Patent Description:
All references to alan/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise.

Airborne radio-controlled drones are becoming more and more common. In the past, drones were limited to remain within range of the radio control equipment dedicated to control the drone.

However, recent functionality facilitating remote control of drones over the cellular network has increased their range considerably. This can be achieved, for example, by attaching a long term evolution (LTE) user equipment (UE) to the drone and coupling the UE to the drone's navigation system. The drone is thus enabled to travel over multiple cells resulting in ranges that are limited only by, for example, the battery capacity of the drone.

Another type of commercially available camera drones are equipped with dedicated radio control equipment with ranges exceeding <NUM>. Such drones are relatively inexpensive and are expected to become commonly used for private flying. These drones are typically equipped with a <NUM>/<NUM> radio transceiver used for high speed transmission of real time video from the drone camera to a smart phone belonging to the drone pilot. The smartphone is attached to the radio control equipment and used for real time display of the drone camera video.

In some markets, this is already being regulated, and UEs attached to drones are registered as such. In some markets, like in Sweden, camera drones can be used without registration if the weight is below <NUM> kgs, which is the case for almost all non-professional camera drones. The pilot is supposed to follow the flight rules, but because the drone is not registered, many pilots may not know that there are flight rules which may lead to an increasing number of violations.

In addition, in other markets, a large amount of users fail to register. Such users that either fail to register, or that fly illegally because of, for example, ignorance of flight rules, are denoted as "rogue drones".

Thus, LTE and new radio (NR) capable drones need to be restricted in terms of their flight. This is particularly the case for rogue drones. To restrict their flight, it is necessary to estimate their location and movements, and also to determine if the UE is attached to a drone or not.

A first underlying reason for this need is that rogue drones create hazardous situations when flying illegally in certain parts of the airspace. Examples include airports, where commercial air traffic may be at danger, other restricted areas, and the airspace over densely populated areas where a crash is likely to cause human injuries. This is likely to be a major concern for aviation authorities and thereby for cellular operators. Recently, Gatwick International Airport, London UK, had to close down for many hours because of rogue drones. Early in <NUM>, the same situation occurred at Heathrow International Airport, London, UK. Costs amounted to millions of pounds.

A second reason for the above need is that rogue drones that transmit and receive cellular radio messages at significant altitudes tend to create more interference than ground based UEs. Because there are less obstacles when the altitude of the drone is significant, propagation can be close to free-space propagation. The interference therefore reaches further and creates interference problems also in adjacent cells. At higher altitudes, drones may also be served by the sidelobes of radio base station antennas that are downtilted. This may increase the risk of sudden signal changes.

There currently exist certain challenges. For example, there are a number of lacking items that prevent an eNB/gNB-only implementation (i.e., performed entirely in a single gNB or eNB) of a state estimation system to identify such rogue drones. Some of the items include the following.

There is not an accurate enough range measurement principle available that provides measurement of range or a related quantity from a gNBs to a drone, to be used for rogue drone state estimation in a single gNB (or eNB with reduced accuracy).

There is no drone state estimation technology that is able to fuse range only information from a single gNBs or eNB, with directional information (angle of arrival (AoA) or angle of transmission (AoT)) in two dimensions given by azimuth and elevation, thereby providing Cartesian drone state estimate information with accurately estimated altitude and altitude velocity.

There is no drone state estimation technology that handles the unique drone movement mode of hovering, at the same time as handling normal flight modes like straight line motion and maneuvering. There is no drone state estimation technology that restricts direct switching between constant velocity movement and hovering.

There is no signaling functionality in the present Third Generation Partnership Project (3GPP) NR wireless standards that facilitates distribution of rough drone state estimates and related, derived information, to the radio access network (RAN) gNB (or eNB) nodes or other core network (CN) nodes that could be used to detach or interrupt drone communication, or alert relevant bodies that could take action against the illegal activity.

<CIT> discloses a first wireless unmanned aerial vehicle (UAV)-locating signal being transmitted by a wireless network access point in a network based on a first UAV-locating mode selected from a plurality of UAV-locating modes. The wireless network access point receives a wireless signal in response to the first transmitted UAV-locating signal, the wireless signal indicative of a location of an airborne UAV, and causes the determination of the location of the airborne UAV based on the received wireless signal. The wireless network access point transmits a second wireless UAV-locating signal based on a second UAV-locating mode selected from the plurality of UAV-locating modes. The selected UAV-locating modes control an emission pattern of an antenna of the wireless network access point.

<CIT> discloses a vehicle system and method for the acquisition and transformation of data from vehicle mounted sensors oriented to monitor the environment proximate the vehicle for relevant objects. Data transformations are accomplished using polar to Cartesian debiased corrections, recursive filters, and measurement-to-track update techniques.

As described above, there currently exist certain challenges with detecting aerial vehicles, such as drones, using only a single network node, such as a gNB or eNB. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

According to one aspect of the present invention, a method for use in a network node is provided in accordance with claim <NUM>.

According to another aspect of the present invention, a network node is provided in accordance with claim <NUM>.

According to another aspect of the present invention, a non-transitory computer readable medium is provided in accordance with claim <NUM>.

According to another aspect of the present invention, a computer program product is provided in accordance with claim <NUM>.

For example, particular embodiments fuse range information with directional information to estimate the Cartesian movement state of a drone using a single network node. More specifically, particular embodiments include one or more of the following elements:
Particular embodiments include an interactive multiple modeling (IMM) filter performing drone state estimation in a single gNB (or eNB with reduced accuracy). The IMM filter fuses range information, typically obtained with a round trip time measurement, with directional information in azimuth and elevation. The directional information may be obtained, for example, by codebook based or reciprocity assisted beamforming techniques.

The IMM filter is characterized by a) a combination of movement models adapted to the hovering capabilities of drones, b) a restricted mode transition probability model reflecting the characteristics of the hovering capability, and/or c) integrated measurement fusion of range only measurements with respect to multiple eNBs/gNBs.

Some embodiments include signaling for distribution of estimated drone information and information derived therefrom, to radio access network (RAN) eNBs/gNBs, other evolved packet core (EPC) nodes than the state estimation node, or to external sources.

While particular embodiments and examples are described with respect to new radio (NR) using the accurate round trip time (RTT) measurement, other embodiments may rely on long term evolution (LTE) and a timing advance (TA) measurement.

Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments can pinpoint the location of rogue drones with an accuracy that allows countermeasures to be exercised effectively. Particular embodiments are implemented in a single eNb or gNB, without any need for coordination with other base stations. Other advantages include lower development cost, lower cost of operation and significantly easier handling.

Particular embodiments are described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein. The disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

In general, particular embodiments include one or more components that are further discussed below. One component is high accuracy range information based on a high accuracy round trip time measurement with respect to the serving base station. Some embodiments may use new radio (NR) round trip time (RTT) measurements, and some may use long term evolution (LTE) timing advance (TA) measurements. Another component is high accuracy distance information in terms of angle of arrival (AoA) or angle of transmission (AoT) derived in the gNB or eNB.

Some embodiments include improved drone movement modeling methods and drone state estimation algorithms that fuse range and directional measurements with movement model information. Some embodiments include signaling of rogue drone state information for interference mitigation and/or flight restriction.

The technical field of air vehicle state estimation is mature today, with many systems operational worldwide. An example is illustrated in <FIG>.

<FIG> is a block-diagram of a multi-sensor air vehicle state estimation system. As illustrated, measurements consisting of strobes (angle only measurements) and plots (Cartesian position measurements) are collected from the sensors attached to the air vehicle estimation system. The plots and strobes are sent for association with existing three-dimensional (3D) state estimates.

Association is the process of determining which measurements that belong to each state estimate. (Association may not apply to particular embodiments described herein because each user equipment (UE) has a unique ID when attached to the cellular system. ) The association is performed in the measurement space of each sensor, i.e. the state estimates (that typically reside in an earth tangential Cartesian coordinate system) are transformed to the measurement space of each sensor. Associated data then updates state estimates with Kalman filtering techniques discussed further below, again in the measurement space of each sensor.

Plots and strobes that are not associated may originate from new objects and they are sent to the plot handler or the strobe handler for initiation of new state estimates. Advanced state initiation is not needed for particular embodiments described herein, but the techniques may be combined with particular embodiments for further enhancement. Plots and strobes that are associated to high quality estimates are also used for computation of sensor bias parameters in the sensor registration block. The sensor registration is also not needed for particular embodiments, but the techniques may be combined with the current invention for further enhancement.

Drone state estimation may be performed using multiple mode estimation. To accurately estimate the movement state of a drone, it is first recognized that drones fly in specific ways. These modes of movement may be reflected by the estimator applied for measurement processing.

There are many methods for estimation when multiple dynamic modes describe the behavior of an object whose state is estimated. A first and more general way of performing such estimation is to use the joint probability distribution of the objects state. The propagation of the state forward in time may be governed by the Fokker-Planck partial differential equation. The measurement processing is performed by a multi-dimensional integration to obtain the posterior probability state distribution from the likelihood of the measurement and the prior probability distribution. This process is referred to as Bayesian inference.

It is however significantly more computationally complex and memory intensive than the embodiments described herein. Bayesian inference is today approximated by particle filters, in which the probability density functions are discretized in terms of individual "particles". Because particle filtering is still significantly more complex than the embodiments described herein, the details are not discussed in detail.

At the other complexity extreme, each mode may be modeled separately and also estimated separately. Ad hoc logic may be used to select the movement mode. Traditional air vehicle state estimation is designed in that way. One movement mode is assumed to be constant velocity movement, i.e. straight line movement, and the other movement mode is a maneuver mode, modeled by a leaky constant velocity movement that responds to measurements with much higher agility than the constant velocity mode. A maneuver detector is used to choose the maneuver filter in case matches measurements better. After the maneuver is terminated, a re-initialized constant velocity movement mode is used for state estimation. This approach is robust but suffers from difficulties in the selection of threshold values for the maneuver detector.

A more systematic approach to the state estimation problem at hand is offered by the interacting-multiple-model (IMM) filter. The IMM algorithm assumes that the system behaves according to one of a finite number of models which is one of several modes. These models can differ in noise levels or their structure, such as different state dimensions and unknown inputs. In the IMM approach, at time k the state estimate is computed under each possible model using r filters, with each filter using a different combination of the previous model-conditioned estimates.

<FIG> is a block diagram illustrating the IMM algorithm. The illustrated example includes one cycle, which consists of r interacting filters operating in parallel. The mixing is done at the input of the filters with the probabilities conditioned on data Zk-<NUM>. The structure of the IMM algorithm is <MAT> where Ne is the number of estimates at the start of the cycle of the algorithm and Nf is the number of filters in the algorithm.

One cycle of the algorithm consists of the following steps. A first step comprises calculation of mixing probabilities (i, j = <NUM>, ··· , r). The probability that mode Mi was in effect at time k - <NUM> given that Mj is in effect at k conditioned on Zk-<NUM> is <MAT> where the normalizing constants are given by the below equation that uses the mode transition probabilities pij which is the probability that the estimated object is in mode j at time k, conditioned on being in mode i at time k - <NUM>. The expression for the normalizing constant is <MAT>.

A next step comprises mixing (j = <NUM>, ··· r). Starting with x̂i(k - <NUM>|k - <NUM>), the mixed initial condition for the filter matched to Mj(k) is calculated as <MAT>.

The covariance corresponding to the above is <MAT> with ' denotes the transpose.

A next step is mode-matched filtering (j = <NUM>, ··· r). The estimate and the covariance obtained in the previous step are used as input to the filter matched to Mj(k) , which uses z(k) to yield x̂j(k|k) and Pj(k|k).

The likelihood function corresponding to the r filters <MAT> are computed using the mixed initial condition and the associated covariance as <MAT>.

A next step is model probability update (j = <NUM>, ··· , r). This is done as follows <MAT> where cj is given above and <MAT> is the normalization factor.

The last step is estimate and covariance combination. Combination of the model-conditioned estimates covariances is done according to the mixture equations <MAT> <MAT>.

To set up an IMM filter, three main choices are made: (a) definition of the movement mode; (b) definition of the transition probabilities; and (c) selection of the initial conditions of the filters.

For each movement mode, this amounts to the definition of a state space model, i.e., one vector difference equation that defines the dynamics, and another static vector equation that defines the measurement relation, by mapping states to the measurements. In addition, the inaccuracies of the measurement equation and the dynamic state model are given in terms of the covariance matrices of the uncertainties.

The second choice describes, in terms of a hidden Markov model, how the modes interact, this being expressed in terms of the probabilities of a mode transition of the estimated object between two discrete instances of time.

The third choice is typically made in terms of the expected initial state and covariance of each model.

Some embodiments may use an extended Kalman Filter. For each filter Mj, j = <NUM>, ··· , r a nonlinear extended Kalman filter (EKF) is used. EKF is based on linear approximations of the nonlinear system. It can be used to estimate the state of a discrete-time dynamic system described by a vector difference equation with additive white Gaussian noise that models unpredictable disturbances.

The dynamic model is <MAT> where x(k) is the nx-dimensional state vector, and v(k), k = <NUM>,<NUM>, ··· is the sequence of zero-mean white Gaussian process noise (also nx vectors) with covariance <MAT>.

The measurement equation is <MAT> with h(. ) a nonlinear function of state and w(k) the sequence of zero-mean Gaussian measurement noise with covariance <MAT>.

The matrices F, Q, R and the function h(. ) are assumed known and possibly time varying. In other words, the system can be time varying and the noises nonstationary. The Jacobian of the measurement model h(x(k), k) with respect to k is defined as <MAT>.

The initial state x(<NUM>), in general unknown, is modeled as a random variable, Gaussian distributed with known mean and covariance. The two noise sequences and the initial state are assumed mutually independent. This constitutes the linear-Gaussian (LG) assumption.

The conditional mean <MAT> where Zk = {z(j), j ≤ k} denotes the sequence of observations available at time k, is the estimate of the state if j = k and predicted value of the state if j > k. The conditional covariance matrix of x(j) given the data Zk or the covariance associated with the estimate is <MAT>.

The estimation algorithm starts with the initial estimate x̂(<NUM>|<NUM>) of x(<NUM>) and the associated initial covariance P(<NUM>|<NUM>), assumed to be available. The second (conditioning) index <NUM> stands for Z<NUM>, the initial information.

One cycle of the dynamic estimation algorithm - the extended Kalman filter (KF) -thus consists of the computations to obtain the estimate <MAT> which is the conditional mean of the state at time k (the current stage) given the observation up to and including time k, and the associated covariance matrix <MAT> An example of the EKF is illustrated in <FIG>, which is a flowchart illustrating one cycle of the state estimation of a linear system with nonlinear measurement.

Particular embodiments include range measurement. There are several possibilities for range measurement. One way is to perform path-loss measurements. Path-loss measurement, however, is not accurate enough for particular embodiments described herein because the path loss is affected by radio fading and range needs to be computed from an assumed radio propagation model.

A better and more accurate basis for measurement of range is to measure the travel time of radio waves from a base station and a UE and back, i.e. a round-trip-time measurement. Given the round trip time measurement, the range follows as <MAT> where c denotes the speed of light. The principle of the RTT measurement is illustrated in <FIG>.

<FIG> is a sequence diagram illustrating the principle of a WCDMA RTT measurement and an LTE TA measurement. The RTT value is thus obtained as <MAT> where UE RxTx is measured in the UE as <MAT> and reported back to the base station over the radio resource control (RRC) protocol.

The main contribution to any inaccuracy of the measurement originates from the two reception processes in the UE and the base station. The theoretical inaccuracy of one such measurement is, in free space propagation, inversely proportional to the measurement bandwidth, as <MAT>.

This means that if the measurement bandwidth is for example <NUM>, then the best time inaccuracy that is possible is <NUM> ns, which corresponds to a little less than <NUM>. That is a <NUM> sigma value. Because two independent measurement processes are used for RTT, a <NUM> measurement bandwidth results in a combined RTT measurement inaccuracy of about <NUM>.

Some embodiments include directional measurements. Particular embodiments may use codebook based beamforming. The beamforming concept may be understood by considering an idealized one-dimensional beamforming case. When the UE is located far away from the antenna array, it follows that the difference in travel distance from the base station to the UE, between adjacent antenna elements, is l = kλ sin(θ), where kλ is the antenna element separation. Here k is the separation factor, which may be <NUM>-<NUM> in a typical correlated antenna element arrangement. This means that a reference signal siejωt transmitted from the i:th antenna element will arrive at the UE antenna as a weighted sum <MAT> Here ω is the angular carrier frequency, hi is the complex channel from the ith antenna element, t is the time, and fc is the carrier frequency.

In the above equation, θ and hiare unknown. For a feedback solution, the UE therefore needs to search for all complex channel coefficients hi and the unknown angle θ. For this reason, the standard defines a codebook of beams in different directions given by steering vector coefficients like wmi, = e-jf(m,i), where m indicates a directional codebook entry.

The UE then tests each codebook and estimates the channel coefficients. The information rate achieved for each codebook entry m is computed and the best one defines the direction and channel coefficients. This is possible because si is known. The result is encoded and reported back to the base station. This provides the base station with a best direction (codebook entry) and information that allows it to build up a channel matrix H. This matrix represents the channel from each of the transmit antenna elements to each of the receive antenna elements. Typically, each element of H is represented by a complex number.

From the above description it follows that the channel state information feedback in itself represent a best angle of transmission, that can also in principle be used for drone state estimation.

Particular embodiments may use reciprocity based AoA estimation for directional measurements. Channel reciprocity is a consequence of Maxwell's equations. Given two nodes equipped with antenna arrays that communicate in a single frequency band, the channel reciprocity property means that at any given point in time, the complex channel coefficient between any transmitting antenna element in one node and any receiving antenna element in the other node is the same (to within a transpose) in the uplink and the downlink. The channel matrix thus essentially remains the same between the antenna arrays of the two nodes when the direction of the transmission is reversed. The two nodes may typically be a UE and a gNB (or eNB in <NUM>). Note that the time is assumed to be the same for the two directions of transmission.

To take advantage of reciprocity, the channel coefficients can be directly estimated by the base station from UE uplink transmission of known pilot signals, for example, sounding reference signals (SRSs). The estimated channel can be used to compute the combining weight matrix with a selected principle and used for downlink transmission. This works because the uplink and downlink channels are the same (to within a transpose) when reciprocity is valid.

Particular embodiments include reciprocity assisted transmission. The reciprocity assisted transmission scheme is obtained as a minimum mean squared error (MMSE) solution.

To express the requirements on the beamforming weights W, a desired situation can be expressed by the equation ĤW + H̃W = I, which is valid for an arbitrary number of users and antenna elements. In this equation, Ĥ is the estimated channel of dimension (Nrx, Ntx), where Nrx is the total number of receive antennas for all UEs and where Ntx is the number of base station antennas. H̃ is the channel estimation error, assumed to have covariance matrix Γ. To find the beam weights, an MMSE criterion is used with E{WWH} = I so that the MMSE estimate of becomes W = Ĥ(ĤĤ H + Γ)-<NUM>.

Given the beamforming solution, it is possible to compute an antenna diagram with a gain being a function of the direction(s) with respect to the transmit antenna of the gNB. The angle(s) that result in the maximum beam gain can then be selected as the sought AoA. Several other solutions for AoA estimation are also available in the literature.

Some embodiments include ground surface modeling. As will be disclosed below, models for calculation of the altitude above mean sea level of the ground are useful for particular embodiments.

A first example of such a system is a complete geographical information system (GIS) that consist of ground altitude maps covering the region of the cellular system. A second example is to use a configured ground altitude for each antenna site of the cellular system. A third example is to use a model of the ground altitude valid in the interior of each cell of the cellular system.

The models described below are defined in continuous time using differential equations. For computer implementation, they may be discretized. Given a continuous time Wiener process <MAT> it follows that the discrete time state equation after sampling with the period T is <MAT> where.

<MAT> and with the discretized process noise covariance <MAT>.

It is assumed below that all continuous time equations are discretized like this before applying the IMM filter.

<FIG> illustrates the drone state estimation problem solved by particular embodiments. As illustrated, the base station uses IMM and measurements from the drone to detect and estimate the state of the drone.

Particular embodiments include a three-mode drone movement model. The following three-mode model is adapted to the hovering movement that drones are capable of. The three modes are: (<NUM>) 3D (almost) constant velocity movement Wiener process; (<NUM>) 3D (almost) constant acceleration movement Wiener process; and (<NUM>) 3D (almost hovering) constant position Wiener process.

For the (almost) constant velocity movement model, the continuous time state space constant velocity model is described using the states <MAT> where the subscript defines the Cartesian coordinate directions. The model is <MAT> with <MAT> The process noise covariance matrix is Qc<NUM> = diag([q<NUM> q<NUM> q<NUM>]), where q<NUM>, q<NUM> and q<NUM> are the process noise variances.

For the (almost) constant acceleration movement, the continuous time state space constant acceleration model is defined using the states <MAT> where the subscript defines the Cartesian coordinate directions. The model is: <MAT> <MAT> The process noise covariance matrix is QC<NUM>.

For the (almost hovering) constant position model, the continuous time state space constant position hovering model is defined by the states <MAT> where the subscript defines the Cartesian coordinate directions. The model is: <MAT> <MAT> The process noise covariance is QC<NUM>.

To efficiently describe particular embodiments, the states described above are renumbered as follows:.

In particular embodiments, the transition probability model is restricted based on drone movement. For example, some embodiments include an IMM filtering process related to the physics of drone movement. When the drone is in constant velocity movement, it cannot stop immediately, rather it brakes. This means that the sequence of mode transitions is from mode <NUM>, over mode <NUM>, to mode <NUM>. The direct mode transmission from mode <NUM> to mode <NUM> is forbidden. This is reflected by new constraints in the mode transition probability matrix of the IMM filter, namely in <MAT> The new restrictions are selected as p<NUM> ≤ ε<NUM>, p<NUM> ≤ ε<NUM>. Here ε<NUM> and ε<NUM> are both much smaller than <NUM>.

Particular embodiments use a nonlinear range measurement model based on a round trip time (RTT) measurement. The RTT based range measurement model is <MAT> where xs(. ) denotes the serving site position and the constant c is the speed of the light. The derivative of the measurement model is defined as <MAT>.

Some embodiments use a directional measurement model. Using configured information about the location and orientation of the antenna array used for angle of arrival (AoA) or angle of transmission (AoT) measurement, the measured AoA or AoT can be readily transformed to the Cartesian Earth Tangential co-ordinate system where drone state estimation is performed. Assuming that azimuth and elevation angles φ and θ are available, they relate to the estimated states via the following <NUM>-dimensional measurement vector equation <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

Differentiation results in <MAT> <MAT> <MAT> <MAT> <MAT>.

An example of the drone IMM estimator operation and performance is illustrated in the following steps.

Process noise variances for constant velocity, acceleration and hovering models are <NUM>, <NUM> and <NUM>, respectively.

Initial conditions: <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

The result of the simulation is illustrated in <FIG> and <FIG>, which illustrate the true state trajectory, the IMM filtered trajectory, the site positions, as well as the true and estimated mode probabilities.

<FIG> is a three-dimensional graph illustrating a flight path of a drone. The solid line represents the actual drone trajectory, and the dashed line represents the estimated trajectory.

<FIG> is a time graph illustrating the probability that the IMM model is in any of the three modes at a particular time. The horizontal axis represents time in seconds and the vertical axis represents probability.

<FIG> illustrates an example wireless network, according to certain embodiments.

These components work together to provide network node and/or wireless device functionality, such as providing wireless connections in a wireless network.

Although network node <NUM> illustrated in the example wireless network of <FIG> may represent a device that includes the illustrated combination of hardware components, other embodiments may comprise network nodes with different combinations of components (e.g., the same components, different components, fewer components, or more components).

Similarly, network node <NUM> may be composed of multiple physically separate components (e.g., a NodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components.

Processing circuitry <NUM> is configured to perform any determining, calculating, or similar operations (e.g., certain obtaining operations) described herein as being provided by a network node, such as the scheduling operations described herein and with respect of <FIG>. The operations performed by processing circuitry <NUM> may include processing information obtained by processing circuitry <NUM> by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.

Interface <NUM> is used in the wired or wireless communication of signaling and/or data between network node <NUM>, network <NUM>, and/or WDs <NUM>.

As used herein, wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Unless otherwise noted, the term WD may be used interchangeably herein with user equipment (UE). Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air.

In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction. For instance, a WD may be designed to transmit information to a network on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the network.

Examples of a WD include, but are not limited to, a smart phone, a mobile phone, a cell phone, a voice over IP (VoIP) phone, a wireless local loop phone, a desktop computer, a personal digital assistant (PDA), a wireless cameras, a gaming console or device, a music storage device, a playback appliance, a wearable terminal device, a wireless endpoint, a mobile station, a tablet, a laptop, a laptop-embedded equipment (LEE), a laptop-mounted equipment (LME), a smart device, a wireless customer-premise equipment (CPE). a vehicle-mounted wireless terminal device, etc. A WD may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X) and may in this case be referred to as a D2D communication device.

As yet another specific example, in an Internet of Things (IoT) scenario, a WD may represent a machine or other device that performs monitoring and/or measurements and transmits the results of such monitoring and/or measurements to another WD and/or a network node. The WD may in this case be a machine-to-machine (M2M) device, which may in a 3GPP context be referred to as an MTC device. As one example, the WD may be a UE implementing the 3GPP narrow band internet of things (NB-IoT) standard. Examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances (e.g. refrigerators, televisions, etc.) personal wearables (e.g., watches, fitness trackers, etc.). In other scenarios, a WD may represent a vehicle or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

A WD as described above may represent the endpoint of a wireless connection, in which case the device may be referred to as a wireless terminal. Furthermore, a WD as described above may be mobile, in which case it may also be referred to as a mobile device or a mobile terminal.

In some embodiments, the wireless device may comprise a component of an aerial vehicle, such as a drone. In some embodiments, the wireless device may provide command and control for the aerial vehicle. In some embodiments, the wireless device may provide multimedia transmission from the aerial vehicle.

Radio front end circuitry <NUM> is connected to antenna <NUM> and processing circuitry <NUM> and is configured to condition signals communicated between antenna <NUM> and processing circuitry <NUM>.

In some embodiments, WD <NUM> may include regular-power radio front end circuitry and/or antenna <NUM> and low-power radio front end circuitry and/or antenna <NUM>. In some embodiments, the same radio circuitry may be configurable to operate as a low-power radio or a regular-power radio as needed over time.

The benefits provided by such functionality are not limited to processing circuitry <NUM> alone or to other components of WD <NUM>, but are enjoyed by WD <NUM>, and/or by end users and the wireless network generally.

Device readable medium <NUM> may include computer memory (e.g. RAM or ROM), mass storage media (e.g., a hard disk), removable storage media (e.g., a CD or a DVD), and/or any other volatile or non-volatile, non-transitory device readable and/or computer executable memory devices that store information, data, and/or instructions that may be used by processing circuitry <NUM>. In some embodiments, processing circuitry <NUM> and device readable medium <NUM> may be integrated.

User interface equipment <NUM> is configured to allow input of information into WD <NUM> and is connected to processing circuitry <NUM> to allow processing circuitry <NUM> to process the input information. Using one or more input and output interfaces, devices, and circuits, of user interface equipment <NUM>, WD <NUM> may communicate with end users and/or the wireless network and allow them to benefit from the functionality described herein.

Although the subject matter described herein may be implemented in any appropriate type of system using any suitable components, the embodiments disclosed herein are described in relation to a wireless network, such as the example wireless network illustrated in <FIG>. For simplicity, the wireless network of <FIG> only depicts network <NUM>, network nodes <NUM> and 160b, and WDs <NUM>, 110b, and 110c. In practice, a wireless network may further include any additional elements suitable to support communication between wireless devices or between a wireless device and another communication device, such as a landline telephone, a service provider, or any other network node or end device. Of the illustrated components, network node <NUM> and (WD <NUM> are depicted with additional detail. The wireless network may provide communication and other types of services to one or more wireless devices to facilitate the wireless devices' access to and/or use of the services provided by, or via, the wireless network.

The communication system <NUM> may itself be connected to a host computer (not shown), which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm. The host computer may be under the ownership or control of a service provider or may be operated by the service provider or on behalf of the service provider.

The communication system of <FIG> as a whole enables connectivity between one of the connected WDs <NUM> and the host computer. The host computer and the connected WDs <NUM> are configured to communicate data and/or signaling via the OTT connection, using an access network, a core network, any intermediate network and possible further infrastructure (not shown) as intermediaries.

The host computer may provide host applications which may be operable to provide a service to a remote user, such as a WD <NUM> connecting via an OTT connection terminating at the WD <NUM> and the host computer. In providing the service to the remote user, the host application may provide user data which is transmitted using the OTT connection. The "user data" may be data and information described herein as implementing the described functionality. In one embodiment, the host computer may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The host computer may be enabled to observe, monitor, control, transmit to and/or receive from the network node <NUM> and or the WD <NUM>.

One or more of the various embodiments in this disclosure improve the performance of OTT services provided to the WD <NUM> using the OTT connection.

<FIG> is a flowchart illustrating an example method <NUM> in a network node, according to certain embodiments. In particular embodiments, one or more steps of <FIG> may be performed by network node <NUM> described with respect to <FIG>.

The method begins at step <NUM> where a network node (e.g., network node <NUM>) obtains range information for a wireless device in communication with the network node. In particular embodiments, obtaining the range information for the wireless device comprises obtaining (e.g., calculating) a transmission RTT between the network node and the wireless device, for example, according to any of the embodiments and examples described above. Other embodiments may use timing advance to obtain the range information. For example, the network node may determine that a UE enabled aerial vehicle may be at a distance of <NUM> yards from the base station based on the RTT of communications (e.g., control, multimedia, etc.) with the aerial vehicle.

At step <NUM>, the network node obtains direction information for the wireless device. In particular embodiments, obtaining the direction information for the wireless device comprises one or more of determining elevation and azimuth information for the wireless device, determining one of an angle of arrival or an angle to transmission for a wireless transmission between the network node and the wireless device, determining codebook based beamforming information for the wireless device, determining reciprocity assisted beamforming information for the wireless device, and/or determining direction information according to any of the embodiments and/or examples described herein. For example, the network node may determine direction information for the UE enabled aerial vehicle based on angle between two beams transmitted between the network node and the aerial vehicle.

At step <NUM>, the network node estimates a movement of the wireless device based on the range information, the direction information, and an IMM filter. In particular embodiments, the IMM filter comprises a 3D constant velocity model, a 3D constant acceleration model, and a 3D constant position model. For example, the network node may use the range and direction information obtained in the previous steps as input to the three-mode IMM filter described above to estimate movement of an aerial vehicle.

In particular embodiments, estimating the movement of the wireless device is further based on a difference between an estimated altitude of the wireless device and an obtained ground altitude. For example, the network node may adjust an absolute altitude value based on a terrain map of the terrain near the network node.

At step <NUM>, the network node optionally determines that the wireless device comprises an aerial vehicle based on the estimated movement. For example, the network node can distinguish between a ground-based UE and an UE enabled aerial vehicle based on the estimated movement.

At step <NUM>, the network node optionally signals the estimated movement of the wireless device to another network node. In some embodiments, the network node signals an estimated position of the wireless device to another network node. For example, the network node may signal an estimated position (e.g., based on the estimated movement) to another network node, such as a core network node, that is equipped to analyze the position/movement information and determine, for example, whether the wireless device is in or approaching restricted airspace.

At step <NUM>, the network node optionally determines that the wireless device is in or near a restricted airspace. For example, the network node may include sufficient knowledge of nearby airspace to determine whether the UE enabled aerial vehicle is in or near restricted airspace.

In some embodiments, the network node determines that the wireless device is in or near a restricted airspace by receiving an indication from another network node, such as a core network node. For example, the core network node described with respect to step <NUM> may determine that the aerial vehicle is in or near a restricted airspace and send a notification to the network node.

At step <NUM>, the network node optionally disconnects the wireless device from the network node. For example, based on determining that the wireless device is in or near a restricted airspace (step <NUM>), the network node may determine to disconnect the wireless device. In some embodiments, the network node is instructed to disconnect the wireless device by another network node, such as the core network node described with respect to step <NUM> (e.g., step <NUM> is not performed).

Modifications, additions, or omissions may be made to method <NUM> of <FIG>. Additionally, one or more steps in the method of <FIG> may be performed in parallel or in any suitable order.

<FIG> illustrates an example network node, according to certain embodiments. The network node <NUM> may comprise network node <NUM> illustrated in <FIG>.

Network node <NUM> is operable to carry out the example method described with reference to <FIG> and possibly any other processes or methods disclosed herein. It is also to be understood that the method of <FIG> is not necessarily carried out solely by apparatus <NUM>. At least some operations of the method can be performed by one or more other entities, including virtual apparatuses.

Network node <NUM> may comprise processing circuitry such as processing circuitry <NUM> of <FIG>. In some implementations, the processing circuitry may be used to cause obtaining module <NUM>, estimating module <NUM>, transmitting module <NUM>, and any other suitable units of network node <NUM> to perform corresponding functions according one or more embodiments of the present disclosure.

As illustrated in <FIG>, network node <NUM> includes obtaining module <NUM>, estimating module <NUM>, and may include transmitting module <NUM>. In certain embodiments, obtaining module <NUM> may obtain range and direction information according to any of the embodiments and examples described herein. Estimating module <NUM> may estimate movement of a wireless device according to any of the embodiments and examples described herein. Transmitting module <NUM> may signal information about the movement of a wireless device to another network node, according to any of the embodiments and examples described herein.

In some embodiments, some or all of the functions described herein, such as the method of <FIG>, may be implemented as virtual components executed by one or more virtual machines implemented in one or more virtual environments <NUM> hosted by one or more of hardware nodes <NUM>.

NFV may be used to consolidate many network equipment types onto industry standard high-volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

Claim 1:
A method (<NUM>) for use in a network node, the method comprising:
obtaining (<NUM>) range information for a wireless device in communication with the network node;
obtaining (<NUM>) direction information for the wireless device;
estimating (<NUM>) a movement of the wireless device based on the range information, the direction information, and an interactive multiple modeling, IMM, filter, wherein the IMM filter comprises a three-dimensional, 3D, constant velocity model, a 3D constant acceleration model, and a 3D constant position model; and
determining (<NUM>) the wireless device comprises an aerial vehicle based on the estimated movement.