User equipment state estimation

Abstract

A method for state estimation of a UE connected to a wireless communication network comprises obtaining (S1) measurement information relating to range-related measurements about the UE. The measurement information comprises data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. Based on the measurement information, a state estimation of the UE is performed (S2). A method for propagating the measurement information through a network and a network node performing such a method are also disclosed.

Description

Technical Field

[0001] The proposed technology generally relates to user equipment state estimation, and in particular to a method for state estimation of a user equipment, a method for assisting state estimation, a node for state estimation of a user equipment, and a node for assisting state estimation. Background Art

[0002] Nowadays, airborne radio-controlled drones are becoming more and more common. These drones have been limited to the range of the radio control equipment dedicated to controlling the drone in the past. However, the recent functionality that allows drones to be remotely controlled via cellular networks has greatly increased their range. This can be achieved, for example, by attaching a Long Term Evolution (LTE) user equipment (UE) to the drone and by coupling the UE to the drone's navigation system. In this way, the drone is able to travel over multiple cells, resulting in a range limited only by the drone's battery. In some markets, this has been regulated and the UE attached to the drone is therefore registered. However, a large number of users fail to register. Such users are called "rogue drones".

[0003] Drones with LTE (or future New Radio (NR)) capabilities need to be restricted in the ways they fly. This is especially true for rogue drones. The first reason for this is that rogue drones transmitting and receiving cellular radio messages at fairly high altitudes tend to create more interference than ground-based UEs. This is simply explained by the fact that when the altitude of a drone is quite high, there are fewer obstacles. The propagation can then approach free space propagation. Therefore, the interference reaches further away and creates interference problems in neighboring cells as well. At higher altitudes, drones may also be served by the sidelobes of radio base station antennas that are tilted downwards. This can increase the risk of sudden changes in the signal.

[0004] The second reason is that rogue drones can create dangerous situations when they fly illegally in certain parts of the airspace. Examples include airports, where commercial air traffic may be in a dangerous location, and airspace over densely populated areas where a crash would likely cause harm to people. Note that this can be a major concern for aviation authorities and, therefore, for cellular operators. Thus, there is a need for a technology that can pinpoint the location of a rogue drone with an accuracy that allows countermeasures to be effectively implemented.

[0005] The overall problem, therefore, is to mitigate the illegal flights of so-called rogue drones, or at least identify rogue drones so that countermeasures can be implemented. Summary of the invention

[0006] One purpose is to provide a state estimate of the UE.

[0007] This object and other objects are met by embodiments of the proposed technology.

[0008] According to a first aspect, there is provided a method for state estimation of a UE connected to a wireless communication network, comprising obtaining measurement information relating to range-related measurements about the UE. The measurement information comprises data of the range-related measurements, a time at which the range-related measurements were performed, an identity of the UE and data enabling determination of a measurement location. The range-related measurements consist of data defining a distance between the UE and the measurement location. State estimation of the UE is performed based on the measurement information.

[0009] According to a second aspect, there is provided a method for assisting state estimation of a UE connected to a wireless communication network, comprising performing range-related measurements on the UE. A transfer of measurement information related to the range-related measurements to a state estimation node is initiated. The measurement information comprises data of the range-related measurements, a time at which the range-related measurements are performed, an identity of the UE and data enabling determination of a measurement location. The range-related measurements consist of data defining a distance between the UE and the measurement location.

[0010] According to a third aspect, there is provided a method for assisting state estimation of a UE connected to a wireless communication network, comprising obtaining measurement information relating to a large number of range-related measurements about at least one UE. The measurement information comprises data of the range-related measurements, a time at which the range-related measurements were performed, an identity of the UE, and data enabling determination of a measurement location. The range-related measurements consist of data defining a distance between the UE and the measurement location. The measurement information relating to the large number of range-related measurements is compiled into compiled measurement information. A transfer of the compiled measurement information to a state estimation node is initiated.

[0011] According to a fourth aspect, there is provided a node configured for state estimation of a UE connected to a wireless communication network. The node is configured to obtain measurement information related to range-related measurements about the UE. The measurement information comprises data of the range-related measurements, a time at which the range-related measurements are performed, an identity of the UE and data enabling determination of a measurement location. The range-related measurements consist of data defining a distance between the UE and the measurement location. The node is further configured to perform state estimation of the UE based on the measurement information.

[0012] According to a fifth aspect, there is provided a node configured to assist in state estimation of a UE connected to a wireless communication network. The node is configured to perform range-related measurements on the UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The node is further configured to initiate a transfer of measurement information related to the range-related measurements to a state estimation node.

[0013] According to a sixth aspect, there is provided a node configured to assist in state estimation of a UE connected to a wireless communication network. The node is configured to obtain measurement information related to a large number of range-related measurements about at least one UE. The measurement information includes data of the range-related measurements, the time when the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The node is further configured to compile the measurement information related to the large number of range-related measurements into compiled measurement information. The node is further configured to initiate the transfer of the compiled measurement information to the state estimation node.

[0014] According to a seventh aspect, there is provided a wireless communication network comprising at least one node according to the fourth aspect and at least one node according to the fifth aspect.

[0015] According to an eighth aspect, there is provided a computer program comprising instructions, which, when executed by at least one processor, cause (one or more) processors to obtain measurement information related to range-related measurements about a UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The instructions, when executed by (one or more) processors, further cause (one or more) processors to perform state estimation of the UE based on the measurement information.

[0016] According to a ninth aspect, there is provided a computer program comprising instructions, which, when executed by at least one processor, cause (one or more) processors to perform range-related measurements on a UE. The instructions, when executed by (one or more) processors, further cause (one or more) processors to initiate the transfer of measurement information related to the range-related measurements to a state estimation node. The measurement information includes data of the range-related measurements, the time at which the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location.

[0017] According to a tenth aspect, there is provided a computer program comprising instructions, which, when executed by at least one processor, cause (one or more) processors to obtain measurement information related to a large number of range-related measurements about at least one UE. The measurement information includes data of the range-related measurements, the time when the range-related measurements are performed, the identity of the UE, and data that enables determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The instructions, when executed by (one or more) processors, further cause (one or more) processors to compile the measurement information related to the large number of range-related measurements into compiled measurement information. The instructions, when executed by (one or more) processors, further cause (one or more) processors to initiate transmission of the compiled measurement information to a state estimation node.

[0018] According to an eleventh aspect, there is provided a computer program product comprising a computer readable medium having stored thereon the computer program of any of the eighth to tenth aspects.

[0019] According to a twelfth aspect, there is provided a carrier of a computer program comprising any of aspects 8 to 10, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal or a computer-readable storage medium.

[0020] According to a thirteenth aspect, there is provided a signal configured to be communicated within a wireless communication network. The signal represents measurement information relating to a range-related measurement about a UE. The measurement information comprises data of the range-related measurement, a time at which the range-related measurement is performed, an identity of the UE, and data enabling determination of a measurement location. The range-related measurement consists of data defining a distance between the UE and the measurement location.

[0021] According to a fourteenth aspect, there is provided a signal configured to be transmitted in a wireless communication network. The signal represents state estimation information of a UE. The state estimation information includes a state estimation information identifier, a UE identity, a time when the state estimation information is valid, and an estimated state of the UE. The estimated state includes at least a three-dimensional position, and preferably also includes a three-dimensional velocity.

[0022] An advantage of the proposed technique is that a state estimate of the UE can be obtained in a radio base station, such as a new radio Node B (gNB).

[0023] Other advantages will be recognized upon reading the detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The embodiments together with further objects and advantages thereof may best be understood by referring to the following description taken in conjunction with the accompanying drawings, in which:

[0025] Figure 1 is an example of a typical moving object state estimation system;

[0026] Figure 2 is a block diagram of a typical example of an interacting multiple model algorithm;

[0027] Figure 3 is a diagram of a cycle of an example of state estimation of a linear system with nonlinear measurements;

[0028] Figure 4 The diagram illustrates the principle of round-trip time measurement;

[0029] Figure 5 is a schematic flow chart illustrating the steps of an embodiment of a method for state estimation of a UE connected to a wireless communication network;

[0030] Figure 6 is a schematic flow chart illustrating the steps of an embodiment of a method for assisting state estimation of a UE connected to a wireless communication network;

[0031] Figure 7 is a schematic flow chart illustrating the steps of an embodiment of another method for assisting state estimation of a UE connected to a wireless communication network;

[0032] Figure 8-13 illustrates the architecture of an embodiment of a system for UE state estimation;

[0033] Fig.14 illustrates a UE state estimation setup;

[0034] Fig.15 is a schematic block diagram illustrating an embodiment of a network node;

[0035] Fig.16 is a schematic block diagram illustrating an embodiment of a network node implemented based on hardware circuits;

[0036] Fig.17 is a schematic block diagram illustrating another embodiment of a network node based on a combination of both a processor and a hardware circuit;

[0037] Fig.18 is a schematic diagram illustrating an embodiment of a computer implementation of a network node;

[0038] Fig.19 is a schematic block diagram illustrating an embodiment of a network device;

[0039] Fig. 20 is a schematic diagram illustrating an embodiment of a network node;

[0040] Fig.21 is a schematic diagram illustrating another embodiment of a network node;

[0041] Fig. 22 is a schematic diagram illustrating yet another embodiment of a network node;

[0042] Fig.23 The simulated station locations, estimated trajectories, and true trajectories are illustrated;

[0043] Fig.24 Picture shows Fig.23 The estimated and true pattern probabilities of different models for the simulations;

[0044] Fig.25 is a schematic diagram illustrating a general example of the distribution or partitioning of functionality; and

[0045] Fig.26 is a schematic diagram illustrating an example of a wireless communication system that cooperates with one or more cloud-based network devices. DETAILED DESCRIPTION

[0046] Throughout the drawings, the same reference numerals are used for similar or corresponding elements.

[0047] In order to identify and possibly control a UE belonging to, for example, a drone, it would be convenient to have access to an estimate of the state of the potential UE. Furthermore, since a wireless communication network is available, it would also be convenient if a node of the wireless communication network is capable of doing this. A UE attached to a drone would then be distinguishable from other UEs.

[0048] In the present disclosure, an estimate of the state of a device is intended to be an estimate of a signal defining the current position of the device and an estimate of the time derivative of such position. In other words, the state is to be understood as (an estimate of) the position that the device is in, and the velocity that the device has, and preferably also its acceleration. A state is valid at a certain moment in time.

[0049] However, there is no range measurement principle available in the prior art that allows a single gNB to obtain ranges (from multiple gNBs to a UE). Furthermore, there is no signaling defined in the prior art radio specifications that allows measurement ranges (from multiple gNBs to a UE) to be aggregated in a single gNB.

[0050] Furthermore, there is no UE state estimation technique available in the prior art that is able to fuse, for example, range-only information from multiple gNBs. This typically results in poor altitude and altitude velocity estimates caused by the poor vertical geometry of normal base station antenna locations.

[0051] Furthermore, for example in the current 3rd Generation Partnership Project (3GPP) NR wireless standard, there is no signaling functionality that allows distribution of UE state estimates and related derived information to Radio Access Network (RAN) gNB nodes, other Core Network (CN) nodes, or other relevant entities.

[0052] Thus, the present technology proposes novel state estimation techniques, which mainly involve measurement techniques to obtain ranges relative to multiple base stations. Estimation techniques are applied to obtain an object state estimation system using only the range information available. The obtained state estimation can be further used by the network nodes in different aspects.

[0053] The present technology requires the use of range measurement principles that allow high accuracy and associated signaling of range measurements between multiple base stations and UEs that may be connected to drones.

[0054] The node architecture of the UE state estimation system may reside in an evolved Node B (eNB) / gNB, or be closely associated with an evolved Node B (eNB) / gNB, or in an external node.

[0055] However, to better understand the proposed technique, it may be useful to start with a brief overview of some general aspects of range measurement and state estimation of moving objects.

[0056] The technical field of mobile object state estimation is so mature today with many operating systems around the world. See, for example, any of the many available textbooks on the subject. Figure 1 An example block diagram of a so-called multi-sensor state estimation system is depicted in .

[0057] The operation of this particular system can be explained simply as follows. Measurements consisting of strobes and plots are first collected from sensors attached to the moving object estimation system. The strobes are angle-only measurements, while the plots are Cartesian position measurements. The plots and strobes are sent to an input processing unit for association with existing three-dimensional state estimates. Association is the process of determining which measurements belong to each state estimate.

[0058] The association is performed in the measurement space of each sensor, i.e., the state estimate, which typically resides in the earth tangential Cartesian coordinate system, is transformed to the measurement space of each sensor. The associated data is then used to update the state estimate in the state estimation kernel, typically using a Kalman filter technique, as discussed further below. Unassociated plots and strobes may originate from new objects, and they are sent to a plot handler or a strobe handler for initiating new state estimates. A cross is a strobe pair temporarily associated with a new object. Plots and strobes associated with high quality estimates are also used to calculate sensor bias parameters in the sensor registration block.

[0059] If this state estimation technique is implemented in a wireless communication system, the input measurement value may have only range characteristics. In other words, the distance from the sensor to the state estimation object is measured, but the direction is basically unknown.

[0060] Further, some simplifications can be achieved. In a wireless communication system, each UE has a unique ID when attached to a cellular system. This means that the above-mentioned association process is not required. In addition, advanced state initiation is not usually required in wireless communication systems, but for further enhancement, known techniques can be combined with the techniques proposed here. Finally, the above-mentioned sensor alignment is not usually required in this implementation. However, for further enhancement, known techniques can be combined with the current ideas.

[0061] In summary, the novel UE state estimation techniques disclosed in this disclosure are therefore primarily directed to measurement techniques to obtain the range of a UE relative to multiple base stations, together with estimation techniques applied to obtain a UE state estimation system using the available information.

[0062] When multiple dynamic patterns describe the behavior of an object whose state is to be estimated, there are many known methods for estimation.

[0063] The first and most common way to perform such an estimation would be to depend on the joint probability distribution of the object state. The propagation of such a state forward in time is known in the prior art to be governed by the so-called Focke-Planck partial differential equation. The measurement processing is performed by multidimensional integration to obtain the posterior probability state distribution from the possibility of the measurement and the prior probability distribution. This process is called Bayesian inference. However, it is much more computationally complex and memory intensive than the solution further disclosed below. By the way, it may be mentioned that Bayesian inference is nowadays approximated by so-called particle filters, in which the probability density function is discretized according to a single "particle". Since particle filtering is still much more complex than the disclosed solution, the details will not be discussed further here.

[0064] At another extreme of complexity, each mode can be modeled separately and also estimated separately. Special logic can then be used to select the movement mode. Traditional state estimation for aircraft, for example, is designed in this way. One movement mode is assumed to be constant velocity movement, i.e., straight line movement, while the other movement mode is a maneuver mode modeled by a leaky constant velocity movement, which responds to measurements with much higher agility than the constant velocity mode. Finally, in the case where the maneuver detector is considered to better match the measurement, the maneuver filter is selected using the maneuver detector. After the maneuver is terminated, the reinitialized constant velocity movement mode is used for state estimation. This approach is robust, but difficulties are encountered in selecting the threshold of the maneuver detector.

[0065] The discovery of interacting multiple model (IMM) filters about 30 years ago provided a more systematic approach to the state estimation problem at hand.

[0066] The IMM algorithm assumes that the system operates according to one of a finite number of models, which is one of several modes. These models may differ in terms of noise level or their structure (such as different state dimensions and unknown inputs). In the IMM method, at time k, r filters are used to calculate state estimates under each possible model, where each filter uses a different combination of the previous model conditional estimate-mixed initial conditions.

[0067] Figure 2 A loop of a typical IMM algorithm is described, which consists of r interacting filters operating in parallel. The IMM algorithm is described in more detail in Appendix A.

[0068] To set up an IMM filter, three main choices need to be made. First, the movement patterns must be defined. Second, the transition probabilities between movement patterns must be defined. Finally, the initial conditions for the filter must be chosen.

[0069] For each mobility mode, this amounts to the definition of a state space model, i.e., one vector difference equation defining the dynamics by mapping states to measurements, and another static vector equation defining the measurement relation. Furthermore, the inaccuracies of the dynamic state model and the measurement equations need to be given in terms of the uncertainty covariance matrix.

[0070] The second option describes how modes interact based on hidden Markov models, which are represented in terms of the estimated probability of an object switching between modes between two discrete time instances.

[0071] The third choice is often made based on the expected initial states and covariances of each model.

[0072] All of these choices are typically specific to the actual implementation of the 1MM, and are thus more or less unique to each new implementation.

[0073] For each filter M j , j = 1, ..., r, a nonlinear extended Kalman filter (EKF) is usually used. The EKF is based on a linear approximation of a nonlinear system. It can be used to estimate the state of a discrete-time dynamic system described by a vector difference equation with additive Gaussian white noise to model unpredictable disturbances.

[0074] Figure 3 A flow chart of a typical EKF filter is shown in , and the EKF is described in more detail in Appendix B.

[0075] It is possible to perform range measurements, i.e. distance measurements between a base station and a UE, within a wireless communication system. There are several possibilities for range measurements. One way is to perform a path loss measurement. However, this is usually not accurate enough for current applications because the path loss is affected by radio fading and because the range needs to be calculated based on an assumed radio propagation model. However, in specific applications, e.g. where radio fading is well modeled, path loss measurements may contribute to acceptable range measurements.

[0076] A better and more accurate basis for range measurement is usually to measure the travel time of radio waves to and from the base station and the UE. An alternative is then to measure the travel time of radio waves from the base station to the UE and back, i.e. a round trip time (RTT) measurement. Given a round trip time measurement, the range is as follows:

[0077]

[0078] Where c is the speed of light. The principle of RTT measurement is Figure 4 . The base station transmits a signal at time t1. The signal propagates to the UE, which receives the signal at time t2. The UE responds with an acknowledgement signal transmitted at time t3. At time t4, the acknowledgement signal is received in the base station. The UE tracks the delay UE RxTx between the reception of the first signal and the transmission of the acknowledgement signal, and typically signals this delay time to the base station in a Radio Resource Protocol (RRC) message. UE RxTx is measured in the UE as:

[0079] UE RxTx=t3-t2 (2)

[0080] On the base station side, the real RTT time can then be obtained as:

[0081] RTT=t4-t1-UE RxTx. (3)

[0082] It can be noted that the main contribution to the measurement inaccuracy comes from the two reception processes in the UE and the base station. In free space propagation, the theoretical inaccuracy of one such measurement is inversely proportional to the measurement bandwidth as follows:

[0083]

[0084] This means that if the measurement bandwidth is, for example, 30 MHz, the best possible time inaccuracy is 2.65 ns, which corresponds to slightly less than 1 meter, corresponding to a value of 10. Since two independent measurement processes are used for the RTT, a 40 MHz measurement bandwidth will result in a combined RTT measurement inaccuracy of about 1 m.

[0085] Thus, there is a possibility to give a reasonably high accuracy RTT measurement between a base station and a UE. However, this information is obtained in different base stations, i.e. separately from each other. Also, an RTT measurement from only a single base station cannot be used for state estimation, since it only includes range information and not direction information. In order to enable UE state estimation, RTT measurements must be collected by an estimator node from a large number of measurement locations.

[0086] The solution to the problem disclosed above relies on some components discussed further below. These components first include the novel availability of high-accuracy range information based on high-accuracy RTT measurements relative to multiple base stations. The RTT measurement must be passed from the base station or other measurement node to the node that performs the actual UE state estimation. The RTT measurement can be provided at different stages of the processing and can even be compiled before or during the provision of the information. Therefore, the node that performs the actual UE state estimation is included in the wireless communication network, or at least connected to it in terms of information. Preferably, such an estimation node resides in the eNB / gNB, or is closely related to the eNB / GNb.

[0087] Furthermore, the solution according to the technology proposed here preferably includes an estimation method adapted by novel mobility modeling suitable for drone estimation. In order to be able to accurately estimate the mobility state of a UE connected to a drone, it is first realized that drones fly in very specific ways, such as hovering. Thus, these mobility patterns preferably need to be reflected by an optimal estimator applied to the measurement process. Thus, the solution must provide a new UE state estimation technology that handles the unique drone mobility mode of hovering while being able to handle normal flight modes such as straight-line movement and maneuvers.

[0088] Furthermore, a novel UE state estimation algorithm is provided that fuses available multi-base station range measurements and mobility model information.

[0089] Figure 5 is a schematic flow chart illustrating steps of an embodiment of a method for state estimation of a UE connected to a wireless communication network. In step S1, measurement information relating to range-related measurements about the UE is obtained. This obtaining of the measurement information may comprise receiving the measurement information from a base station or other node performing measurements, possibly via an intermediate node. The intermediate node may, for example, compile measurement information from several base stations. The obtaining step S1 may also comprise performing the measurements by the node in which the method is implemented, for example if the node is a base station.

[0090] Thus, in one embodiment, the step S1 of obtaining measurement information comprises receiving measurement information from another node.

[0091] In one embodiment, the step S1 of obtaining measurement information comprises performing range-related measurements on the UE.

[0092] The above two embodiments can also be combined.

[0093] The measurement information includes data of a range-related measurement. This range-related measurement consists of data defining the distance between the UE and the measurement location. The measurement information further includes the time at which the range-related measurement was performed. This tells the measurement time. Furthermore, the measurement information includes the identity of the UE so that an association between the UE and the measurement can be established, distinguishing this data from data associated with other UEs.

[0094] The measurement location in the present disclosure is to be understood as a location other than the UE relative to which the range-related measurements are performed. It may also be different from the node (which may also be the UE) that performs or reports the actual measurements. Thus, for example in RTT measurements, the location is typically the source of the radio signal for measurement purposes. The measurement location may be, for example, the location of a base station, any other network node.

[0095] In addition, the measurement information includes data that can realize the determination of the measurement position. Such data that can realize the determination of the measurement position may include direct information about the measurement position (for example, in the form of Cartesian coordinates in the earth model or any other type of coordinates). The data that can realize the determination of the measurement position may alternatively include the identity of the node that performs the range-related measurement. Then, this identity of the node that performs the range-related measurement can be converted into a position. A pre-provided data storage device with a relationship between a node ID and a position stored therein can be used for this purpose. Then, the ID of the node can be used as an input to a database, and the database responds by giving the corresponding position.

[0096] In other words, in one embodiment, the data enabling determination of the measurement location comprises position data of the measurement location.

[0097] In one embodiment, the data enabling determination of the measurement location comprises an identity of a node performing the range dependent measurements.The method thus comprises the further step of retrieving the location of the node performing the range dependent measurements from a data storage device based on the identity of said node performing the range dependent measurements.

[0098] In step S2, a state estimation of the UE is performed based on the measurement information. A preferred embodiment of such state estimation is further proposed below.

[0099] Preferably, as shown in step S3, the step S1 of obtaining measurement information and the step S2 of performing state estimation are repeated for additional measurements on the UE. Preferably, the repetition S3 is performed for measurements performed by different nodes.

[0100] As mentioned above, a base station (e.g., an eNB or a gNB) is well suited for performing range measurements. Thus, in a preferred embodiment, the range-related measurements are performed by the base station. Furthermore, the entire method may be implemented in the base station. In other words, the method for state estimation of a UE is performed in a radio base station.

[0101] Of course, the method may also be performed for a large number of UEs simultaneously. In other words, in one embodiment, the step S1 of obtaining measurement information and the step S2 of performing state estimation are repeated for additional UEs. Different measurements may be distinguished by the provided identity of the UE included in the measurement information.

[0102] In one embodiment, the step S2 of performing state estimation comprises an extended Kalman filter.

[0103] Figure 6 is a schematic flow chart illustrating steps of an embodiment of a method for assisting state estimation of a UE connected to a wireless communication network. In step S11, range-related measurements are performed with respect to the UE. The measurements may be performed according to any known range measurement procedure. Examples are discussed further below.

[0104] The measurement may depend on different kinds of measurement results, such as the determination of the transmission and reception times. The measurement may also include different calculation actions, such as subtracting the Rx-Tx delay received from the UE from the determined difference between the reception and transmission times. In other words, the term measurement should be interpreted as the action of deriving data related to the range between the measurement location and the UE.

[0105] In step S12, a transfer of measurement information related to range-related measurements to the state estimation node is initiated. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location.

[0106] Furthermore, in this case, in one embodiment, the data enabling the determination of the measurement position include position data of the measurement position.

[0107] In another embodiment, the data enabling determination of the measurement location comprises the identity of the node performing the range-related measurement.

[0108] This process may be repeated as indicated in step S13.

[0109] In a preferred embodiment, the step S11 of performing range-related measurements and the step S12 of initiating measurement information transfer are repeated for additional measurements on the UE.

[0110] In one embodiment, the step S11 of performing range-related measurements and the step S12 of initiating measurement information transfer are repeated for additional UEs.

[0111] As mentioned above, the range-related measurements may be of different kinds, and so may the resulting data. The measurement unit may also process the result in different ways before passing it to the state estimator.

[0112] An alternative is that the measurement unit uses the obtained range measurements to calculate the value of the actual distance. In other words, the provided measurement data may comprise the value of the distance between the UE and the measurement location.

[0113] If the UE and the base station are well synchronized, a direct one-way propagation time measurement can be performed. Alternatively, the one-way propagation time can be calculated from other measured quantities. Using the speed of light as a conversion factor, such a one-way propagation time is indeed a measure of distance. In other words, the provided measurement data may include the value of the one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE.

[0114] However, as indicated above, the RTT measurement is the most likely measurement to be used for this purpose. The base station or other measuring node may calculate the round trip time according to equations (2) and (3). According to equation (1), this is indeed also a measure of range. In other words, the measurement data provided may include the RTT value of the radio signal going back and forth between the UE and the measurement location.

[0115] In the case where the measurement unit itself does not have or is unwilling to use computing resources for calculating the actual RTT, the terms of equation (3) can also be used to represent the measurement. The transformation of the timestamp used for downlink signal transmission, the timestamp used for uplink signal reception triggered by the downlink signal, and the value of the RTT delay in the UE can be used to calculate the appropriate RTT or range at any other node. In other words, the measurement data provided may include the timestamp used for downlink signal transmission, the timestamp used for uplink signal reception triggered by the downlink signal, and the value of the RTT delay in the UE.

[0116] Similar to the discussion above, the measurement data received by the nodes performing the actual state estimation may also be in these different formats.

[0117] In other words, in one embodiment, the obtained measurement data includes a value of the distance between the UE and the measurement location, a value of the one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE, a value of the round-trip time of a radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the round-trip time delay in the UE.

[0118] Measurement data are collected from several measurement locations and provided to a common UE state estimation process. During this collection process, the measurement data may pass through different intermediate nodes. These intermediate nodes may simply forward the information, or they may compile the measurement data into another appearance, for example to save transmission resources. Thus, the UE state estimation process may utilize measurement data provided in another form than the measurement data provided by the measurement.

[0119] Figure 7 is a schematic flow chart illustrating the steps of an embodiment of a method for assisting state estimation of a UE connected to a wireless communication network. In step S21, measurement information related to a large number of range-related measurements about at least one UE is obtained. As described above, the measurement information includes data of the range-related measurements, the time when the range-related measurements are performed, the identity of the UE and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. In step S22, the measurement information related to the large number of range-related measurements is compiled into compiled measurement information. In step S23, the transmission of the compiled measurement information to the state estimation node is initiated.

[0120] Preferably, steps S21 - S23 are repeated as indicated by step S24 .

[0121] In one embodiment, the step S21 of obtaining measurement information comprises receiving measurement information from another node.

[0122] In one embodiment, the step S21 of obtaining measurement information comprises performing range-related measurements on the UE.

[0123] In addition, similar to the discussion above, in one embodiment, the measurement information includes measurement data, which includes a value of the distance between the UE and the measurement location, a value of the one-way propagation time of the radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of the radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the RTT delay in the UE.

[0124] Likewise, similar to the discussion above, in one embodiment, the compiled measurement information includes measurement data, which includes a value of the distance between the UE and the measurement location, a value of the one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of a radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the RTT delay in the UE.

[0125] In one embodiment, the method is performed in a base station.

[0126] Several new architectural aspects of UAV state estimation functionality are possible. Figure 8 An embodiment of a wireless communication system 1 is shown, in which a UE state estimator 60 is included in a base station 20 (e.g., an eNB / gNB 32), preferably in a positioning node. A wireless device 50 (UE 10 in this embodiment) cooperates with a network node 30 (eNB / gNB 32 in this embodiment) to establish an RTT by transmitting signals 12 back and forth. The UE 10 also sends a message including UE RxTx 14 to the eNB / gNB 32. The eNB / gNB including the UE state estimator measures its own RTT and receives measurement information 62 from another eNB / gNB 32. In the illustration, in order to simplify the reading of the figure, only one other eNB / gNB 32 is illustrated, however, as will be appreciated by those skilled in the art, there are typically multiple neighboring eNB / gNB 32. As discussed above, the measurement information 62 may include range values, RTT values, etc. However, the measurement information 62 may also include only raw data, such as timestamps for transmission / reception and UE RxTx measurements of multiple neighbor eNB / gNB 32, and the state estimator 60 calculates their RTT. The measurement is then performed in the UE state estimator 60.

[0127] There may also be synchronization, time relationship and / or setup information passed between the eNB / gNB 32.

[0128] The UE state estimator now generates state estimation information indicated in the estimated state vector. This information may be useful, for example, for interference mitigation, since drones generate more interference at higher altitudes than ground-based UEs. Therefore, new state estimation information 68 may be sent to an external client 70 for further use. The external client 70 may reside inside or outside the wireless communication system 1. The provision of this new information requires a 3GPP protocol extension. The state estimation information 68 may be used, for example, to prevent UE intrusion into restricted airspaces. This measure is discussed further below.

[0129] Fig. 9 An embodiment of a wireless communication system 1 is shown, in which a UE state estimator 60 is provided externally with respect to the eNB / gNB 32. In this embodiment, the UE state estimator 60 receives measurement information 62 from all eNB / gNB 32.

[0130] Fig.10 An embodiment of a wireless communication system 1 is shown in which the UE state estimator 60 is also taken out of the eNB / gNB 32. However, one of the eNB / gNB 32 acts as a collection node for the measurement information 62 from all eNB / gNBs 32. A compiler 65 receives the measurement information 62 from all eNB / gNBs 32 and compiles the data into compiled measurement information 64, which is provided to the UE state estimator 60. For example, if the measurement information 62 from all eNB / gNBs 32 includes raw measurement data, such as timestamps for transmission / reception and UE RxTx measurements, the compiler may perform calculations of different RTTs and forward these RTTs as compiled measurement information 64.

[0131] In various embodiments, the measurement information 62 and the compiled measurement information 64 are signaled between different nodes in the wireless communication network. In one aspect of the present concept, a signal is provided that is configured to be communicated within a wireless communication network. The signal represents measurement information related to range-related measurements about a UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location.

[0132] As will be discussed further below, different nodes or portions thereof may be provided in a cloud. Fig.11 An embodiment of a wireless communication system 1 is shown, which is similar to Figure 8The wireless communication system in FIG. 1 , however, wherein the external client 70 is provided in the cloud 80 . Fig.12 An embodiment of a wireless communication system 1 is shown, which is similar to Fig. 9 , however, wherein both the UE state estimator 60 and the external client 70 are arranged in the cloud 80. Fig.13 An embodiment of a wireless communication system 1 is shown, which is similar to Fig.10 In the wireless communication system, similarly, both the UE state estimator 60 and the external client 70 are arranged in the cloud 80.

[0133] The different eNBs / gNBs performing signaling and measurements to the UE are not necessarily synchronized. Thus, measurements obtained from one eNB / gNB may be performed at one time instant, while measurements from another eNB / gNB may be associated with another time instant. However, state estimation can handle such unsynchronized measurements if the measurement data is accompanied by an indication of when the measurement was actually performed.

[0134] Thus, in one embodiment, the above mentioned repetitive steps S3, S13 and S24 are performed for the asynchronous measurement.

[0135] The state estimate information 68 will typically consist of at least one of a state estimate information identifier, a UE identity, a time when the state estimate information is valid, and an estimated state. The state will include at least a 3D position, but preferably also a 3D velocity. The information may also include ground height information, as will be discussed further below. Alternatively, the ground height information signaled in the state estimate information may have been subtracted from the state information to obtain the height above the ground. This will require an additional identifier indicating whether the ground height information is subtracted.

[0136] One aspect of the present concept includes a signal configured to be transmitted in a wireless communication network. The signal represents state estimation information of a UE. The state estimation information includes a state estimation information identifier, a UE identity, a time when the state estimation information is valid, and an estimated state of the UE. The estimated state includes at least a three-dimensional position, and preferably also includes a three-dimensional velocity.

[0137] Fig.14The UE state estimation problem is illustrated. A drone 11 including a UE 10 moves, for example, in a restricted area. The drone 11 communicates via a base station, such as an eNB / gNB 32, which provides noise measurements of the range between the respective eNB / gNB 32 and the drone. The UE state estimator 60 uses the noise measurements, preferably using IMM, to provide state estimation information 68. The UE type unit 74 can analyze the state estimation information 68 by using the knowledge of the characteristic movement of the drone 11 and determine that the UE 10 must be attached to the drone 11. Thereby, different kinds of measurements 76 can be performed.

[0138] As indicated above, the use of IMM is expected to be the preferred way to achieve the estimation. To this end, a new 3-mode drone movement model has been created. The following 3-mode model is considered to constitute a new combination of movement modes that is adapted to the hovering motion that drones can perform. The three models are a 3D constant velocity movement Wiener process, a 3D constant acceleration movement Wiener process, and a 3D constant position Wiener process. The notation "constant" takes into account small fluctuations, so that the first model actually involves almost constant velocity movement, the second model actually involves almost constant acceleration movement, and the third model actually involves almost hovering constant position movement.

[0139] The continuous-time state-space constant velocity model uses the following state description:

[0140]

[0141] where the subscripts define the Cartesian coordinate directions. The model is:

[0142]

[0143] in:

[0144]

[0145] The process noise covariance matrix is:

[0146]

[0147] where q 11 ,q 12 and q 13 is the process noise variance.

[0148] The continuous-time state-space constant acceleration model uses the following state definition:

[0149]

[0150] where the subscripts define the Cartesian coordinate directions. The model is:

[0151]

[0152] Process noise covariance matrix

[0153] The continuous-time state-space constant-position hovering model is defined by the following states:

[0154] where the subscripts define the Cartesian coordinate directions. The model is:

[0155]

[0156] The process noise covariance matrix is

[0157] Thus, in one embodiment, performing state estimation includes interacting multiple model filtering.

[0158] In a further embodiment, the interacting multiple model filtering includes three interacting models. Preferably, the interacting multiple model filtering includes a three-dimensional constant velocity moving Wiener process, a three-dimensional constant acceleration moving Wiener process and a three-dimensional constant position Wiener process.

[0159] Another aspect of the new preferred IMM filtering process disclosed herein is related to the physics of drone movement. When a drone is moving at a constant speed, it cannot stop immediately, but brakes. This means that the sequence of mode transitions is from mode 1, through mode 2 to mode 3. Direct mode transitions from mode 1 to mode 3 are prohibited. This is reflected by a new constraint in the mode transition probability matrix of the IMM filter, namely:

[0160]

[0161] The new limits were chosen to be the following:

[0162] p 13 ≤ε 13 , p 31 ≤ε 31 .(16)

[0163] Here ε 13 and ε 31 Both are much smaller than 1.

[0164] No UE state estimation technique that limits direct switching between constant speed movement and hovering behavior has been proposed in the prior art.

[0165] Thus, in one embodiment, the interactive multiple model filtering includes a reduced switching probability between a three-dimensional constant velocity moving Wiener process and a three-dimensional constant position Wiener process. Preferably, the reduced switching probability between the three-dimensional constant velocity moving Wiener process and the three-dimensional constant position Wiener process is at least two orders of magnitude lower than the switching probability involving a three-dimensional constant acceleration moving Wiener process.

[0166] In a preferred embodiment, a nonlinear range measurement model is used based on RTT measurement. The RTT-based range measurement model is:

[0167]

[0168] where x s (.) represents the station position, and the constant c is the speed of light. The derivative of the measurement model is defined as:

[0169]

[0170] In this example, it is assumed that measurements are made relative to one station when updating. In the application, multiple measurements will appear as new rows. Also note that the Jacobian given here is for hover mode. Obvious modifications occur for constant velocity and constant acceleration models.

[0171] Currently, it is believed that the range measurement most likely to be used is the RTT measurement due to accuracy and availability. However, as mentioned above, other types of measurements are also likely to be used for this purpose.

[0172] To describe the measurement and signaling principles, assume that each node is indexed by i and each node is associated with Figure 4 Similarly, the time t is measured 1，i and t 4，i , while the UE is measuring UERxTx i =t 3，i -t 2，i Currently, there is no standardized means for communicating such measures within wireless communication networks. Thus, new signaling of these quantities must be provided. Figure 8-13 As already indicated, there are several different alternatives for signaling and RTT calculation.

[0173] In multi-node RTT calculation and signaling, UE signals UE RxTx to node i i =t 3，i -t 2，i Alternatively, the time constituting the UE RxTx measurement may be signaled. Node i then calculates:

[0174] RTT i =t 4，i -t1，i -UERxTx i , (3B)

[0175] The calculated RTT is then signaled to the node hosting the UE state estimation function.The measurement time is also signaled to the node hosting the UE state estimation.UE state estimation is then performed based on the RTT(s).

[0176] In multi-node all-time signaling, UE signals UE ERxTx to node i i =t 3，i -t 2，i Alternatively, the times constituting the UE RxTx measurements may be signaled. Node i then signals all time information together with the measurement time tag to the node hosting the UE state estimation function. The node then calculates the RTT according to equation (3B), after which UE state estimation is performed based on the RTT(s).

[0177] In a single node UE RxTx signaling, the UE sends all UE RxTx i =t 3，i -t 2，i Signaling to the node hosting the UE state estimation function, which may be forwarded by node i. Alternatively, the time constituting the UE RxTx measurement may be signaled. Node i then 1，i and t 4，i This is signaled together with the measurement time tag to the node hosting the UE state estimation function. The node then calculates the RTT according to equation (3B), after which UE state estimation is performed based on the RTT(s).

[0178] As an alternative to the above-described true RTT measurement, another alternative denoted as asymmetric RTT can be discussed. In short, this amounts to a hybrid measurement where the downlink travel time to a UE is measured from one gNB, while the uplink travel time is measured relative to another gNB. This setup has the disadvantage of requiring either high accuracy synchronization or a time relation between gNBs to be maintained or obtained, respectively. Assuming the latter is in place, the result is that the measurement equation is changed to:

[0179]

[0180] The above equation is valid for measurements involving sites i and j. To this end, one embodiment involves at least 4 sites and generates at least 4 asymmetric round trip time measurements limited to these sites.

[0181] The modification of the Jacobian is straightforward. For a mixed measurement, it becomes:

[0182]

[0183] Note that in case UE delay is involved and reported in the above measurements, it can be compensated for.

[0184] The estimation of the UE state is usually done in a Cartesian coordinate system. If the system is tangential to the earth, the estimated altitude will therefore be given relative to, for example, sea level or another fixed altitude. However, in implementations where the altitude above the ground is an important feature, such as when it should be decided whether the UE is connected to a drone. Then it may be of interest to include subtracting the geographic ground altitude from the estimated altitude in order to obtain the altitude above the ground.

[0185] Thus, in one embodiment, the method comprises the further step of subtracting the geographical ground height from the estimated height to obtain the height above ground.

[0186] Thus, a model for calculating the height above mean sea level of the ground would be useful for the present thinking. A first example of such a system would be a complete Geographic Information System (GIS) consisting of a map of the ground height covering the area of ​​the cellular system. A second example would be to use the configured ground height for each antenna site of the cellular system.

[0187] A third example would be to use a ground height model valid inside each cell of a cellular system, obtained by the method of T. Wigren “Wireless hybrid positioning based on surface modeling with polygon support” (Proc. VTC 2018 Spring, Porto, Portugal, June 2018 [1]).

[0188] A note about discretization may be useful to the reader. The models discussed in the detailed description of this technique are defined in continuous time using difference equations. For computer implementation, they need to be discretized. Given a continuous-time Wiener process:

[0189] dx=Axdt+Bdv (21)

[0190] It follows that the discrete time state equation after sampling with a sampling period of T is:

[0191] x(k+1)=F i x(k)+v(k) (22)

[0192] in:

[0193]

[0194] And the discretized process noise covariance is:

[0195]

[0196] In this disclosure, it is assumed that all continuous-time equations are discretized like this before applying the IMM filter.

[0197] The state estimation discussed herein above may be performed for one or more UEs. If all UEs in a certain area were to be the subject of the state estimation, the signaling and computational load could become very high for areas hosting a large number of UEs. It may therefore be of interest to define a subset of UEs from all current UEs for which the state estimation is of interest. The predetermined set of UEs may be defined by any external party or by the node performing the actual state estimation. The selection criteria may be based, for example, on previous mobility history, the type of UE, registration attributes of the UE user, etc.

[0198] In one embodiment, the UE that is the subject of the state estimation is selected from a predetermined set of UEs.

[0199] In one embodiment, the method for state estimation of UEs comprises the further step of defining a predetermined set of UEs.

[0200] As briefly mentioned above, novel signaling of UE status information is provided. This information can be further utilized in different ways, such as for interference mitigation and / or flight restrictions. The UE status information may be used to disconnect or interrupt drone communications, or to alert relevant authorities who may take action against possible illegal activities.

[0201] The resulting state estimate can be further used by the network node or any external party for many purposes. It can be used to locate individual drones. The state information can be sent to another network node, such as another base station, a control node, O&M, SON, etc. The state information can also be stored in an internal or external memory for use at a later opportunity, such as for statistical analysis of a specific drone or multiple drones in a specific area. The state information can also trigger one or more operational tasks in the network node or another node. This may involve, for example, configuring specific measurements, signals or messages related to the state of the drone. The state information can also be used to trigger one or more actions in the network node or another node for controlling the movement of the drone. This may include stopping / blocking / redirecting the movement of the drone or preventing the drone from passing through a certain area. The state information can also trigger one or more preventive measures to control the impact of the drone movement. This may include alarms or notifications when the drone passes through a specified area.

[0202] A first action that may be taken based on the state information of the UE may be classification. For example, if the primary purpose is to detect the presence of a rogue drone, a specific pattern in the state information may indicate whether the UE is associated with a drone. A UE that spends some non-negligible time according to a hovering model may be a UE associated with a drone. Conversely, a UE that never leaves the ground plane is unlikely to be associated with a drone. In other words, in one embodiment, the method for state estimation of a UE includes a further step of classifying the UE based on the state estimation.

[0203] When a UE is classified as being associated with a drone, additional actions may be considered. One possibility is to influence signaling to and from the UE, for example to control interference contribution. In other words, in one embodiment, the method for state estimation of a UE comprises the further step of initiating an action regarding communication with the UE based on the state estimation.

[0204] One way to reduce the interference caused by UE signaling would be to simply stop such signaling. This can be done by requiring the UE to stop sending any signal in the uplink direction. Alternatively, scheduling uplink data from the UE may be stopped, which will reduce uplink traffic. In other words, in one embodiment, the step of initiating an action includes initiating a disabling of at least a portion of uplink communications from the UE.

[0205] If the UE is associated with a drone that may have illegally entered a restricted area, it may be of interest to at least interrupt the possibility for the operator of the drone to further control the drone. Any authorized party may try to gain control of the drone in order to navigate the drone out of the restricted area. Another possibility is to disable all downlink communications, which means that the operator of the drone can no longer control the drone. In other words, in one embodiment, the step of initiating an action includes initiating a disabling of downlink communications from the unauthorized party to the UE.

[0206] Shutting down the drone operator's extension may be to simply disconnect the UE from the wireless communication network completely. The UE's ID may be indicated as an unauthorized ID and connection to the wireless communication network is no longer allowed. In other words, in one embodiment, the step of initiating an action includes initiating the separation of the UE from the wireless communication network.

[0207] The impact of disconnecting the UE or disabling downlink signaling will most likely be that the drone aborts its mission and returns to its starting point using internal global positioning system (GPS) guidance. It may also crash or land in a controlled manner. If it returns, additional surveillance may be invoked in order to track the drone back and capture the pilot if the drone intrusion was hostile.

[0208] Since the accuracy of the state estimation is quite high, the disclosed technique can be used to pinpoint the location of a rogue drone very accurately. This opens the way for other actions, such as capturing the drone or even destroying it.

[0209] As used herein, the non-limiting terms "user equipment (UE)", "station (STA)" and "wireless communication device" or "wireless device" may refer to a mobile phone, a cellular phone, a personal digital assistant (PDA) equipped with radio communication capabilities, a smart phone, a laptop or personal computer (PC) equipped with an internal or external mobile broadband modem, a tablet PC with radio communication capabilities, a target device, a device-to-device UE, a machine type UE or a UE capable of machine-to-machine communication, an iPAD, a customer premises equipment (CPE), a laptop embedded equipment (LEE), a laptop mounted equipment (LME), a universal serial bus (USB) dongle, a portable electronic radio communication device, a sensor device equipped with radio communication capabilities, etc. In particular, the term "UE", the term "station", the term "wireless device" and the term "wireless communication device" should be interpreted as non-limiting terms, which include any type of wireless device that communicates with a network node in a wireless communication system and / or may communicate directly with another wireless communication device. In other words, a wireless communication device may be any device equipped with circuitry for wireless communication according to any relevant standard for communication.

[0210] As used herein, the non-limiting term "network node" may refer to a base station, an access point, a network control node such as a network controller, a radio network controller, a base station controller, an access controller, etc. In particular, the term "base station" may include different types of radio base stations, including standardized base stations such as Node B (NB), evolved Node B (eNB) or new radio Node B (gNB) and also macro / micro / pico radio base stations, home base stations (also known as femto base stations), relay nodes, repeaters, radio access points, base transceiver stations (BTS) and even radio control nodes that control one or more remote radio units (RRUs), etc.

[0211] In the following, the generic non-limiting term "communication unit" includes a network node and / or an associated wireless device.

[0212] As used herein, the term "network device" may refer to any device located in connection with a communication network, including but not limited to devices in access networks, core networks, and similar network structures. The term network device may also include cloud-based network devices.

[0213] It will be appreciated that the methods and apparatus described herein can be combined and rearranged in various ways.

[0214] For example, embodiments may be implemented in hardware or in software executed by suitable processing circuitry, or a combination thereof.

[0215] The steps, functions, processes, modules and / or blocks described herein may be implemented in hardware using any conventional technology, such as discrete circuit or integrated circuit technology, including both general purpose electronic circuits and application specific circuits.

[0216] Alternatively or in addition, at least some of the steps, functions, processes, modules and / or blocks described herein may be implemented using software, such as a computer program executed by a suitable processing circuit such as one or more processors or processing units.

[0217] Examples of processing circuitry include, but are not limited to, one or more microprocessors, one or more digital signal processors (DSPs), one or more central processing units (CPUs), video acceleration hardware, and / or any suitable programmable logic circuitry, such as one or more field programmable gate arrays (FPGAs) or one or more programmable logic controllers (PLCs).

[0218] It should also be understood that it may be possible to reuse the general processing capabilities of any conventional device or unit in which the proposed technology is implemented. It may also be possible to reuse existing software, for example by reprogramming it or by adding new software components.

[0219] According to one aspect of the proposed technology, there is provided a node configured for state estimation of a UE connected to a wireless communication network. The node is configured to obtain measurement information related to range-related measurements about the UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The node is further configured to perform state estimation of the UE based on the measurement information.

[0220] Fig.15 2 is a schematic block diagram illustrating an example of a network node 30 (e.g., a base station) based on a processor-memory implementation according to an embodiment. In this particular example, the network node 30 includes a processor 241 and a memory 251. The memory 251 includes instructions executable by the processor 241, whereby the processor 241 is operable to perform state estimation.

[0221] In one embodiment, the node is further configured to receive measurement information from another node.

[0222] Thus, optionally, the network node 30 may also include a communication circuit 201. The communication circuit 201 may include functionality for wired and / or wireless communication with other devices and / or network nodes in the network. In a specific example, the communication circuit 201 may be based on a radio circuit for communicating with one or more other nodes (including transmitting and / or receiving information). The communication circuit 201 may be interconnected to the processor 241 and / or the memory 251. For example, the communication circuit 201 may include any of the following items: a receiver, a transmitter, a transceiver, an input / output (I / O) circuit, (one or more) input ports and / or (one or more) output ports. The communication circuit 201 is operable to receive measurement information from another node.

[0223] In one embodiment, the node is configured to perform obtaining of the measurement information by performing range-related measurements with respect to the UE.In a further embodiment, the node comprises a processor and a memory, wherein the memory comprises instructions executable by the processor, whereby the processor is operable to perform range-related measurements with respect to the UE.

[0224] In one embodiment, the node is configured to perform state estimation of the UE based on measurement data, the measurement data comprising a value of a distance between the UE and a measurement location, a value of a one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of a radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of an RTT delay in the UE.

[0225] In one embodiment, the node is configured to perform a state estimation of the UE based on the position data of the measured position.

[0226] In a further embodiment, the node is further configured to retrieve the location of the node performing the range related measurements from the data storage device based on the identity of the node performing the range related measurements obtained as data enabling determination of the measurement location.

[0227] In one embodiment, the node is further configured to repeat the obtaining of measurement information and the performing of state estimation with respect to additional measurements of the UE.

[0228] In a further embodiment, repetitions are performed on measurements performed by different nodes.

[0229] In another further embodiment, the repetition is performed for an asynchronous measurement.

[0230] In one embodiment, the node is further configured to repeat the obtaining of the measurement information and the performing of the state estimation of the additional UE.Preferably, the UE and the additional UE are selected from a predetermined set of UEs.

[0231] In a further embodiment, the node is further configured to define a predetermined set of UEs.

[0232] In one embodiment, the state estimation includes interactive multiple model filtering. Preferably, the interactive multiple model filtering includes three interactive models. More preferably, the interactive multiple model filtering includes a three-dimensional constant velocity moving Wiener process, a three-dimensional constant acceleration moving Wiener process, and a three-dimensional constant position Wiener process.

[0233] In a further embodiment, the interactive multiple model filtering includes a reduced switching probability between a three-dimensional constant velocity movement Wiener process and a three-dimensional constant position Wiener process. Preferably, the reduced switching probability between the three-dimensional constant velocity movement Wiener process and the three-dimensional constant position Wiener process is at least two orders of magnitude lower than the switching probability involving a three-dimensional constant acceleration movement Wiener process.

[0234] In one embodiment, the state estimation comprises an extended Kalman filter.

[0235] In one embodiment, the node is further configured to subtract the geographic ground height from the estimated height to obtain the height above the ground.

[0236] In one embodiment, the node is further configured to initiate transmission of state estimation information. The state estimation information includes a state estimation information identifier, a UE identity, a time when the state estimation information is valid, and an estimated state of the UE. The estimated state includes at least a three-dimensional position, and preferably also includes a three-dimensional velocity.

[0237] In one embodiment, the node is further configured to classify the UE based on the state estimation.

[0238] In one embodiment, the node is further configured to initiate actions regarding communication with the UE based on the state estimate.

[0239] In a further embodiment, the initiation of the action includes initiating inhibition of downlink communications from the unauthorized party to the UE.

[0240] In another further embodiment, the initiating of the action includes initiating detachment of the UE from the wireless communication network.

[0241] In yet a further embodiment, the initiation of the action comprises initiating inhibition of uplink communications from the UE.

[0242] In one embodiment, the node is a base station. Preferably, the node is an evolved Node B or a new radio Node B.

[0243] According to another aspect of the proposed technology, there is provided a node configured to assist in state estimation of a UE connected to a wireless communication network. The node is configured to perform range-related measurements on the UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The node is further configured to initiate a transfer of measurement information related to the range-related measurements to a state estimation node.

[0244] Fig.15 It can also be interpreted as a schematic block diagram illustrating an example of a network node 30 (e.g., a base station) based on a processor-memory implementation according to another embodiment. In this particular example, the network node 30 includes a processor 241 and a memory 251, the memory 251 including instructions executable by the processor 241, whereby the processor 241 is operable to perform range-related measurements.

[0245] Optionally, the network node 30 may also include a communication circuit 201. The communication circuit 201 may include functionality for wired and / or wireless communication with other devices and / or network nodes in the network. In a specific example, the communication circuit 201 may be based on a radio circuit for communicating with one or more other nodes (including transmitting and / or receiving information). The communication circuit 201 may be interconnected to the processor 241 and / or the memory 251. For example, the communication circuit 201 may include any of the following items: a receiver, a transmitter, a transceiver, an input / output (I / O) circuit, (one or more) input ports and / or (one or more) output ports. The communication circuit 201 is operable to initiate the transmission of measurement information.

[0246] In one embodiment, the measurement data includes a value of the distance between the UE and the measurement location, a value of a one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of a radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the RTT delay in the UE.

[0247] In one embodiment, the data enabling determination of the measurement location comprises position data of the measurement location.

[0248] In another embodiment, the data enabling determination of the measurement location comprises the identity of the node.

[0249] In one embodiment, the node is further configured to repeat the performance of the range-related measurements and the initiation of the transfer of measurement information regarding additional measurements of the UE.

[0250] In one embodiment, the node is further configured to repeat the performing of the range-related measurements and the initiating of the transfer of the measurement information of the additional UE.Preferably, the UE and the additional UE are selected from a predetermined set of UEs.

[0251] In one embodiment, the node is a radio base station.Preferably, the node is an evolved NodeB or a New Radio NodeB.

[0252] According to another aspect of the proposed technology, there is provided a node configured to assist in state estimation of a UE connected to a wireless communication network. The node is configured to obtain measurement information related to a large number of range-related measurements about at least one UE. The measurement information includes data of the range-related measurements, the time when the range-related measurements are performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The node is further configured to compile the measurement information related to the large number of range-related measurements into compiled measurement information. The node is further configured to initiate the transfer of the compiled measurement information to the state estimation node.

[0253] Fig.15 It can also be interpreted as a schematic block diagram illustrating an example of a network node 30 (e.g., a base station) based on a processor-memory implementation according to another embodiment. In this particular example, the network node 30 includes a processor 241 and a memory 251, the memory 251 including instructions executable by the processor 241, whereby the processor 241 is operable to perform compilation of measurement information.

[0254] Optionally, the network node 30 may also include a communication circuit 201. The communication circuit 201 may include functionality for wired and / or wireless communication with other devices and / or network nodes in the network. In a specific example, the communication circuit 201 may be based on a radio circuit for communicating with one or more other nodes (including transmitting and / or receiving information). The communication circuit 201 may be interconnected to the processor 241 and / or the memory 251. For example, the communication circuit 201 may include any of the following items: a receiver, a transmitter, a transceiver, an input / output (I / O) circuit, (one or more) input ports and / or (one or more) output ports. The communication circuit 201 is operable to initiate the transmission of compiled measurement information.

[0255] In one embodiment, the node is configured to perform the obtaining of the measurement information by receiving the measurement information from another node.

[0256] In one embodiment, the node is configured to perform the obtaining of the measurement information by performing range-related measurements with respect to the UE.

[0257] In one embodiment, the obtained measurement information includes measurement data, which includes a value of the distance between the UE and the measurement location, a value of the one-way propagation time of the radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of the radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the RTT delay in the UE.

[0258] In one embodiment, the compiled measurement information includes measurement data, which includes a value of the distance between the UE and the measurement location, a value of the one-way propagation time of a radio signal between the UE and the measurement location or between the measurement location and the UE, an RTT value of the radio signal traveling back and forth between the UE and the measurement location, and / or a timestamp for downlink signal transmission, a timestamp for uplink signal reception triggered by the downlink signal, and a value of the RTT delay in the UE.

[0259] In one embodiment, the node is a base station. Preferably, the node is an evolved Node B or a new radio Node B. In a wireless communication network, for example, according to Figure 8-13 In any of the figures, there is at least one node configured for state estimation of a UE connected to a wireless communication network, and at least one node for assisting in state estimation of a UE connected to a wireless communication network. In other words, there is one node hosting a state estimation function and at least one node providing measurement data.

[0260] In some embodiments, the wireless communication network may further comprise intermediate nodes for assisting state estimation of UEs connected to the wireless communication network. These intermediate nodes host the functionality to compile measurement data according to the principles discussed further above.

[0261] Fig.16 2 is a schematic block diagram illustrating another example of a network node 30 based on a hardware circuit implementation according to an embodiment. Specific examples of suitable hardware (HW) circuits 211 include one or more appropriately configured or possibly reconfigurable electronic circuits, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or any other hardware logic, such as circuits based on discrete logic gates and / or flip-flops, which are interconnected to perform specialized functions in conjunction with suitable registers (REGs) and / or memory cells (MEMs).

[0262] Fig.172 is a schematic block diagram illustrating another example of a network node 30 based on a combination of (one or more) processors 241-1, 241-2 and hardware circuits 211-1, 211-2 in combination with (one or more) suitable memories 251. The network node 30 includes one or more processors 241-1, 241-2, a memory 251 including storage for software and data, and one or more units of hardware circuits 211-1, 211-2 (such as ASICs and / or FPGAs). Thus, the overall functionality is divided between programming software (SW) for execution on one or more processors 241-1, 241-2 and one or more preconfigured or possibly reconfigurable hardware circuits 211-1, 211-2 (such as ASICs and / or FPGAs). The actual hardware-software division can be determined by the system designer based on many factors, including processing speed, implementation cost, and other requirements.

[0263] Alternatively or additionally, at least some of the steps, functions, processes, modules and / or blocks described herein may be implemented by software, such as a computer program executed by a suitable processing circuit such as one or more processors or processing units.

[0264] When executed by one or more processors, the one or more flowcharts presented herein may therefore be viewed as one or more computer flow charts. The corresponding device may be defined as a set of functional modules, wherein each step performed by the processor corresponds to a functional module. In this case, the functional module is implemented as a computer program running on the processor.

[0265] Examples of processing circuitry include, but are not limited to, one or more microprocessors, one or more digital signal processors (DSPs), one or more central processing units (CPUs), video acceleration hardware, and / or any suitable programmable logic circuitry, such as one or more field programmable gate arrays (FPGAs) or one or more programmable logic controllers (PLCs).

[0266] It should also be understood that it may be possible to reuse the general processing capabilities of any conventional device or unit in which the proposed technology is implemented. It may also be possible to reuse existing software, for example by reprogramming it or by adding new software components.

[0267] Fig.182 is a schematic diagram illustrating an embodiment of a computer implementation of a network node 30. In this particular embodiment, at least some of the steps, functions, processes, modules and / or blocks described herein are implemented with a computer program 231, which is loaded into a memory 251 for execution by a processing circuit including one or more processors 241. The (one or more) processors 241 and the memory 251 are interconnected to each other to enable normal software execution. The optional input / output device 204 may also be interconnected to the (one or more) processors 241 and / or the memory 251 to enable the input and / or output of relevant data (such as (one or more) input parameters and / or the resulting (one or more) output parameters).

[0268] The term "processor" should be interpreted in a general sense as any system or device capable of executing program code or computer program instructions to perform specific processing, determination or computing tasks.

[0269] Thus, the processing circuitry including one or more processors 241 is configured to perform well-defined processing tasks (such as those described herein) when executing the computer program 231. The terms "processing circuitry" and "processor" will be used as synonyms in this disclosure.

[0270] The processing circuitry need not be dedicated to performing only the above-described steps, functions, processes and / or blocks, but may also perform other tasks.

[0271] In a particular embodiment, the computer program 231 includes instructions that, when executed by at least one processor 241, cause the processor(s) 241 to obtain measurement information related to range-related measurements about the UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The instructions, when executed by the processor(s), cause the processor(s) to perform a state estimation of the UE based on the measurement information.

[0272] In another specific embodiment, the computer program 231 includes instructions that, when executed by at least one processor 241, cause the processor(s) 241 to perform range-related measurements with respect to the UE, and wherein the instructions, when executed by the processor(s), cause the processor(s) to initiate a transfer of measurement information related to the range-related measurements to the state estimation node. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location.

[0273] In yet another specific embodiment, the computer program 231 includes instructions that, when executed by at least one processor 241, cause the processor(s) 241 to obtain measurement information related to a large number of range-related measurements about at least one UE. The measurement information includes data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The instructions, when executed by the processor(s), cause the processor(s) to compile the measurement information related to the large number of range-related measurements into compiled measurement information. The instructions, when executed by the processor(s), cause the processor(s) to initiate a transfer of the compiled measurement information to a state estimation node.

[0274] The proposed technology also provides a carrier including the computer program, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal or a computer-readable storage medium.

[0275] For example, the software or computer program 230, 231 may be implemented as a computer program product, which is normally carried or stored on a computer readable medium 255, in particular a non-volatile medium. The computer readable medium may include one or more removable or non-removable memory devices, including but not limited to read-only memory (ROM), random access memory (RAM), compact disk (CD), digital versatile disk (DVD), Blu-ray disk, universal serial bus (USB) memory, hard disk drive (HDD) storage device, flash memory, magnetic tape or any other conventional memory device. The computer program can thus be loaded into the operating memory of a computer or equivalent processing device for execution by its processing circuit.

[0276] Thus, in one embodiment, a computer program product comprises a computer readable medium having stored thereon a computer program as described herein above.

[0277] In one embodiment, the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

[0278] Fig.19 is a schematic block diagram illustrating an example of a network device (ND) 40 including a network node 30 according to any of the embodiments.

[0279] According to an aspect, there is provided a network device 40 comprising a network node 30 as described herein.

[0280] The network device may be any suitable network device in a wireless communication system, or a network device connected to a wireless communication system. For example, the network device may be a suitable network node, such as a base station or an access point. However, the network device may alternatively be a cloud-implemented network device.

[0281] According to another aspect, there is provided a communication unit 10 in a wireless communication system, wherein the communication unit 10 comprises a network node 30 as described herein. The communication unit may be any suitable communication unit in a wireless communication system. For example, the communication unit may be a wireless communication device, such as a UE, a STA or a similar end-user device.

[0282] When executed by one or more processors, one or more flowcharts presented herein may be viewed as one or more computer flowcharts. The corresponding device may be defined as a set of functional modules, wherein each step performed by the processor corresponds to a functional module. In this case, the functional module is implemented as a computer program running on the processor.

[0283] The computer program resident in the memory may thus be organized into appropriate functional modules that are configured to, when executed by the processor, perform at least part of the steps and / or tasks described herein.

[0284] Fig. 20 is a schematic diagram illustrating an example of a network node 30 for state estimation of a UE connected to a wireless communication network. The network node 30 comprises an obtaining module 310 for obtaining measurement information related to range-related measurements about the UE. The measurement information comprises data of the range-related measurements, the time at which the range-related measurements are performed, the identity of the UE and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The network node 30 further comprises a state estimator 320 for performing state estimation of the UE based on the measurement information.

[0285] Fig.21 is a schematic diagram illustrating an example of a network node 30 for assisting in state estimation of a UE connected to a wireless communication network. The network node 30 comprises a measurement module 330 for performing range-related measurements on the UE. The network node 30 further comprises a delivery module 340 for initiating a delivery of measurement information related to the range-related measurements to the state estimation node. The measurement information comprises data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the UE and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location.

[0286] Fig. 22is a schematic diagram illustrating an example of a network node 30 for assisting in state estimation of a UE connected to a wireless communication network. The network node 30 includes an obtaining module 350, the obtaining module 350 being used to obtain measurement information related to a large number of range-related measurements about at least one UE. The measurement information includes data of the range-related measurements, the time when the range-related measurements were performed, the identity of the UE, and data enabling determination of the measurement location. The range-related measurements consist of data defining the distance between the UE and the measurement location. The network node 30 further includes a compiler 360, the compiler 360 being used to compile the measurement information related to the large number of range-related measurements into compiled measurement information. The network node 30 further includes a transmission module 370, the transmission module 370 being used to initiate transmission of the compiled measurement information to the state estimation node.

[0287] Alternatively, it may be implemented primarily by hardware modules or alternatively by hardware through appropriate interconnection between relevant modules. Figure 20-22 Specific examples include one or more appropriately configured digital signal processors and other known electronic circuits, such as discrete logic gates interconnected to perform specialized functions and / or application specific integrated circuits (ASICs) as described above. Other examples of usable hardware include input / output (I / O) circuits and / or circuits for receiving and / or sending signals. The degree of software versus hardware is purely an implementation choice.

[0288] For the purpose of illustrating the operation and performance of the proposed technique, a UAV IMM estimator performance has been simulated.

[0289] The state estimation scenario is as follows:

[0290] The drone starts at an initial position [0 0 0] with an initial velocity [0 0 0]. During the 40s upward period, it continues with constant acceleration (i.e., speed increase) for a short period of time, then constant speed, followed by braking (mode 1). It then hovers for 20s (mode 3). The movement is followed by constant acceleration, i.e., speed increase, followed by constant speed, to the right for 20s (mode 1). It then makes a coordinated left turn for 20s (mode 2). Continue to move to the right at a constant speed for 20s (mode 1). The drone then performs a coordinated right turn for 20s (mode 2). The drone then decelerates for 20s (mode 1). Then, finally, the drone hovers for 20s (mode 3).

[0291] The site locations of the measurement nodes are:

[0292] S1=(200 100 5)′

[0293] S2 = (100-100 30)′

[0294] S3 = (600-50 50)′

[0295] S4=(500 50 40)′

[0296] S5=(30 0 100)′

[0297] S6 = (400 20 120)′

[0298] The parameters used are:

[0299] T==1 second

[0300] n == 180 Number of discrete time steps

[0301] The noise variance is 1m 2

[0302] The process noise variances for the constant velocity, acceleration, and hovering models are 0.001, 1.0, and 0.01, respectively.

[0303] The IMM transition probability matrix is:

[0304]

[0305] The initial conditions are:

[0306]

[0307] exist Fig.23 and Fig.24 The simulation results are shown in . Fig.23 The simulated true trajectory (solid line), estimated trajectory (dashed line) and station locations (triangles) are shown. The agreement between the estimated and true trajectory is convincing.

[0308] Fig.24 The true and estimated mode probabilities for different models are shown. The true modes are shown by solid lines, while the corresponding estimated mode probabilities are represented by dotted or dashed lines. Simulations demonstrate that the estimated trajectories are highly accurate and that the estimated mode probabilities behave as expected.

[0309] It is becoming increasingly common to provide computing services (hardware and / or software) in network devices (such as network nodes and / or servers), where resources are delivered as services over the network to a remote location. This means, for example, that the functional performance as described herein is distributed or relocated to one or more separate physical nodes or servers. The functionality may be relocated or distributed to one or more physical and / or virtual machines acting in conjunction, which may be located in (one or more) separate physical nodes, i.e., in the so-called cloud. This is sometimes also referred to as cloud computing, which is a model for enabling ubiquitous, on-demand network access to a pool of configurable computing resources (such as networks, servers, storage devices, applications, and general or custom services).

[0310] There are different forms of virtualization that may be useful in this context, including one or more of the following:

[0311] Consolidating network functionality into virtualized software running on custom or general-purpose hardware. This is sometimes referred to as network function virtualization.

[0312] Co-locating one or more application stacks (including operating systems) running on separate hardware onto a single hardware platform is sometimes referred to as system virtualization or platform virtualization.

[0313] Co-location of hardware and / or software resources with the goal of obtaining improved system resource utilization using some advanced domain-level scheduling and coordination techniques. This is sometimes referred to as resource virtualization, or centralized and coordinated resource pooling.

[0314] While it may often be desirable to centralize functionality in a so-called common data center, in other scenarios it may actually be beneficial to distribute functionality across different parts of the network.

[0315] Fig.25 is a schematic diagram illustrating an example of how functionality can be distributed or divided between different network devices (NDs) in a general case. In this example, there are at least two separate but interconnected network devices ND 1 and ND 2, respectively, with reference numerals 40A and 40B, which may have different functionality or different parts of the same functionality divided between network devices 40A and 40B. There may be additional network devices as part of such a distributed implementation, such as ND 3 with reference numeral 40C. The network devices 40A-C may be part of the same wireless communication system, or one or more of the network devices may be so-called cloud-based network devices located outside the wireless communication system.

[0316] Fig.26is a schematic diagram illustrating an example of a wireless communication system including an access network 120 and / or a core network 130 and / or an operation and support system (OSS) 135 cooperating with one or more cloud-based network devices 40. Functionality associated with the access network 120 and / or the core network 130 and / or the OSS system 135 may be at least partially implemented to be performed in the cloud-based network device 40, wherein appropriate information is transferred between the cloud-based network device and relevant network nodes and / or communication units in the access network and / or the core network and / or the OSS system.

[0317] A network device (ND) may generally be viewed as an electronic device that is communicatively connected to other electronic devices in a network.

[0318] For example, the network device can be implemented with hardware, software or a combination thereof. For example, the network device can be a dedicated network device or a general-purpose network device, or a mixture thereof.

[0319] A dedicated network device may use customized processing circuitry and a proprietary operating system (OS) to execute software that provides one or more of the features or functions disclosed herein.

[0320] The general purpose network device may use a common off-the-shelf (COTS) processor and a standard OS to execute software configured to provide one or more of the features or functions disclosed herein.

[0321] For example, a dedicated network device may include hardware that includes (one or more) processing or computing resources (which typically include a collection of one or more processors) and a physical network interface (NI) (which is sometimes referred to as a physical port) and a non-transitory machine-readable storage medium on which software is stored. The physical NI can be viewed as hardware in the network device through which a network connection is made, such as wirelessly through a wireless network interface controller (WNIC) or by plugging a cable into a physical port connected to a network interface controller (NIC). During operation, the software can be executed by the hardware to instantiate a collection of one or more software instances. Each of the (one or more) software instances and the portion of the hardware that executes the software instance can form a separate virtual network element.

[0322] As another example, a general network device may, for example, include hardware including a set of one or more processors (often COTS processors) and (one or more) network interface controllers (NICs), as well as a non-transitory machine-readable storage medium on which software is stored. During operation, (one or more) processors execute software to instantiate one or more sets of one or more applications. Although one embodiment does not implement virtualization, alternative embodiments may use different forms of virtualization—represented, for example, by a virtualization layer and software containers. For example, one such alternative embodiment implements virtualization at the operating system level, in which case the virtualization layer represents the kernel of the operating system (or a shim executed on the base operating system) that allows for the creation of multiple software containers, each of which can be used to execute one of the sets of applications. In an example embodiment, each of the software containers (also referred to as virtualization engines, virtual private servers, or jails) is a user space instance (typically a virtual memory space). These user space instances can be separate from each other and from the kernel space in which the operating system is executed; a set of applications running in a given user space cannot access the memory of other processes unless explicitly allowed. Another such alternative embodiment implements full virtualization, in which case: 1) the virtualization layer represents a hypervisor (sometimes called a virtual machine monitor (VMM)), or a hypervisor executing on top of a host operating system; and 2) the software containers each represent a tightly isolated form of software container called a virtual machine, which is executed by the hypervisor and may include a guest operating system.

[0323] The hypervisor is the software / hardware responsible for creating and managing various virtualized instances, and in some cases actual physical hardware. The hypervisor manages the underlying resources and presents them as virtualized instances. What the hypervisor virtualizes to appear as a single processor may actually include multiple separate processors. From the operating system's perspective, the virtualized instances appear to be actual hardware components.

[0324] A virtual machine is a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine; and applications are typically unaware that they are running on a virtual machine rather than on "bare metal" host electronics, but some systems provide para-virtualization, which allows the operating system or application to be aware of the presence of virtualization for optimization purposes.

[0325] One or more sets of one or more applications and the instantiation of the virtualization layer and software container (if implemented) are collectively referred to as (one or more) software instances. Each set of applications, the corresponding software container (if implemented), and the hardware portion on which they are executed (be it hardware dedicated to the execution and / or a hardware time slice temporarily shared by the software container), form (one or more) separate virtual network elements.

[0326] Compared to (one or more) virtual network elements (VNEs), (one or more) virtual network elements may perform similar functionality. This hardware virtualization is sometimes referred to as network function virtualization (NFV). Thus, NFV can be used to integrate many network device types onto industry-standard high-capacity server hardware, physical switches, and physical storage devices, which may be located in data centers ND and customer premises equipment (CPE). However, different embodiments may implement one or more software containers differently. For example, although the embodiments are described with each software container corresponding to a VNE, alternative embodiments may implement such a correspondence or mapping between software containers and VNEs at a finer level of granularity; it should be understood that the techniques described herein with reference to the correspondence between software containers and VNEs are also applicable to embodiments using such a finer level of granularity.

[0327] According to yet another embodiment, a hybrid network device is provided that includes both a custom processing circuit / proprietary OS and a COTS processor / standard OS in the network device (e.g., in a card or circuit board within an ND). In certain embodiments of such a hybrid network device, a platform virtual machine (VM) (such as a VM that implements the functionality of a dedicated network device) may provide for paravirtualization to hardware present in the hybrid network device.

[0328] The present technology discloses in a preferred embodiment an IMM filter with a new combination of models, adapted to the hovering capability of a UAV, with a new restricted mode transition probability model, features reflecting the hovering capability, and integrated measurement fusion with only range measurements on multiple eNBs / gNBs. In addition, a new signaling notification component is proposed for distributing the estimated UAV information and information derived therefrom to the RAN eNB / gNB, other EPC nodes other than the state estimation node, or external sources. In addition, the preferred embodiment includes a node architecture of the UAV state estimation system, which resides in the eNB / gNB, or is closely associated with it, or in an external node. A new range measurement principle is used that employs associated signaling that allows high accuracy and range measurements between multiple base stations and UAVs.

[0329] The embodiments described above are given as examples only, and it should be understood that the proposed technology is not limited thereto. It will be appreciated by those skilled in the art that various modifications, combinations and changes may be made to the embodiments without departing from the current scope defined by the appended claims. In particular, different partial solutions in different embodiments can be combined in other configurations where technically possible.

[0330] abbreviation:

[0331] 3GPP Third Generation Partnership Project

[0332] ASIC

[0333] BTS Base Transceiver Station

[0334] CD compact disk

[0335] CN Core Network

[0336] COTS ordinary spot

[0337] CPE Customer Premises Equipment

[0338] CPU Central Processing Unit

[0339] DSP Digital Signal Processor

[0340] DVD Digital Versatile Disc

[0341] EKF Extended Kalman Filter

[0342] eNB Evolved Node B

[0343] FPGA Field Programmable Gate Array

[0344] GIS Geographic Information System

[0345] gNB New Radio Node B

[0346] GPS Global Positioning System

[0347] HDD Hard Drive

[0348] HW Hardware

[0349] IMM Interactive Multi-Model

[0350] I / O Input / Output

[0351] LEE Laptop Embedded Device

[0352] LME Laptop Mounted Device

[0353] LTE Long Term Evolution

[0354] MEM memory unit

[0355] NB Node B

[0356] ND Network Device

[0357] NFV Network Function Virtualization

[0358] NI Network Interface

[0359] NIC Network Interface Controller

[0360] NR New Radio

[0361] OS Operating System

[0362] OSS Operation and Support System

[0363] PC Personal Computer

[0364] PDA Personal Digital Assistant

[0365] PLC Programmable Logic Controller

[0366] RAM Random Access Memory

[0367] RAN Radio Access Network

[0368] REG Register

[0369] ROM Read Only Memory

[0370] RRU Remote Radio Unit

[0371] RTT Round Trip Time

[0372] STA Station

[0373] SW software

[0374] UE User Equipment

[0375] USB Universal Serial Bus

[0376] VM Virtual Machine

[0377] VMM virtual machine monitor

[0378] VNE Virtual Network Element

[0379] WNIC Wireless Network Interface Controller

[0380] Appendix A

[0381] Figure 2 A typical IMM algorithm consists of r interacting filters operating in parallel. k-1The conditional probability is mixed at the input of the filter in the state interaction / mixing unit. The structure of the IMM algorithm is:

[0382] (N e ; N f )=(r;r), (A1)

[0383] Where N e is the estimated number at the beginning of the algorithm cycle, and N f is the number of filters in the algorithm.

[0384] One cycle of the algorithm consists of the following: Calculate the mixing probability (i, j = 1, ..., r). Given M j In Z k-1 If condition k is valid, then mode M i The probability of being valid at time k-1 is:

[0385]

[0386] where the normalization constant is given by the following equation. This equation uses the mode transition probability pij, which is the corresponding probability that the estimated object is in mode j at time k, provided that it is in mode i at time k-1. The expression for the normalization constant is:

[0387]

[0388] Next, mixing is performed for (j=1, ...r). From Start with a component and match to M j The mixed initial condition of the filter of (k) is:

[0389]

[0390] The covariance corresponding to the above is:

[0391]

[0392] Use ' to indicate transposition.

[0393] Next, pattern matching filtering is performed for (j=1, ...r). The estimates and covariances obtained in equation (5) are used as j (k) The input of the matched filter, which is generated using z(k) and P j (k|k).

[0394] The likelihood function corresponding to r filters is:

[0395] Λ j (k)=p[z(k)|M j (k), Zk-1 ] (A6)

[0396] Using mixed initial conditions and the associated covariance is calculated as:

[0397]

[0398] For (j=1, ...r), a model probability update is performed. This is done as follows:

[0399]

[0400] in Given above, and

[0401]

[0402] is the normalization factor.

[0403] Perform estimation and covariance combining. Combining of estimated covariances conditional on the model is performed according to the mixing equation:

[0404]

[0405] Appendix B

[0406] The dynamic model of EKF is:

[0407] x(k+1)=F(k)x(k)+v(k), (B1)

[0408] where x(k) is the nx-dimensional state vector, and v(k), k = 0, 1, ... is a zero-mean white Gaussian process noise sequence (also n x vectors):

[0409]

[0410] The measurement equation is:

[0411] z(k)=h(x(k),k)+w(k)k=1,… (B3)

[0412] where h(.) is a nonlinear function of the state and w(k) is a zero-mean Gaussian measurement noise sequence with the following covariance:

[0413]

[0414] The matrices F, Q, R and the function h(.) are assumed to be known and possibly time-varying. In other words, the system may be time-varying and the noise is unstable. The Jacobian of the measurement model h(x(k), k) with respect to k is defined as:

[0415]

[0416] The unknown initial state x(0) is modeled as a Gaussian distributed random variable with known mean and covariance. The two noise sequences and the initial state are assumed to be independent of each other. This constitutes the linear Gaussian (LG) assumption.

[0417] Conditional mean:

[0418]

[0419] Where Z k = {z(j), j≤k} represents the observation sequence available at time k, which is the state estimate for j=k and the state prediction for j>k. Given data Z k Or the covariance associated with the estimate, the conditional covariance matrix of x(j) is:

[0420]

[0421] The estimation algorithm starts with an initial estimate of x(0) that is assumed to be available and the associated initial covariance P(0|0). The second (conditional) index 0 represents the initial information Z 0 .

[0422] Thus, one cycle of the dynamic estimation algorithm (Extended Kalman Filter (KF)) will consist of the calculations to obtain the estimate:

[0423]

[0424] It is the conditional mean of the state at time k (the current stage) given the observations up to and including time k, and the associated covariance matrix is:

[0425]

[0426] References

[0427] [1] T. Wigren, “Wireless hybrid positioning based on surface modeling with polygon support,” Proc. VTC 2018 Spring, Porto, Portugal, June 2018.

Claims

1. A method for state estimation of a user equipment (10) connected to a wireless communication network (1), wherein the method comprises the following steps: - obtaining (S1) measurement information (62, 64) relating to range-related measurements about a user equipment (10); The measurement information (62, 64) comprises data of the range-related measurement, the time at which the range-related measurement was performed, the identity of the user equipment (10) and data enabling determination of the measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location; - performing (S2) state estimation of the user equipment (10) based on the measurement information; and - classifying the user equipment (10) as being associated with a drone based on the state estimate of the user equipment (10).

2. The method according to claim 1, characterized in that The step of obtaining (S1) measurement information (62, 64) comprises receiving said measurement information from a node performing said range-related measurements.

3. The method according to claim 1 or 2, characterized in that Said step of obtaining (S1) measurement information (62, 64) comprises performing said range-related measurements in relation to said user equipment (10).

4. The method according to claim 1 or 2, characterized in that: The measurement data comprises at least one of the following: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

5. The method according to claim 1 or 2, characterized in that: The data enabling determination of a measurement location include position data of the measurement location.

6. The method according to claim 1 or 2, characterized in that: The data enabling determination of the measurement location comprises the identity of the node performing the range-related measurements, whereby the method comprises the further steps of: - based on said identity of said node performing said range related measurements, retrieving from a data storage device a location of said node performing said range related measurements.

7. The method according to claim 1 or 2, characterized in that: The range-related measurements are performed by a base station (20).

8. The method according to claim 1 or 2, characterized in that: The steps of obtaining (S1) measurement information (62, 64) and performing (S2) state estimation of additional measurements on the user equipment (10) are repeated (S3).

9. The method according to claim 8, characterized in that The repetition (S3) is performed on measurements performed by different nodes.

10. The method according to claim 8, characterized in that The repetition (S3) is performed for an asynchronous measurement.

11. The method according to claim 1 or 2, characterized in that: The steps of obtaining (S1) measurement information (62, 64) and performing (S2) state estimation of additional user equipment (10) are repeated.

12. The method according to claim 11, characterized in that The user equipment (10) and the additional user equipment (10) are selected from a predetermined set of user equipment.

13. The method according to claim 12, characterized in that Further steps below: - defining said predetermined set of user equipments.

14. The method according to claim 1 or 2, characterized in that: Said step of performing (S2) state estimation comprises interacting multiple model filtering.

15. The method according to claim 14, characterized in that The interacting multi-model filtering includes three interacting models.

16. The method according to claim 15, characterized in that The interactive multi-model filtering includes a three-dimensional constant velocity moving Wiener process, a three-dimensional constant acceleration moving Wiener process and a three-dimensional constant position Wiener process.

17. The method according to claim 16, characterized in that The interacting multiple model filtering includes a reduced switching probability between the three-dimensional constant velocity movement Wiener process and the three-dimensional constant position Wiener process.

18. The method according to claim 17, characterized in that The reduced switching probability between the three-dimensional constant velocity movement Wiener process and the three-dimensional constant position Wiener process is at least two orders of magnitude lower than the switching probability involving the three-dimensional constant acceleration movement Wiener process.

19. The method according to claim 1 or 2, characterized in that: Said step of performing (S2) state estimation comprises an extended Kalman filter.

20. The method according to claim 1 or 2, characterized in that Following further steps: Subtract the geographic ground height from the estimated height to obtain the height above the ground.

21. The method according to claim 1 or 2, characterized in that The following further step: initiating transmission of state estimation information (68), wherein the state estimation information (68) includes a state estimation information identifier, a user device (10) identity, a time when the state estimation information is valid, and an estimated state, wherein the estimated state includes at least a three-dimensional position.

22. The method according to claim 21, characterized in that The estimated state also includes three-dimensional velocity.

23. The method according to claim 1 or 2, characterized in that Further steps below: - initiating an action regarding communication with said user equipment (10) based on said state estimate.

24. The method of claim 23, wherein: The step of initiating an action includes initiating inhibition of downlink communications from an unauthorized party to the user equipment (10).

25. The method of claim 23, wherein: The step of initiating an action comprises initiating disabling of at least a portion of uplink communications from the user equipment (10).

26. The method of claim 23, wherein: The step of initiating an action comprises initiating a detachment of the user equipment (10) from the wireless communication network (1).

27. A method for assisting state estimation of a user equipment (10) connected to a wireless communication network (1), wherein the method comprises the following steps: - performing (S11) range-related measurements on said user equipment (10); as well as - initiating (S12) a transfer of measurement information (62, 64) related to said range-related measurements to a state estimation node; The measurement information (62, 64) comprises data of the range-related measurement, the time at which the range-related measurement was performed, the identity of the user equipment (10) and data enabling determination of the measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location, The state estimate of the user equipment (10) is used to classify the user equipment (10) as being associated with a drone.

28. The method of claim 27, wherein: The measurement data includes at least one of the following items: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

29. The method according to claim 27 or 28, characterized in that The data enabling determination of a measurement location include position data of the measurement location.

30. The method according to claim 27 or 28, characterized in that Said data enabling determination of the measurement location comprises the identity of the node performing said range-related measurement.

31. The method according to claim 27 or 28, characterized in that Repeating (S13) said steps of performing (S11) said range related measurements and initiating (S12) said transfer of measurement information (62, 64) of additional measurements concerning said user equipment (10).

32. The method according to claim 27 or 28, characterized in that The steps of performing (S11) the range-related measurements and initiating (S12) the transfer of measurement information (62, 64) of additional user equipments (10) are repeated.

33. The method of claim 32, wherein: The user equipment (10) and the additional user equipment (10) are selected from a predetermined set of user equipment.

34. The method according to claim 27 or 28, characterized in that The method is performed in a radio base station (20).

35. A method for assisting state estimation of a user equipment (10) connected to a wireless communication network (1), wherein the method comprises the following steps: - obtaining (S21) measurement information (62) relating to a number of range-related measurements about at least one user equipment (10); the measurement information (62) comprising data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the user equipment (10) and data enabling determination of the measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location; - compiling (S22) said measurement information (62) relating to a plurality of range-related measurements into compiled measurement information (64); as well as - initiating (S23) the transfer of said compiled measurement information (64) to the state estimation node (60), The state estimate of the user equipment (10) is used to classify the user equipment (10) as being associated with a drone.

36. The method of claim 35, wherein: Said step of obtaining (S21) measurement information comprises receiving the measurement information (62) from another node.

37. The method according to claim 35 or 36, characterized in that The step of obtaining (S21) measurement information comprises performing range-related measurements on the user equipment (10).

38. The method according to claim 35 or 36, characterized in that The obtaining (S21) of measurement information includes measurement data, wherein the measurement data includes at least one of the following items: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

39. The method according to claim 35 or 36, characterized in that The compiled measurement information (64) includes measurement data, the measurement data including at least one of the following: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

40. The method according to claim 35 or 36, characterized in that The method is executed in a base station (20).

41. A node (30) configured for state estimation of a user equipment (10) connected to a wireless communication network (1), wherein: The node (30) is configured to obtain measurement information (62, 64) relating to range-related measurements about a user equipment (10); The measurement information (62, 64) comprises data of the range-related measurement, the time at which the range-related measurement was performed, the identity of the user equipment (10) and data enabling determination of the measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location; wherein the node (30) is further configured to perform state estimation of the user equipment (10) based on the measurement information (62, 64); and The node (30) is further configured to classify the user equipment (10) as being associated with a drone based on the state estimate of the user equipment (10).

42. The node according to claim 41, characterized in that The node (30) comprises a processor (241) and a memory (251), the memory (251) comprising instructions executable by the processor (241), whereby the processor (241) is operable to perform the state estimation.

43. A node according to claim 41 or 42, characterized in that The node (30) is further configured to receive the measurement information (62, 64) from another node.

44. A node according to claim 41 or 42, characterized in that The node comprises a communication circuit (201) configured to receive the measurement information (62, 64) from another node.

45. A node according to claim 41 or 42, characterized in that The node (30) is configured to obtain the measurement information (62, 64) by performing the range-related measurements with respect to the user equipment (10).

46. ​​The node according to claim 45, characterized in that The node (30) comprises a processor (241) and a memory (251), the memory (251) comprising instructions executable by the processor (241), whereby the processor (241) is operable to perform the range-related measurements with respect to the user equipment (10).

47. A node according to claim 41 or 42, characterized in that The node (30) is configured to perform the state estimation of the user equipment (10) based on measurement data, the measurement data comprising at least one of the following: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

48. A node according to claim 41 or 42, characterized in that The node (30) is configured to perform the state estimation of the user equipment (10) based on the position data of the measured position.

49. The node according to claim 48, characterized in that The node (30) is further configured to retrieve the location of the node performing the range-related measurement from a data storage device based on the identity of the node performing the range-related measurement obtained as the data enabling determination of the measurement location.

50. The node according to claim 41 or 42, characterized in that The node (30) is further configured to repeat the obtaining of measurement information and the performing of state estimation with respect to additional measurements of the user equipment (10).

51. The node of claim 50, wherein: The repetition is performed on measurements performed by different nodes.

52. The node of claim 50, wherein: The repetition is performed for an asynchronous measurement.

53. The node according to claim 41 or 42, characterized in that The node (30) is further configured to repeat the obtaining of measurement information and the performing of state estimation of additional user equipments (10).

54. The node according to claim 53, characterized in that The user equipment (10) and the additional user equipment (10) are selected from a predetermined set of user equipment.

55. The node of claim 54, wherein: The node (30) is further configured to define the predetermined set of user equipments.

56. The node according to claim 41 or 42, characterized in that The state estimation includes interacting multiple model filtering.

57. The node according to claim 56, characterized in that The interacting multi-model filtering includes three interacting models.

58. The node according to claim 57, characterized in that The interactive multi-model filtering includes a three-dimensional constant velocity moving Wiener process, a three-dimensional constant acceleration moving Wiener process and a three-dimensional constant position Wiener process.

59. The node according to claim 58, characterized in that The interacting multiple model filtering includes a reduced switching probability between the three-dimensional constant velocity movement Wiener process and the three-dimensional constant position Wiener process.

60. The node according to claim 59, characterized in that The reduced switching probability between the three-dimensional constant velocity movement Wiener process and the three-dimensional constant position Wiener process is at least two orders of magnitude lower than the switching probability involving the three-dimensional constant acceleration movement Wiener process.

61. The node according to claim 41 or 42, characterized in that The state estimation includes an extended Kalman filter.

62. The node according to claim 41 or 42, characterized in that The node (30) is further configured to subtract the geographic ground height from the estimated height to obtain the height above the ground.

63. The node according to claim 41 or 42, characterized in that The node (30) is further configured to initiate transmission of state estimation information, the state estimation information comprising a state estimation information identifier, an identity of the user device (10), a time when the state estimation information is valid, and an estimated state, the estimated state comprising at least a three-dimensional position.

64. The node according to claim 63, characterized in that The estimated state also includes three-dimensional velocity.

65. The node according to claim 41 or 42, characterized in that The node (30) is further configured to initiate an action regarding communication with the user equipment (10) based on the state estimate.

66. The node of claim 65, wherein: The initiation of an action includes initiating inhibition of downlink communications from an unauthorized party to the user equipment (10).

67. The node of claim 65, wherein: The initiation of an action comprises initiating disabling of at least a portion of uplink communications from the user equipment (10).

68. The node of claim 65, wherein: The initiation of an action comprises initiating a detachment of the user equipment (10) from the wireless communication network.

69. The node according to claim 41 or 42, characterized in that The node (30) is a base station (20).

70. The node of claim 69, wherein: The node (30) is an evolved Node B or a New Radio Node B (32).

71. A node (30) configured to assist in state estimation of a user equipment (10) connected to a wireless communication network (1), in, The node (30) is configured to perform range-related measurements with respect to the user equipment; as well as wherein the node (30) is further configured to initiate a transfer of measurement information (62, 64) related to the range-related measurement to a state estimation node (60); The measurement information comprises data of the range-related measurement, a time at which the range-related measurement was performed, an identity of the user equipment (10) and data enabling determination of a measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location, The state estimate of the user equipment (10) is used to classify the user equipment (10) as being associated with a drone.

72. The node of claim 71, wherein: The node (30) comprises a processor (241) and a memory (251), the memory (251) comprising instructions executable by the processor (241), whereby the processor (241) is operable to perform the range-related measurements.

73. A node according to claim 71 or 72, characterized in that The node (30) comprises a communication circuit (201) configured to initiate the transfer of measurement information.

74. A node according to claim 71 or 72, characterized in that The measurement data includes at least one of the following items: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

75. A node according to claim 71 or 72, characterized in that The data enabling determination of a measurement location include position data of the measurement location.

76. A node according to claim 71 or 72, characterized in that Said data enabling determination of the measurement location comprises the identity of said node.

77. A node according to claim 71 or 72, characterized in that The node (30) is further configured to repeat the performing of the range related measurements and the initiating of the transferring of the measurement information of the additional measurements regarding the user equipment (10).

78. A node according to claim 71 or 72, characterized in that The node (30) is further configured to repeat the performing of the range-related measurements and the initiating of the transferring of the measurement information of additional user equipments (10).

79. The node according to claim 78, characterized in that The user equipment (10) and the additional user equipment (10) are selected from a predetermined set of user equipment.

80. The node according to claim 71 or 72, characterized in that The node (30) is a radio base station (20).

81. The node of claim 80, wherein: The node (30) is an evolved Node B or a New Radio Node B (32).

82. A node (30) configured to assist in state estimation of a user equipment (10) connected to a wireless communication network (1), in, The node (30) is configured to obtain measurement information (62) relating to a plurality of range-related measurements about at least one user equipment (10); The measurement information (62) comprises data of the range-related measurement, a time at which the range-related measurement was performed, an identity of the user equipment (10) and data enabling determination of a measurement location; The range-related measurement consists of data defining the distance between the user equipment (10) and the measurement location; wherein the node (30) is further configured to compile the measurement information (62) relating to a plurality of range-related measurements into compiled measurement information (64); wherein the node (30) is further configured to initiate communication of the compiled measurement information (64) to a state estimation node (60); and The state estimate of the user equipment (10) is used to classify the user equipment (10) as being associated with a drone.

83. The node according to claim 82, characterized in that The node (30) comprises a processor (241) and a memory (251), the memory (251) comprising instructions executable by the processor (241), whereby the processor is operable to perform the compilation of the measurement information (62).

84. A node according to claim 82 or 83, characterized in that The node (30) comprises a communication circuit (201) configured to initiate the communication of the compiled measurement information (64).

85. The node according to claim 82 or 83, characterized in that The node (30) is configured to perform the obtaining of measurement information (62) including receiving measurement information from another node.

86. The node according to claim 82 or 83, characterized in that The node (30) is configured to perform the obtaining of measurement information (62) including performing range-related measurements on the user equipment (10).

87. The node according to claim 82 or 83, characterized in that The obtained measurement information (62) includes measurement data, and the measurement data includes at least one of the following items: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

88. The node according to claim 82 or 83, characterized in that The compiled measurement information (64) includes measurement data, the measurement data including at least one of the following: a value of the distance between the user equipment (10) and the measurement location; a value of a one-way propagation time of a radio signal between the user equipment (10) and the measurement location or between the measurement location and the user equipment (10); a value of a round trip time of a radio signal traveling between the user equipment (10) and the measurement location; as well as A timestamp for transmitting a downlink signal, a timestamp for receiving an uplink signal triggered by the downlink signal, and a value of a round trip time delay in the user equipment (10).

89. The node according to claim 82 or 83, characterized in that The node (30) is a base station (20).

90. The node of claim 89, wherein: The node (30) is an evolved Node B or a New Radio Node B (32).

91. A wireless communication network (1) comprising at least one node (30) according to any one of claims 41 to 70 and at least one node (30) according to any one of claims 71 to 81.

92. The wireless communication network of claim 91, wherein: Further comprising at least one node (30) as claimed in any one of claims 82 to 90.

93. A computer readable medium having stored thereon a computer program (230, 231) comprising instructions which, when executed by at least one processor (241), cause the at least one processor (241) to obtain measurement information (62, 64) relating to range-related measurements about a user equipment (10), the measurement information (62, 64) comprising data of the range-related measurements, a time at which the range-related measurements were performed, an identity of the user equipment (10), and data enabling determination of a measurement location, the range-related measurements consisting of data defining a distance between the user equipment (10) and the measurement location, wherein When the instructions are executed by at least one processor (241), the at least one processor (241) performs a state estimation of the user device (10) based on the measurement information (62, 64), and wherein, when the instructions are executed by at least one processor (241), the at least one processor (241) classifies the user device (10) as being associated with a drone based on the state estimation of the user device (10).

94. A computer readable medium having stored thereon a computer program (230, 231) comprising instructions which, when executed by at least one processor (241), cause the at least one processor (241) to perform range-related measurements with respect to a user equipment (10), and wherein: The instructions, when executed by at least one processor (241), cause the at least one processor (241) to initiate the transmission of measurement information (62, 64) related to the range-related measurement to a state estimation node (60), the measurement information (62, 64) comprising data of the range-related measurement, a time at which the range-related measurement is performed, an identity of the user device (10) and data enabling determination of a measurement location, the range-related measurement consisting of data defining a distance between the user device (10) and the measurement location, wherein the measurement information (62, 64) is used for state estimation of the user device (10), and wherein the state estimation of the user device (10) is used for classifying the user device (10) as being associated with a drone.

95. A computer readable medium having stored thereon a computer program (230, 231) comprising instructions which, when executed by at least one processor (241), cause the at least one processor (241) to obtain measurement information (62) relating to a number of range-related measurements about at least one user equipment (10), the measurement information (62) comprising data of the range-related measurements, the time at which the range-related measurements were performed, the identity of the user equipment (10) and data enabling determination of a measurement location, the range-related measurements consisting of data defining a distance between the user equipment (10) and the measurement location, wherein The instructions, when executed by at least one processor (241), cause the at least one processor (241) to compile the measurement information (62) relating to a large number of range-related measurements into compiled measurement information (64), and wherein the instructions, when executed by at least one processor (241), cause the at least one processor (241) to initiate transmission of the compiled measurement information (64) to a state estimation node (60), wherein the compiled measurement information (64) is used for state estimation of the user device (10), and wherein the state estimation of the user device (10) is used to classify the user device (10) as being associated with a drone.

96. A computer program product (255) comprising a computer readable medium as claimed in any one of claims 93 to 95.

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