EP4677870A1

EP4677870A1 - Improvements to active localization in wireless networks

Info

Publication number: EP4677870A1
Application number: EP23712956.4A
Authority: EP
Inventors: Marcus Roland HENNINGER; Silvio MANDELLI; Leonardo Chiarello; Thorsten Wild
Original assignee: Nokia Solutions and Networks Oy
Current assignee: Nokia Solutions and Networks Oy
Priority date: 2023-03-07
Filing date: 2023-03-07
Publication date: 2026-01-14
Also published as: WO2024184674A1

Abstract

Systems, methods, apparatuses, and computer program products for detecting spoofed and/or untrustworthy active localization measures by sensing and leveraging sensing measurements to enhance overall positioning performance. One method may include a network entity determining an active localization estimate of a target device; defining an area of interest based on the active localization estimate; receiving from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest; and modifying the active localization estimate based on the passive localization estimate derived from the sensing information.

Description

IMPROVEMENTS TO ACTIVE LOCALIZATION IN WIRELESS NETWORKS TECHNICAL FIELD: [0001] Some example embodiments may generally relate to mobile or wireless telecommunication systems, such as 3^rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), 5^th generation (5G) radio access technology (RAT), new radio (NR) access technology, 6^th generation (6G), and/or other communications systems. For example, certain example embodiments may relate to systems and/or methods for detecting spoofed and/or untrustworthy active localization measures by sensing and leveraging sensing measurements to enhance overall positioning performance. BACKGROUND: [0002] Examples of mobile or wireless telecommunication systems may include radio frequency (RF) 5G RAT, the Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), LTE Evolved UTRAN (E-UTRAN), LTE-Advanced (LTE-A), LTE-A Pro, NR access technology, and/or MulteFire Alliance. 5G wireless systems refer to the next generation (NG) of radio systems and network architecture. A 5G system is typically built on a 5G NR, but a 5G (or NG) network may also be built on E-UTRA radio. NR may be able to support service categories such as enhanced mobile broadband (eMBB), ultra-reliable low-latency- communication (URLLC), and massive machine-type communication (mMTC). NR is expected to deliver extreme broadband, ultra-robust, low-latency connectivity, and massive networking to support the Internet of Things (IoT). The next generation radio access network (NG-RAN) represents the radio access network (RAN) for 5G, which may provide radio access for NR, LTE, and LTE-A. It is noted that the nodes in 5G providing radio access functionality to a user equipment (e.g., similar to the Node B in UTRAN or the Evolved Node B (eNB) in LTE) may be referred to as next-generation Node B (gNB) when built on NR radio, and may be referred to as next-generation eNB (NG-eNB) when built on E-UTRA radio. SUMMARY: [0003] In accordance with some example embodiments, a method may include determining, by a network entity, an active localization estimate of a target device. The method may further include defining, by the network entity, an area of interest based on the active localization estimate. The method may further include receiving, by the network entity, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The method may further include modifying, by the network entity, the active localization estimate based on the passive localization estimate derived from the sensing information. [0004] In accordance with certain example embodiments, an apparatus may include means for determining an active localization estimate of a target device. The apparatus may further include means for defining an area of interest based on the active localization estimate. The apparatus may further include means for receiving, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The apparatus may further include means for modifying the active localization estimate based on the passive localization estimate derived from the sensing information. [0005] In accordance with various example embodiments, a non-transitory computer readable medium may include program instructions that, when executed by an apparatus, cause the apparatus to perform at least a method. The method may include determining an active localization estimate of a target device. The method may further include defining an area of interest based on the active localization estimate. The method may further include receiving, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The method may further include modifying the active localization estimate based on the passive localization estimate derived from the sensing information. [0006] In accordance with some example embodiments, a computer program product may perform a method. The method may include determining an active localization estimate of a target device. The method may further include defining an area of interest based on the active localization estimate. The method may further include receiving, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The method may further include modifying the active localization estimate based on the passive localization estimate derived from the sensing information. [0007] In accordance with certain example embodiments, an apparatus may include at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to determine an active localization estimate of a target device. The at least one memory and instructions, when executed by the at least one processor, may further cause the apparatus at least to define an area of interest based on the active localization estimate. The at least one memory and instructions, when executed by the at least one processor, may further cause the apparatus at least to receive from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The at least one memory and instructions, when executed by the at least one processor, may further cause the apparatus at least to modify the active localization estimate based on the passive localization estimate derived from the sensing information. [0008] In accordance with various example embodiments, an apparatus may include determining circuitry configured to determine an active localization estimate of a target device. The apparatus may further include defining circuitry configured to define an area of interest based on the active localization estimate. The apparatus may further include receiving circuitry configured to receive, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest. The apparatus may further include modifying circuitry configured to modify the active localization estimate based on the passive localization estimate derived from the sensing information. BRIEF DESCRIPTION OF THE DRAWINGS: [0009] For a proper understanding of example embodiments, reference should be made to the accompanying drawings, wherein: [0010] FIG. 1 illustrates an example of a flow diagram depicting certain example embodiments; [0011] FIG. 2 illustrates an example of a flow diagram of a method according to some example embodiments; [0012] FIG. 3 illustrates an example of fusion of active and passive localization according to various example embodiments; [0013] FIG. 4 illustrates an example of an orthogonal frequency division multiplexing radar periodogram according to certain example embodiments; [0014] FIG. 5 illustrates an example of various network devices according to some example embodiments; and [0015] FIG. 6 illustrates an example network and system architecture according to various example embodiments. DETAILED DESCRIPTION: [0016] It will be readily understood that the components of certain example embodiments, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of some example embodiments of systems, methods, apparatuses, and computer program products for detecting spoofed and/or untrustworthy active localization measures by sensing and leveraging sensing measurements to enhance overall positioning performance is not intended to limit the scope of certain example embodiments, but is instead representative of selected example embodiments. [0017] Modern cellular networks, including, for example, the cellular networks implemented in fully-automated factories and smart homes, may be relied upon to provide accurate location information of one or more connected user devices, smart objects, smart inventory items, and so on. A variety of localization/positioning techniques may rely on one or more time of arrival (ToA), time difference of arrival (TDoA), and/or angle of arrival/departure (AoA/AoD) measurements. In view of the increased reliance on the location information provided by the network, the network may be configured to evaluate and track a positioning integrity (PI) parameter and indicate, based on a value of the PI parameter, whether a positioning system is able to provide accurate location measurements. [0018] Moreover, modern cellular networks may include sensing capabilities, such as, but not limited to, one or more sensing nodes or other devices supported by transceivers, processing and memory units, and other hardware and software functionality, that interconnect to form sensing networks capable of detecting environmental states and state changes, collecting and interpreting vast amounts of data for a myriad of applications in civil, military, commercial, industrial, household, and personal contexts. In some instances, the cellular networks equipped with the sensing capabilities may be configured to use, for example, reflection of radio signals within the environment to construct a digital model of that environment. The sensing networks (which may include cellular networks having sensing capabilities) may monitor and maintain a “confidence level” key performance indicator (KPI) that indicates a measure of confidence the network assigns to a currently available sensing data and/or a digital model rendered based on that sensing data. [0019] In some instances, information obtained using the positioning and sensing capabilities of a given network may be combined to provide increasingly accurate location information resilient to malicious intervention and subversion. In particular, sensing may aid the positioning capabilities of cellular networks by detecting spoofed location signals, such as a user device that alleges to be in a given position that does not correspond to its true physical location. In one example, sensing may provide additional measurements that may be used to improve the PI, and further refine the accuracy of the position estimates. [0020] As discussed herein, reliability and trustworthiness of active localization measures provided by a cellular network may be important and inaccuracies or failures in positioning systems may result in, for example, production outages in factories and/or may impact people’s health in safety-critical applications. The PI parameter may be indicative of a measure of trust in an accuracy of the position- related data provided by the positioning system and/or a measure of trust in the ability to provide timely and valid warnings to a location services (LCS) client when the positioning system cannot fulfill the conditions for intended operation. A protection level (PL) parameter may be a quantifiable measure of trust of a positioning accuracy and may generally indicate a statistical upper-bound of a position error (PE). In some instances, the PL parameter may be estimated based on measurements and other information. [0021] In an example, the PL and PE parameters may be supplemented with a predefined (e.g., application-specific) alert limit (AL) parameter, which may be a maximum allowable PE value corresponding to the positioning system being available for the intended application. Accordingly, a value of the PL parameter may be used to determine whether a positioning system is considered to be available (i.e., if PL is greater than AL) or unavailable (i.e., if PL is less than or equal to AL). It may be important to avoid situations where the positioning system may be wrongly declared available due to a poor PL estimation, i.e., instances when the computed PL is less than the AL, but the actual PL is greater than the AL. Such condition may lead to safety-critical situations, thus, a need exists to optimize the PL computation. [0022] The PI of a positioning system may be affected by spoofing attacks. Spoofing activity may include a malicious device that broadcasts signals to falsify or disguise its own actual physical location. As just some examples, a malicious device may falsify (or “spoof”) its own location to evade geofencing mechanisms, avoid tolls, and invade safety-critical areas (e.g., airport, military base). Thus, the detection and mitigation of spoofing attacks on positioning applications may be an integral part of making mobile networks more reliable and secure. In some instances, network sensing may assist in detecting instances when the active localization information of a given device is being spoofed (or falsified). [0023] Certain example embodiments described herein may have various benefits and/or advantages to overcome the disadvantages described herein. For example, certain example embodiments may enhance the overall positioning performance by refining the position estimate and enhancing the PI by improving the value of the PL, and detecting location spoofing attacks in mobile networks. Thus, certain example embodiments discussed herein are directed to improvements in computer-related technology. [0024] Some example embodiments may support interactions between positioning and sensing functionalities that leverage sensing as an additional measurement source to improve the accuracy, reliability, and/or integrity of the location/position computation in wireless networks. [0025] FIG. 1 illustrates an example flow diagram 100 of a method that may be performed by a network entity (NE) or UE, such as NE 510 or UE 520 illustrated in FIG. 5, according to various example embodiments described herein. In an example embodiment, NE 510 or UE 520 may include a location management function (LMF) configured to establish communication and/or exchange information with a sensing management function that, in turn, may be part of the NE 510 or UE 520 or embodied in a different node, module, controller, or entity or device. In another example embodiment, NE 510, UE 520, or the LMF incorporated/integrated therein, may include or be configured to establish communication with an active localization function or system. [0026] As illustrated at block 102, the active localization system may be configured to determine an active localization estimate. The active localization system, as illustrated, for example, at block 104, may determine an area of interest where a target object should be located. In some embodiments, the active localization system may be configured to identify an area of interest to be an area within a predefined distance, radius, perimeter, and so on, of the active localization estimate. [0027] In response to determining an active localization estimate and/or identifying an area of interest, the active localization system may be configured to perform one or more operations related to sensing information acquisition. In an example embodiment, the active localization system may be configured to, at block 106, establish communication with the sensing management function to query the sensing management function for information or data related to a digital twin rendering of the area of interest. In general, a digital twin of an object, system, or environment may describe one or more digital representations of that object, system, or environment in the physical world and may be generated using one or more sensing devices, nodes, or other components of the network. In an example, the active localization system may query the sensing management function to extract information about a predefined area of interest and to check whether an active position estimate of an object or device within that predefined area of interest is consistent with the digital representation of the predefined area of interest generated by the sensing management function. [0028] Additionally or alternatively, the active localization system, at block 108, may request the sensing management function to perform one or more refined scans in the area of interest. Generally, various operations for active localization and network sensing, as described in reference to FIG. 1 and throughout the this disclosure, may be performed sequentially, concurrently, independently, or otherwise in accordance with the present disclosure. [0029] Based on the information received from the sensing management function, the active localization system may be configured to, at block 110, perform one or more operations to determine a sensing scenario confidence. As just one example, sensing may or may not be suitable for the intended use of improving active positioning, even if fine-grained scans of the area of interest are available. [0030] In response to receiving sensing information, the active localization system may be configured to perform one or more operations to change, revise, refine, enhance, and/or improve accuracy of the active localization estimate. Generally, various operations related to active localization refinement or improvement, as described herein, may include determining a passive localization estimate based on sensing data related to the digital twin and/or based on refined scans or sensing measurements. [0031] For example, the active localization system may be configured to, at block 112, use the sensing information as an additional measurement to change, revise, refine, enhance, and/or improve the active localization estimate. Additionally or alternatively, the LMF, independently or in conjunction with the active localization system and/or a positioning integrity management function (IMF), may be configured to, at block 114, use the sensing information to perform integrity operations to improve positioning integrity (e.g., improve a value of the PL). Still further, the active localization system may be configured to, at block 116, compare the sensing information with active localization signals and measurements to determine whether a network entity is spoofing user equipment (UE) locations. Accordingly, the LMF and/or the positioning IMF may be configured to detect malicious attacks against positioning services and may initiate one or more procedures to mitigate them. [0032] FIG. 2 illustrates an example of a flow diagram of a method that may be performed by a network entity (NE) or UE, such as NE 510 or UE 520 illustrated in FIG.5, according to various example embodiments. [0033] It is noted that certain example embodiments described herein may be triggered and initiated in a variety of ways. For example, some sensing techniques may be triggered after each position update provided by the network. This may provide the highest capability and accuracy information since every location estimate may be checked for spoofing, and possibly refined by sensing measurements. In order to reduce unnecessary overhead, some sensing procedures may avoid performing complete scans over angles using many different beams and, instead, focus on tracking the active localization object of interest using only a few beams around the estimated position obtained from active localization as corresponding to the UE position. This may be quasi-located with an object of interest carrying the UE (e.g., pedestrian, automated guided vehicle (AGV), or drone), as discussed herein. [0034] Additionally or alternatively, some example embodiments may be triggered and initiated periodically, such as at time intervals that are short enough such that spoofing attacks may be detected without severe implications. These periodic corrections to the position estimate may have a reduced overhead as compared to continuous or very frequent corrections. Furthermore, various example embodiments may be triggered and initiated by specific events. For example, the specific events may be derived from localization KPIs that may suggest that a refinement of the position estimate is necessary (e.g., if a continuous increase of the PL is observed). In addition, the procedure may be triggered if a certain pre-assessment of the position estimate indicates a spoofed location (e.g., if the current position estimate is far away from previous positions). Additionally or alternatively, the sensing procedures described herein run periodically, continuously, on a schedule, or using some other cadence, including ad hoc, i.e., the sensing procedures may not rely on a triggering event in order to be initiated. [0035] At step 201, the method may include identifying a predefined area of interest to be sensed. For example, an active localization estimate of a target device may be determined. In some instances, the area of interest may encompass the object to be sensed and may be identified based on the KPIs of the positioning system. For example, the area of interest may be defined using an estimated three-dimensional standard deviation of the active localization position estimate ^^_^^ = which may indicate the positioning uncertainty. The area to be sensed may then roughly be set as an ellipsoid (or other geometry) around the position estimate that spans a predefined constant value multiplied by ^^_^^. In this manner, the set geometry may then be used to derive the required sensing measurements. Accordingly, beamforming may illuminate and, thus, assist in deriving the area of interest. [0036] At step 202, the method may include, in response to the area of interest having been determined, querying a sensing management function (or other entity handling sensing operations) for the required sensing information. It is noted that this information may be obtained using one or more of mono-static sensing operations, bi-static sensing operations in both uplink and downlink directions, and distributed/networked sensing. [0037] Certain example embodiments may include performing a consistency check using a digital twin. Mobile networks may generate, use, control, manage, and/or maintain digital twins of the environment. Thus, a sensing management function may be queried for information about the area of interest from the available digital representation of the surroundings. This digital twin information may then be leveraged to check whether the position estimate provided by the network is consistent with the environment. [0038] As an example, regions may be deduced from the digital twin environment representation in which a UE may be unlikely to be physically located and/or incapable of being physically located (e.g., above the ceiling (for indoor positioning) or inside/at the place of another (previously sensed) object), rendering a position estimate as likely being untrustworthy and/or spoofed. In addition, non-line of sight (NLoS)/line of sight (LoS) profiles may be extracted from the digital twin environment rendering. NLoS position estimates to transmit/receive points may be declared untrustworthy/spoofed since active localization relies mostly on LoS propagation. In response to at least one of the checks failing, the LMF may determine that the position estimate is untrustworthy, and the positioning system may be declared unavailable (e.g., the fused position estimate results in a new PL is greater than the old PL value and the new PL value is greater than the AL). The sensing procedure may then be terminated. [0039] Some example embodiments may include requesting refined sensing measurements, wherein a sensing management function may be queried for fine- grained sensing measurements of a predefined area of the estimated position of the device or object of interest). The LMF (or another function, application, or device of NE 510 or UE 520) may then use the received additional sensing measurement information to check whether the estimated position of the device or object of interest may be confirmed and/or refined. Accordingly, the sensing data may accurately model the environment, but may not provide a detailed image of the surroundings until a refined sensing measurements are requested. In an example, the LMF may be configured to request the refined sensing measurement in response to determining that the available sensing information about the area of interest is insufficient based on the sensing KPIs in that area of interest. The considered KPIs may include accuracy, resolution, signal-to-noise ratio (SNR), and any other end-task KPI for sensing performance evaluation. Accuracy and resolution may be any of spatial, range, and angular [0040] Various example embodiments may also include determining a sensing scenario confidence. Sensing may or may not be suitable for the intended use of improving active positioning, even if fine-grained scans of the area of interest are available. [0041] In some instances, sensing capabilities may be dictated by available hardware (e.g., number of antennas) and/or system parametrization (e.g., available bandwidth), and thus, must be taken into account and modeled accordingly. In addition, information about reflective properties, such as the radar cross-section (RCS) of the object carrying the device (e.g., human, AGV, etc.) may be important to consider to improve the accuracy of the capabilities’ modeling. [0042] In some instances, in cluttered environments (e.g., indoor scenarios), other (strong) reflectors may be close to the object of interest, making it more difficult to determine which reflection corresponds to the position estimate, especially if the reflective properties are not known. Moreover, objects in these cluttered environments may cause interference if the resolution of the network performing the sensing operations is insufficient. In some instances, the LMF may be configured to analyze multiple snapshots of the area of interest to leverage Doppler information to separate moving UEs from background clutter, such as, for example, two UEs (targets) positioned/located at the same range, but moving at different speeds, may be difficult to separate using range information alone and Doppler information may need to be used to be able to separate and see both UEs (targets). [0043] In certain environments these aforementioned factors may play only a limited role and/or be modeled well. In such instances, sensing measurements may be used to aid active position estimates. In one example, the sensing management function may be configured to generate a sensing scenario confidence (SSC) of the area of interest. In some examples, the SSC may be a continuous value between and including 0 and 1. In some other instances, the SCC may be implemented as a binary flag (e.g., “0” = scenario unsuitable, “1” = scenario suitable) that may indicate the usefulness of the area of interest, and may be computed based on the factors listed above. In another example, if the sensing scenario is declared unsuitable (e.g., by a binary flag), the sensing procedure may be terminated early. When expressed as a continuous value, the SSC may influence how much weight is given to the sensing measurements, as discussed herein. [0044] At step 203, the method may include enhancing and/or applying a modification to active localization. For example, active localization may be modified by refining the active localization estimate, improving the integrity of the positioning system, and/or detecting spoofing attacks. These techniques may be performed in any combination and any sequence, or concurrently, and may be triggered independently. [0045] Certain example embodiments may include using the sensing measurements to refine the active location estimate. For example, the LMF may be configured to determine a fused position estimate ^^_^^^^^ using maximum likelihood estimation by assuming the two positions to be independent measurements leveraging their (estimated) variances. This may be calculated according to ^^ wherein ^^_^^ may be the active localization estimate (i.e., the position estimate originally provided by the network), and ^^_^^ may be the passive localization estimate (i.e., the position estimate obtained with sensing). Moreover, the variance of the active position estimate ^^{^} ! _^^ may be said to correspond to the trace of the covariance of the active position estimate "^# _^^ (i.e., ^^{^} ! _^^ = Tr("^# _^^)) and the variance of the passive (or sensing) position estimate ^^{^} ! _^^ may be said to correspond to the trace of the covariance of the passive (or sensing) position estimate "^# _^^ (i.e., ^^{^} ! _^^ = Tr("^# _^^)). [0046] FIG.3 illustrates adjusting the active localization estimate in accordance with the present disclosure. In an example, the estimated variance of the sensed position estimate ^^_^^ may be greater than the estimated variance of the active localization estimate ^^_^^ such that there is only a slight correction, and the fused position estimate ^^_^^^^^ may be closer to the active localization position estimate ^^_^^. [0047] Alternatively, the LMF may be configured to use the covariance matrices "^# _^^ and "^# _^^ to determine the fused position estimate ^^_^^^^^. To fuse the covariances, the LMF may be configured to determine covariance intersection to obtain the fused covariance "^# _^^^^^ and the fused position estimate ^^_^^^^^ according to Equations (2) and (3), respectively, such that ^^_^^^^^ = "^# _^^^^^ where ' may be a weighting parameter that may be selected to minimize the trace of the fused covariance "^# _^^^^^. [0048] Some example embodiments may utilize sensing to enhance the integrity of the positioning system. Similar to refining active localization estimates ^^_^^, the passive localization estimate ^^_^^ may be considered as an additional measurement which may also be used to lower the PL. For instance, the variance of the fused position estimates ^^{^} ! _^^^^^ may be calculated according to Equation (4), such that ^^{^} ! _^^^^^ wherein the variance of the fused position estimates ^^{^} ! _^^^^^ may be less than the variance of the active position estimate ^^{^} ! _./ due to the availability of additional information provided by sensing. Accordingly, the PL may also be lowered, resulting in an increased integrity of the positioning system due to the higher availability. In addition, the covariance matrices "^# _^^ and "^# _^^ may be considered to obtain the fused covariance in matrix form using Equation (2). [0049] In various example embodiments, the sensing measurements may be used to detect spoofed active localization measures, for example, by applying orthogonal frequency division multiplexing (OFDM) radar principles. FIG. 4 illustrates a two- dimensional example of a range-azimuth periodogram of an example area of interest. However, the techniques described herein may be extended to three, four, or any other number of dimensions by considering aspects such as elevation angle and velocity, yielding a complete spatio-temporal image of the area of interest. [0050] As an example, performing sensing operations on the area of interest may include a binary decision regarding whether or not a target has been detected close to the estimated position. This may be illustrated with a signal detection problem, such as 0₁: ! = 3 + 4 0₎: ! = 4, where 0₁ and 0₎ may represent null and alternative hypothesis, respectively, ! may denote a received signal, 3 may denote an expected received signal, and 4 may denote noise. In contrast to some applications of the null hypothesis 0₁ as being representative of noise-only conditions (i.e., conditions where only noise is present), the null hypothesis 0₁ in Equation (5) may be adapted to correspond to a case where the target device is located inside the area of interest, such that the provided position estimate may be valid. [0051] In order to detect an untrustworthy/spoofed position estimate, the LMF may be configured to determine that the alternative hypothesis is true (i.e., there is only noise, corresponding to the case where no object is present in the scan area). Accordingly, the LMF may be configured to apply the Neyman-Pearson criterion to define a likelihood-ratio test according to Equation (6), such that wherein <(!|0₎- and <(!|0₁- may denote the likelihood functions of the respective hypotheses. Moreover, ; may denote the decision threshold chosen to maximize the probability of detection =_> (which may correspond to the probability of detecting a spoofed position estimate) for a desired probability of false alarm =_?^ based on the likelihood function of the null hypothesis <(!|0₁-. The LMF may be configured to apply the hypothesis test as provided, for example, Equations (5) and (6), to a discrete periodogram case to derive the threshold ;. In some instances, the LMF may derive the threshold ; by relying on a previously mentioned case where the null hypothesis 0₁ represents the noise-only case and using the likelihood function of the null hypothesis <(!|0₁-. Since, as provided herein, the alternative hypothesis represents the noise-only cases (i.e., cases where the position estimate spoofed), whereas typically, in signal detection problems, the null hypothesis 0₁ represents the noise-only case, the LMF may be configured to apply these principles by estimating the signal power ^^_@ ^A, which may depend, among other things, on the reflective properties of the object. [0052] In certain example embodiments, the LMF may be configured to determine the threshold ; based on =_> and <(!|0₎-. In an example, the probability B_>,CDE of declaring a single periodogram bin spoofed may be given as wherein Q may denote the absolute value of the periodogram bin in case only noise is present, and <_I(F|0₎- may be an associated probability density function. The random variable Q may denote the magnitude-squared of noise with power and hence, exponentially distributed. Thus, the LMF may determine the threshold ; according to Equation (8), such that ; = −^_E ^A lnT1 − =_>,CDEU. (8) In one example, the LMF may not determine that the position estimate is spoofed in response to only a single periodogram bin being less than the threshold. As another example, the LMF may be configured to determine that the position estimate is spoofed in response to each of the periodogram bins being less than the threshold. Accordingly, the probability =_> of determining that the position estimate is spoofed may be given as =_> = 1 − (1 − =_>,CDE-^V, (9) wherein W may denote the total number of periodogram bins. Thus, =_>,CDE then yields and the detection threshold ; may be determined according to Equation (11), such that ; = −^_E ^A ln^[(1 − =_>-^V]. (11) In some instances, the threshold ; may mainly depend on the estimated noise (plus clutter) power and the desired probability of detection and modelling the unknown signal power may not be necessary. [0053] Furthermore, other objects may be accounted for that are present in the area of interest when determining ;. For example, this could be achieved by raising for a subset of bins by also considering sidelobes generated by other known objects and clutter. [0054] Although the network sensing techniques above are described with respect to OFDM radar and a periodogram, other techniques may be used that do not utilize FDM radar and the periodogram. Frequency modulated continuous wave (FMCW) radar may be used as an alternative to OFDM radar. Alternatively or additionally, super-resolution methods (e.g., multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT)) may be used as an alternative to a periodogram, which may rely on impulsive scatter detection that does not pass through the periodogram. This may require different decision rules as the one previously presented, but the fundamental idea (i.e., sensing certain areas of interest to decide whether a position estimate is spoofed/untrustworthy) may remain the same. [0055] Certain example embodiments may prioritize detecting spoofing attacks on wireless networks. Such embodiments may include determining the area of interest based on the active localization position estimate, e.g., in accordance with step 201. The consistency check with the digital twin may then be performed at step 202, after which the position estimate may be determined or identified as being untrustworthy/spoofed in response to being inconsistent with the environment. In response to the consistency check not indicating that the position estimate is inconsistent with the environment, the LMF may be configured to request one or more refined sensing measurements. In response to determining the sensing scenario confidence of the area of interest, the LMF may use the sensing information to detect a possible spoofing attack at step 203. In an example, the LMF may be configured to perform a binary check of whether or not the active position estimate should be declared as being untrustworthy/spoofed. The LMF, in some instances, may not perform corrections on the position estimate the LMF identified as being untrustworthy. [0056] Alternatively, various example embodiments may prioritize improving active localization. Accordingly, the area of interest may be determined at step 201. Then, refined sensing measurements may be requested at step 202 and used to refine the active localization estimate to improve the integrity of the positioning system at step 203, respectively, if the SSC of interest is greater or less than a predefined threshold (e.g., satisfying a predetermined threshold). Such embodiments may target enhancing the overall performance of the positioning system. [0057] FIG. 5 illustrates an example of a system according to certain example embodiments. In one example embodiment, a system may include multiple devices, such as, for example, NE 510 and/or UE 520. [0058] NE 510 may be one or more of a base station (e.g., 3G UMTS NodeB, 4G LTE Evolved NodeB, or 5G NR Next Generation NodeB), a serving gateway, a server, and/or any other access node or combination thereof. [0059] NE 510 may further comprise at least one gNB-centralized unit (CU), which may be associated with at least one gNB-distributed unit (DU). The at least one gNB-CU and the at least one gNB-DU may be in communication via at least one F1 interface, at least one X_n-C interface, and/or at least one NG interface via a 5^th generation core (5GC). [0060] UE 520 may include one or more of a mobile device, such as a mobile phone, smart phone, personal digital assistant (PDA), tablet, or portable media player, digital camera, pocket video camera, video game console, navigation unit, such as a global positioning system (GPS) device, desktop or laptop computer, single-location device, such as a sensor or smart meter, or any combination thereof. Furthermore, NE 510 and/or UE 520 may be one or more of a citizens broadband radio service device (CBSD). [0061] NE 510 and/or UE 520 may include at least one processor, respectively indicated as 511 and 521. Processors 511 and 521 may be embodied by any computational or data processing device, such as a central processing unit (CPU), application specific integrated circuit (ASIC), or comparable device. The processors may be implemented as a single controller, or a plurality of controllers or processors. [0062] At least one memory may be provided in one or more of the devices, as indicated at 512 and 522. The memory may be fixed or removable. The memory may include computer program instructions or computer code contained therein. Memories 512 and 522 may independently be any suitable storage device, such as a non-transitory computer-readable medium. The term “non-transitory,” as used herein, may correspond to a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., random access memory (RAM) vs. read-only memory (ROM)). A hard disk drive (HDD), random access memory (RAM), flash memory, or other suitable memory may be used. The memories may be combined on a single integrated circuit as the processor, or may be separate from the one or more processors. Furthermore, the computer program instructions stored in the memory, and which may be processed by the processors, may be any suitable form of computer program code, for example, a compiled or interpreted computer program written in any suitable programming language. [0063] Processors 511 and 521, memories 512 and 522, and any subset thereof, may be configured to provide means corresponding to the various blocks of FIGS. 2-5. Although not shown, the devices may also include positioning hardware, such as GPS or micro electrical mechanical system (MEMS) hardware, which may be used to determine a location of the device. Other sensors are also permitted, and may be configured to determine location, elevation, velocity, orientation, and so forth, such as barometers, compasses, and the like. [0064] As shown in FIG.5, transceivers 513 and 523 may be provided, and one or more devices may also include at least one antenna, respectively illustrated as 514 and 524. The device may have many antennas, such as an array of antennas configured for multiple input multiple output (MIMO) communications, or multiple antennas for multiple RATs. Other configurations of these devices, for example, may be provided. Transceivers 513 and 523 may be a transmitter, a receiver, both a transmitter and a receiver, or a unit or device that may be configured both for transmission and reception. [0065] The memory and the computer program instructions may be configured, with the processor for the particular device, to cause a hardware apparatus, such as UE, to perform any of the processes described above (i.e., FIGS. 2-5). Therefore, in certain example embodiments, a non-transitory computer-readable medium may be encoded with computer instructions that, when executed in hardware, perform a process such as one of the processes described herein. Alternatively, certain example embodiments may be performed entirely in hardware. [0066] In certain example embodiments, an apparatus may include circuitry configured to perform any of the processes or functions illustrated in FIGS. 2-5. As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry), (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and/or digital hardware circuit(s) with software/firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions), and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation. This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device. [0067] FIG.6 illustrates an example of a 5G network and system architecture according to certain example embodiments. Shown are multiple network functions that may be implemented as software operating as part of a network device or dedicated hardware, as a network device itself or dedicated hardware, or as a virtual function operating as a network device or dedicated hardware. The NE and UE illustrated in FIG. Error! Reference source not found. may be similar to NE 510 and UE 520, respectively. The user plane function (UPF) may provide services such as intra-RAT and inter-RAT mobility, routing and forwarding of data packets, inspection of packets, user plane quality of service (QoS) processing, buffering of downlink packets, and/or triggering of downlink data notifications. The application function (AF) may primarily interface with the core network to facilitate application usage of traffic routing and interact with the policy framework. [0068] According to certain example embodiments, processors 511 and 521, and memories 512 and 522, may be included in or may form a part of processing circuitry or control circuitry. In addition, in some example embodiments, transceivers 513 and 523 may be included in or may form a part of transceiving circuitry. [0069] In some example embodiments, an apparatus (e.g., NE 510 and/or UE 520) may include means for performing a method, a process, or any of the variants discussed herein. Examples of the means may include one or more processors, memory, controllers, transmitters, receivers, and/or computer program code for causing the performance of the operations. [0070] In various example embodiments, apparatus 510 may be controlled by memory 512 and processor 511 to determine an active localization estimate of a target device; define an area of interest based on the active localization estimate; receive, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest; and modify the active localization estimate based on the passive localization estimate derived from the sensing information. [0071] Certain example embodiments may be directed to an apparatus that includes means for performing any of the methods described herein including, for example, means for determining an active localization estimate of a target device; means for defining an area of interest based on the active localization estimate; means for receiving, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest; and means for modifying the active localization estimate based on the passive localization estimate derived from the sensing information. [0072] The features, structures, or characteristics of example embodiments described throughout this specification may be combined in any suitable manner in one or more example embodiments. For example, the usage of the phrases “various embodiments,” “certain embodiments,” “some embodiments,” or other similar language throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with an example embodiment may be included in at least one example embodiment. Thus, appearances of the phrases “in various embodiments,” “in certain embodiments,” “in some embodiments,” or other similar language throughout this specification does not necessarily all refer to the same group of example embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. [0073] As used herein, “at least one of the following: <a list of two or more elements>” and “at least one of <a list of two or more elements>” and similar wording, where the list of two or more elements are joined by “and” or “or,” mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements. [0074] Additionally, if desired, the different functions or procedures discussed above may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the described functions or procedures may be optional or may be combined. As such, the description above should be considered as illustrative of the principles and teachings of certain example embodiments, and not in limitation thereof. [0075] One having ordinary skill in the art will readily understand that the example embodiments discussed above may be practiced with procedures in a different order, and/or with hardware elements in configurations which are different than those which are disclosed. Therefore, although some embodiments have been described based upon these example embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of the example embodiments.

Claims

WE CLAIM: 1. A method comprising: determining, by a network entity, an active localization estimate of a target device; defining, by the network entity, an area of interest based on the active localization estimate; receiving, by the network entity, from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest; and modifying, by the network entity, the active localization estimate based on the passive localization estimate derived from the sensing information. 2. The method of claim 1, wherein the receiving further comprises querying the sensing management function for the sensing information associated with the area of interest from an available digital representation of the area of interest and determining whether the active localization estimate is consistent with the available digital representation of the area of interest. 3. The method of claim 1, further comprising requesting the sensing management function to perform at least one refined scan in the area of interest according to at least one key performance indicator. 4. The method of claim 3, wherein the at least one key performance indicator includes one or more of accuracy, resolution, and signal-to-noise ratio, and wherein the one or more of accuracy and resolution are one of spatial, range, and angular. 5. The method of claim 1, wherein the modifying includes fusing the active localization estimate and the passive localization estimate to generate a modified localization estimate. 6. The method of claim 1, wherein the modifying includes at least one passive localization estimate of at least one additional measurement. 7. The method of claim 1, wherein the modifying includes determining whether a target has been detected within at least one of an area of interest or a predetermined distance from an initial location estimate. 8. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: determine an active localization estimate of a target device, define an area of interest based on the active localization estimate, receive from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest, and modify the active localization estimate based on the passive localization estimate derived from the sensing information. 9. The apparatus of claim 8, wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to: query the sensing management function for the sensing information associated with the area of interest from an available digital representation of the area of interest, and determine whether the active localization estimate is consistent with the available digital representation of the area of interest. 10. The apparatus of claim 8, wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to: request the sensing management function to perform at least one refined scan in the area of interest according to at least one key performance indicator. 11. The apparatus of claim 10, wherein the at least one key performance indicator includes one or more of accuracy, resolution, and signal-to-noise ratio, and wherein the one or more of accuracy and resolution are one of spatial, range, and angular. 12. The apparatus of claim 8, wherein the modifying includes fusing the active localization estimate and the passive localization estimate to generate a modified localization estimate. 13. The apparatus of claim 8, wherein the modifying includes at least one passive localization estimate of at least one additional measurement. 14. The apparatus of claim 8, wherein the modifying includes determining whether a target has been detected within at least one of an area of interest or a predetermined distance from an initial location estimate. 15. An apparatus comprising: means for determining an active localization estimate of a target device; means for defining an area of interest based on the active localization estimate; means for receiving from a sensing management function, a passive localization estimate based on sensing information associated with the area of interest; and means for modifying the active localization estimate based on the passive localization estimate derived from the sensing information. 16. The apparatus of claim 15, further comprising: means for querying the sensing management function for the sensing information associated with the area of interest from an available digital representation of the area of interest, and means for determining whether the active localization estimate is consistent with the available digital representation of the area of interest. 17. The apparatus of claim 15, further comprising: means for requesting the sensing management function to perform at least one refined scan in the area of interest according to at least one key performance indicator. 18. The apparatus of claim 17, wherein the at least one key performance indicator includes one or more of accuracy, resolution, and signal-to-noise ratio, and wherein the one or more of accuracy and resolution are one of spatial, range, and angular. 19. The apparatus of claim 15, wherein the modifying includes fusing the active localization estimate and the passive localization estimate to generate a modified localization estimate. 20. The apparatus of claim 15, wherein the modifying includes at least one passive localization estimate of at least one additional measurement. 21. The apparatus of claim 15, wherein the modifying includes determining whether a target has been detected within at least one of an area of interest or a predetermined distance from an initial location estimate. 22. A non-transitory computer readable medium comprising program instructions that, when executed by an apparatus, cause the apparatus to perform at least a method according to any of claims 1-7. 23. An apparatus comprising circuitry configured to perform a method according to any of claims 1-7. 24. A computer program comprising instructions, which, when executed by an apparatus, cause the apparatus to perform the method of any of claims 1-7.