if accurate GPS time is not available at session start, one can use back propagation to exploit prior, stored measurements after accurate (sub-millisecond) GPS time is acquired. For example,  FIG. 9  shows a hypothetical case where GPS time is not acquired until about 20 seconds after session start. In other words, the first set of GPS ranging measurements may be acquired and saved, but not immediately used, due to the lack of GPS time information. Once GPS time is resolved, a relationship is established between local clock time and GPS time, and then previously saved GPS measurements may be associated with the correct GPS time and back propagation processing may be used to recover the previously stored data for improved position determination. Thus, back propagation allows the GPS receiver to exploit fully all valid GPS satellite measurements, even if GPS time is not acquired immediately, resulting in improved yield and accuracy. 
     Another example of the benefits of this invention is that if accurate satellite ephemeris data is not available at session start, one can use back propagation to exploit prior, stored measurements after accurate ephemeris is acquired. Once ephemeris data is obtained, the satellite position is known and then previously saved GPS measurements may be associated with the correct satellite ephemeris data and back propagation processing may be used to recover the previously stored data for improved position determination. Thus, back propagation allows the GPS receiver to exploit fully all valid GPS satellite measurements, even if satellite ephemeris data is not acquired immediately, resulting in improved yield and accuracy. 
     In practice, the position information of the Kalman filter is provided, in step  410 , to one or more applications that may be executing on the mobile unit. For example, location based services such as localized weather may utilize position estimates with uncertainty approaching a kilometer or more. In contrast, “911” services may mandate that certainty of position estimates approach 50 meters or less. Accordingly, both the position (and velocity) estimates may be provided to applications along with any uncertainty, or error, estimates. In this way, each application may choose whether or not the position estimate from the Kalman filter is sufficient for its requirements. 
     The techniques described herein for broadcasting different types of transmission over the air may be implemented by various means. For example, these techniques may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units at a base station used to broadcast different types of transmission may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. The processing units at a wireless device used to receive different types of transmission may also be implemented within one or more ASICs, DSPs, and so on. 
     For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit and executed by a processor. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art. 
     The previous description is provided to enable any person skilled in the art to practice the various embodiments described herein. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments. Thus, the claims are not intended to be limited to the embodiments shown herein, but is to be accorded the full scope consistent with the claim language wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. §112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”