Sensing and localization enhancements

Abstract

Example embodiments of the present disclosure relate to methods, apparatuses, and a computer readable storage medium for sensing and localization enhancements. In some embodiments, the present disclosure may include receiving, from a target entity, a responsive signal; determining a confidence value relating to the target entity based on the responsive signal; and transmitting, to a managing entity, an indication of the confidence value.

Description

SENSING AND LOCALIZATION ENHANCEMENTSFIELD:

[0001] Example embodiments of the present disclosure generally relate to the field of telecommunication, and in particular, to methods, apparatuses, and computer readable storage media for performing sensing and localization within a wireless network environment.BACKGROUND:

[0002] Examples of mobile or wireless telecommunication systems may include the Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), Long Term Evolution (LTE) Evolved UTRAN (E-UTRAN), LTE- Advanced (LTE- A), MulteFire, LTE-A Pro, Fifth Generation (5G) Radio Access Technology or New Radio (NR) Access Technology, Sixth Generation (6G) Radio Access Technology, and / or beyond.

[0003] With respect to 5G, network enhancements were driven with respect to latency, throughput, and spectral efficiency. With the advent of 6G and beyond, network enhancements may be pushed even further, so there is a need to facilitate gains in network performance.SUMMARY:

[0004] According to a first embodiment, a sensing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the sensing entity at least to receive a responsive signal, determine one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal, and transmit, to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier.

[0005] In a variant, the responsive signal may be responsive to a probing signal transmitted by the sensing entity or another sensing entity.

[0006] In a variant, the confidence value may relate to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists.

[0007] In a variant, the confidence value may be determined based on a received power of the responsive signal.

[0008] In a variant, the indication of the confidence value may be transmitted in an information element and indicative of a percentage value of likelihood.

[0009] According to a second embodiment, a managing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the managing entity at least to receive, from a first sensing entity, an indication of one or more of a first confidence value, a radial speed measurement, or a target identifier relating to at least one target entity, and perform a classification relating to the target entity based on one or more of the first confidence value, the radial speed measurement, the target identifier, or complementary information.

[0010] In a variant, the first confidence value may relate to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists.

[0011] In a variant, the indication of the first confidence value may be received in an information element and indicative of a percentage value of the likelihood.

[0012] In a variant, the complementary information may include one or more of a previously obtained confidence value of the target entity, or a confidence value relating to another target entity located proximate to the target entity.

[0013] In a variant, the managing entity may be further caused to classify information relating to the target entity as spurious when the first confidence value does not satisfy a lower confidence value threshold, and classify information relating to the target entity as confirmed when the first confidence value satisfies an upper confidence value threshold.

[0014] In a variant, the managing entity may be further caused to receive, from a second sensing entity, an indication of a second confidence value relating to the target entity, and perform the classification relating to the target entity based on the first confidence value and the second confidence value.

[0015] In a variant, the managing entity may be further caused to classify information relating to the target entity as spurious when an overall confidence value does not satisfy a lower confidence value threshold, and classify information relating to the target entity as confirmed when an overall confidence value satisfies an upper confidence value threshold. The overall confidence value may be derived based on the first confidence value and the second confidence value.

[0016] In a variant, the managing entity may be further caused to classify information relating to the target entity as spurious when each of the first confidence value and the second confidence value does not satisfy the lower confidence value threshold, and classify information relating to the target entity as confirmed when either of the first confidence value or the second confidence value satisfies the upper confidence value threshold.

[0017] According to a third embodiment, a method may include receiving, by a sensing entity, a responsive signal; determining, by the sensing entity, one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal; and transmitting, by the sensing entity and to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier.

[0018] According to a fourth embodiment, a method may include receiving, by a managing entity and from a first sensing entity, an indication of one or more of a first confidence value, a radial speed measurement, or a target identifier relating to at least one target entity; and performing, by the managing entity, a classification relating to the target entity based on one or more of the first confidence value, the radial speed measurement, or the target identifier.

[0019] According to a fifth embodiment, an apparatus may include means for receiving a responsive signal; means for determining one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal; and means for transmitting, to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier.

[0020] According to a sixth embodiment, a sensing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the sensing entity at least to receive a responsive signal, determine a radial speed measurement of at least one target entity based on the responsive signal, and transmit, to a managing entity, an indication of the radial speed measurement.

[0021] In a variant, the radial speed measurement may be determined based on one or more of a frequency shift or a phase shift in the responsive signal.

[0022] In a variant, the radial speed measurement may further include a quality of the radial speed measurement including one or more of an error value corresponding to an uncertainty ofthe radial speed measurement, a resolution of the error value, or a number of measurements used to determine the error value.

[0023] In a variant, the indication of the radial speed measurement may be transmitted in one or more information elements of a measurement report or a measurement response.

[0024] In a variant, the radial speed measurement may be associated with one or more of an identification of the target entity or another measurement related to the target entity.

[0025] In a variant, the sensing entity may be further caused to transmit, to the managing entity, a list of a plurality radial speed measurements corresponding to a plurality of target entities.

[0026] According to a seventh embodiment, a managing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the managing entity at least to receive, from a sensing entity, an indication of a radial speed measurement of at least one target entity, and perform a classification relating to the target entity based on the radial speed measurement.

[0027] In a variant, the radial speed measurement may further include a quality of the radial speed measurement including one or more of an error value corresponding to an uncertainty of the radial speed measurement, a resolution of the error value, or a number of measurements used to determine the error value.

[0028] In a variant, the indication of the radial speed measurement may be received in one or more information elements of a measurement report or a measurement response.

[0029] In a variant, the radial speed measurement may be associated with one or more of an identification of the target entity or another measurement related to the target entity.

[0030] In a variant, the managing entity may be further caused to receive, from the sensing entity, a list of a plurality radial speed measurements corresponding to a plurality of target entities.

[0031] In a variant, the managing entity may be further caused to, based at least partially on the radial speed measurement, classify information relating to the target entity as one or more of spurious, confirmed, active, passive, mobile, or static.

[0032] According to an eighth embodiment, a method may include receiving, by a sensing entity, a responsive signal; determining, by the sensing entity, a radial speed measurement ofat least one target entity based on the responsive signal; and transmitting, by the sensing entity and to a managing entity, an indication of the radial speed measurement.

[0033] According to a ninth embodiment, a method may include receiving, by a managing entity and from a sensing entity, an indication of a radial speed measurement of at least one target entity; and performing, by the managing entity, a classification relating to the target entity based on the radial speed measurement.

[0034] According to a tenth embodiment, an apparatus may include means for receiving a responsive signal; means for determining a radial speed measurement of at least one target entity based on the responsive signal; and means for transmitting, to a managing entity, an indication of the radial speed measurement.

[0035] According to an eleventh embodiment, a sensing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the sensing entity at least to receive a responsive signal, determine measurement information relating to at least one target entity based on the responsive signal, and transmit, to a managing entity, an indication of the measurement information and an identifier of the target entity.

[0036] In a variant, the responsive signal may be responsive to a probing signal transmitted by the sensing entity or another sensing entity.

[0037] In a variant, the measurement information may include one or more of a confidence value relating to an existence of the target entity, a confidence value relating to a path associated with the target entity, a radial speed measurement of the target entity, a quality of the radial speed measurement, a time of arrival measurement associated with the target entity, or an angle of arrival measurement associated with the target entity.

[0038] In a variant, the indication of the measurement information may be transmitted in one or more of a measurement report or a measurement response comprising a plurality of measurements related to the target entity.

[0039] In a variant, the measurement report or the measurement response may include the identifier of the target entity

[0040] In a variant, each of the plurality of measurements may include the identifier of the target entity.

[0041] According to a twelfth embodiment, a managing entity may include at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the managing entity at least to receive, from a sensing entity, an indication of measurement information relating to at least one target entity and an identifier of the target entity, and perform a classification relating to the target entity based on the measurement information and the identifier of the target entity.

[0042] In a variant, the measurement information may include one or more of a confidence value relating to an existence of the target entity, a confidence value relating to a path associated with the target entity, a radial speed measurement of the target entity, a quality of the radial speed measurement, a time of arrival measurement associated with the target entity, or an angle of arrival measurement associated with the target entity.

[0043] In a variant, the indication of the measurement information may be received in one or more of a measurement report or a measurement response comprising a plurality of measurements related to the target entity.

[0044] In a variant, the measurement report or the measurement may include the identifier of the target entity.

[0045] In a variant, each of the plurality of measurements may include the identifier of the target entity.

[0046] In a variant, the managing entity may be further caused to, based at least partially on the measurement information, classify information relating to the target entity as one or more of tentative, spurious, confirmed, active, passive, mobile, or static.

[0047] According to a thirteenth embodiment, a method may include receiving, by a sensing entity, a responsive signal; determining, by the sensing entity, measurement information relating to at least one target entity based on the responsive signal; and transmitting, by the sensing entity and to a managing entity, an indication of the measurement information and an identifier of the target entity.

[0048] According to a fourteenth embodiment, a method may include receiving, by a managing entity and from a sensing entity, an indication of measurement information relating to at least one target entity and an identifier of the target entity; and performing, by the managing entity, a classification relating to the target entity based on the measurement information and the identifier of the target entity.

[0049] According to a fifteenth embodiment, an apparatus may include means for receiving a responsive signal; means for determining measurement information relating to at least one target entity based on the responsive signal; and means for transmitting, to a managing entity, an indication of the measurement information and an identifier of the target entity.BRIEF DESCRIPTION OF THE DRAWINGS:

[0050] For proper understanding of example embodiments, reference should be made to the accompanying drawings, wherein:

[0051] FIG. 1 illustrates a signal diagram of an example related to sensing and localization enhancements according to some embodiments;

[0052] FIG. 2 illustrates a signal diagram of an example related to sensing and localization enhancements according to some embodiments;

[0053] FIG. 3 illustrates exemplary applications related to sensing and localization enhancements according to some embodiments;

[0054] FIGS. 4A-4B illustrate exemplary applications related to sensing and localization enhancements according to some embodiments;

[0055] FIG. 5 illustrates a signal diagram of an example related to sensing and localization enhancements according to some embodiments;

[0056] FIG. 6 illustrates exemplary applications related to sensing and localization enhancements according to some embodiments;

[0057] FIGS. 7A-7C illustrate exemplary applications related to sensing and localization enhancements according to some embodiments;

[0058] FIG. 8 illustrates exemplary applications related to sensing and localization enhancements according to some embodiments;

[0059] FIG. 9 illustrates a signal diagram of an example related to sensing and localization enhancements according to some embodiments;

[0060] FIG. 10 illustrates exemplary applications related to sensing and localization enhancements according to some embodiments;

[0061] FIG. 11 illustrates an example flow diagram of a method according to some embodiments;

[0062] FIG. 12 illustrates an example flow diagram of a method according to some embodiments;

[0063] FIG. 13 illustrates an example flow diagram of a method according to some embodiments;

[0064] FIG. 14 illustrates an example flow diagram of a method according to some embodiments;

[0065] FIG. 15 illustrates an example flow diagram of a method according to some embodiments;

[0066] FIG. 16 illustrates an example flow diagram of a method according to some embodiments; and

[0067] FIGS. 17A-17B illustrate example block diagrams of apparatuses according to an embodiment.DETAILED DESCRIPTION:

[0068] 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 sensing and localization enhancements is not intended to limit the scope of certain embodiments but is representative of selected example embodiments.

[0069] 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 “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 embodiment may be included in at least one embodiment. Thus, appearances of the phrases “in certain embodiments,” “in some embodiments,” “in other embodiments,” or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In addition, the phrase “set of’ refers to a set that includes one or more of the referenced set members. As such, the phrases “set of,” “one or more of,” and “at least one of,” or equivalent phrases, may be used interchangeably. Further, “or” is intended to mean “and / or,” unless explicitly stated otherwise.

[0070] Additionally, if desired, the different functions or operations discussed below may be performed in a different order and / or concurrently with each other. Furthermore, if desired, one or more of the described functions or operations may be optional or may be combined. As such, the following description should be considered as merely illustrative of the principles and teachings of certain example embodiments, and not in limitation thereof.

[0071] Wireless communications in the fifth generation (5G), sixth generation (6G), and beyond continue to evolve. In particular, there are continued interests and motivations for enabling and enhancing sensing techniques within wireless networks. Sensing is the ability of a wireless network to employ sensing capabilities of a variety of different sensors (e.g., including dedicated sensors, network devices with sensing functionalities, and / or the like) within a given environment. Joint Communications and Sensing (JCAS) is a particular technology of interest in future wireless communication systems (e.g., in 6G and beyond) that enables wireless networks to gather insightful real-world information about different objects of interest. Objects of interest may include user equipment (UE), base stations (e.g., next generation Node Bs (gNBs)), transmit and receive points (TRPs), access points, and / or other nodes or entities within a network environment. Sensing could further be extended to glean information about other objects and / or features within the network environment (e.g., walls, buildings, trees, animals, vehicles, clutter, and / or the like). The information gathered may be used by different services to provide localization of objects, classification of objects, material inferences, radar-like detection maps, network tomographies, and / or other representations of the given environment.

[0072] Current 3rd Generation Partnership Project (3GPP) standards specify active localization techniques that provide positioning information based on communication signals that are actively transmitted or received by network nodes within the environment. For example, active localization may rely on UEs which actively measure time of arrival (To A), angle of arrival (Ao A), etc. of known reference signals and / or actively transmit signals that are received by access points in order to position the UEs. Future generations of wireless networks, however, may need to further support not only such active localization techniques, but also passive sensing techniques e.g., based on signals passively reflected and / or scattered by objects within an environment. For example, a first network node may transmit probing signals, and a second network node (e.g., which may also be the first network node in some cases) may receive responsive signals (e.g., signals resulting from probing signals that have been scatteredby objects within the network environment). By studying such responsive signals, various types of information (e.g., a location of the object, dimensions of the object, a speed of the object, Doppler of the object, etc.) may be observed, measured, and / or derived.

[0073] Such techniques may be better served using distributed passive sensing within a network environment, e.g., by leveraging different network entities and sharing relevant information between the network entities to provide more spatial diversity and overall better quality of sensing and localization observations. For instance, multiple groups of sensing entities (e.g., Sensing Access Points (SAPs), and / or the like) may be used to probe and sense the same environment, and one or more managing entities (e.g., a Sensing Management Function (SeMF), a Location Management Function (LMF), and / or the like) may be used to further process or aggregate information obtained via the sensing entities. By leveraging a network of such sensing entities and managing entities, it may be possible to provide further insights into the network environment and result in overall improvements in terms of resolution of the sensed environment, localization accuracy of objects sensed in the environment, definition (e.g., shapes, dimensions, etc.) of objects sensed in the environment, characterization of objects sensed in the environment, ranging with respect to objects detected in the sensed environment, among other things.

[0074] As suggested above, a sensing entity may provide information characterizing a network environment including one or more objects therein. In some examples, the information may be used to learn the locations of the objects, the ranges of the objects, the shapes of the objects, the dimensions of the objects, the heights of the objects, the speed of the objects, the Doppler of the objects, and / or other characteristics of the objects sensed in the environment. Such information about the objects, or object information, may be used to provide a map, such as a virtual representation of the environment. Such a map may be in the form of a two-dimensional or a three-dimensional map, image, and / or some other representation of the objects sensed in the environment. The sensing may be considered distributed with respect to the transmission of probing signals and / or the reception of responsive signals being distributed among multiple sensing entities. Moreover, the sensing may be considered passive in the sense that the objects (e.g., scatterers) do not need to actively receive or transmit a probing signal or a responsive signal in order to obtain the object information.

[0075] As suggested above, a managing entity may aid to manage the sensing entities, aggregate the information obtained from the sensing entities, and / or further process the information obtained from the sensing entities to gain more insight into the network environment. In some examples, the managing entity may determine a set of scans needed from each sensing entity in order to determine object information, such as an image or some other representation of the sensed environment. In some examples, the managing entity may determine the quality of the sensing needed in order to provide a sufficient representation of the sensed environment and one or more objects sensed therein. In some cases, the managing entity may determine which sensing entities to configure for transmitting probing signals and / or receiving responsive signals, when to use the configured sensing entities, etc. to obtain the best quality scan. For example, the managing entity may configure a first sensing entity to perform a first scan of an area, and then configure a second sensing entity to perform a subsequent scan (e.g., using narrower beamwidth) of the area to provide a better resolution as compared to the first scan.

[0076] In JCAS, multiple network nodes may be leveraged as sensing entities to gather a variety of different sensing information, which can be centrally processed by the managing entity. Thus, different network nodes and entities can work together to provide processing gains. However, in order to realize such gains, the sensing information must first be transferred from the sensing entities to the managing entity in a feasible manner. One possible way would be to have the sensing entities share raw captured data, e.g., in-phase and quadrature component (IQ) samples, with the managing entity, but this poses additional challenges. Firstly, for larger bandwidths, which is typically needed for higher resolutions, the data rate of the raw IQ samples can easily exceed 100 Gbit / s (e.g., 8 antennas * 10-bit resolution * 800 MHz sample rate * 2 I+Q). Individual sensing entities would need to support such high data rates, but there are no feasible solutions for doing so. For instance, high data rate connections, such as fiber links, between sensing entities and managing entities could theoretically work. However, this would be costly to implement with base stations and not even a plausible option for mobile or wireless UEs. Secondly, managing entities may not be computationally suited to handle the processing of multiple high rates of data streams from different sensing entities.

[0077] Another possible way would be to have the individual sensing entities partially process the sensing information, and then share compressed sensing information to the managing entity for aggregation and further processing. Even then, there can be challenges.Full Channel State Information (CSI) or its derivatives, such as range-angle / Doppler periodogram images, and / or the like, might not be viable in a lot of cases with low bandwidth connected sensing entities. For example, for an update rate of 10 images / second (and beyond for non-static scenarios) an additional (net) data rate of ca. 500 Mbit / s (e.g., 10 * 128 beams * 8 carriers * 373 subcarriers * 64bit float * 21+Q) may be required in addition to existing traffic. Thus, data rate limitations still pose challenges even when sensing information is partially processed or compressed using currently available means, and the partially processed or compressed information may still lack the meaningfulness desired for enhanced sensing and localization. Accordingly, there is a need to enable sensing entities to extract and / or compress more meaningful information in a manner that not only preserves as much of the sensing information as possible, but also allows for such meaningful information to be shared with managing entities in a manner that reduces complexity, costs, and overhead.

[0078] Looking to current 3GPP standards to address such needs, there are only a few or limited ways to convey active localization measures from sensing entities to managing entities. The protocol between a UE and a LMF is the LTE Positioning Protocol (LPP), while the protocol between a gNB and a LMF is the New Radio Positioning Protocol A (NRPPa). Both cases allow spatial impulsive measures to be conveyed based on active localization and impulsive signal transmissions. The sensing entities (e.g., UEs and / or gNBs) can share lists with measured entries regarding ToA and / or AoA. For these, the received signal is processed to determine the strongest ToA and / or AoA (or lists thereof), corresponding to at least one impulsive path incoming at a receiver sensing entity. This allows compression of the full CSI matrix into a few bits of information elements (IE) and the ability to share the IE with the managing entity using ad-hoc procedures. The current 3GPP standards also provide parameters for sharing a quality of the respective measures. However, due to the highly compressed nature of such the quality measures, e.g., with a total of only 10 bits, current 3GPP standards allow only for a very coarse quality assessment of the respective measures.

[0079] In addition, the current 3GPP standards tend to focus too heavily on localization and overly rely on an assumption that an impulsive representation of a transmitted signal originated from a true target. It is thus difficult to adequately adapt the current standards to perform adequate sensing. In particular, sensing deals with detecting the effects of passive targets, that are or may be, spread in space (where there may be no impulsive behavior), difficult to distinguish in real scenarios with a large number of possibly passive targets, and unknown atthe transmitter and / or the receiver sensing entities. As mentioned above, the current standards which compress and convey information between sensing entities and managing entities, e.g., in a list of detected ToA and / or AoA measurements, exhibit some limitations. Firstly, unlike with localization operations, there is no certainty whether the detected ToA and / or AoA measurements correspond to a real target or not. Secondly, using a single measurement report, there is no way to indicate that multiple measurements belong to the same target. Thirdly, in contrast to localization which locates an active UE (a chipset), with sensing it is desired to sense the environment and objects therein, which may have very different physical properties and interactions with the network environment.

[0080] Accordingly, embodiments of the present disclosure provide solutions (e.g., improvements to the current standards) which enable enhanced sensing and localization measurements while addressing the needs noted above. Among other things, embodiments of the present disclosure enable additional information relating to a target entity to be exchanged between a sensing entity and a managing entity. As will be discussed in more detail further below, the additional information may include a confidence value, a radial speed measurement, and / or a target identifier. A confidence value (e.g., a detection confidence, a presence confidence, etc.) may correspond to a probability or a likelihood that a measurement related to a target entity corresponds to a true detection of the target entity (sensing) and / or a path thereto (localization). A radial speed measurement may correspond to a projected radial speed of a target entity as derived from a Doppler frequency shift. A target identifier may correspond to a unique identifier that can be used to associate measurements or other derivations relating to a target entity with the target entity. By enabling such additional information to be obtained, the present disclosure enables more meaningful information to be exchanged between sensing entities and managing entities in a manner that reduces complexity, costs, and overhead.

[0081] Referring now to FIG. 1, one example process 100 for enhancing sensing and localization measurement reports in cellular standards is illustrated. As shown, the process may involve one or more sensing entities 102 and one or more managing entities 104. In some examples, a sensing entity 102 may correspond to a SAP that includes, is included in, and / or otherwise implemented via a UE, a base station (e.g., a gNB), a distributed unit (DU), a TRP, and / or another access point, network node, network entity, or device that is configured to sense objects within a network environment. In some examples, a managing entity 104 may include, be included in, and / or otherwise implemented via a SeMF, a LMF, and / or another networknode, network entity, core network function, or device that is configured to manage, cause, and / or enable sensing and localization measurement reports as disclosed herein. Although the process 100 is depicted in FIG. 1 using three sensing entities 102 and one managing entity 104, it will be understood by persons of skill in the art that other quantities of sensing entities 102 and / or managing entities 104 may be used in similar manners to achieve similar outcomes.

[0082] In the example of FIG. 1, the managing entity 104 may manage the transmission and / or the reception of signals used for scanning a network environment. The managing entity 104 may enable a first set of sensing entities 102 to transmit probing signals, and enable a second set of sensing entities 102 to receive responsive signals. In some examples, some or all of the sensing entities 102 may or may not be shared between the first and second sets of sensing entities 102. The responsive signals may include unscattered probing signals and / or signals resulting from probing signals that have been scattered by objects within the network environment. Although described as probing signals and responsive signals, such signals may be referred to as other types of signals. For example, a probing signal and / or a responsive signal may also be referred to as, or correspond to, a sensing signal, a Positioning Reference Signal (PRS), a Channel State Information Reference Signal (CSI-RS), and / or another type of signal that may be used for sensing and localization measurement reports. In some examples, the probing signal and / or the responsive signal may be a predefined or structured signal, such as a control signal, a user data signal, and / or the like.

[0083] As shown in FIG. 1, at 106, the managing entity 104 may transmit a configuration to request or configure one or more of the sensing entities 102 to scan the network environment. For example, the configuration may configure at least one transmitter sensing entity 102 to transmit a probing signal and / or configure at least one receiver sensing entity 102 to receive a responsive signal. In some examples, the managing entity 104 may configure multiple transmitter sensing entities 102 to transmit probing signals and / or configure multiple receiver sensing entities 102 to receive responsive signals. When multiple transmitter sensing entities 102 are configured, the transmitter sensing entities 102 may share a common probing signal transmission pattern, or have different probing signal transmission patterns (e.g., different parameters with respect to frequency, time, bandwidth, beamwidth, etc.). In some examples, the configuration may be transmitted via a LPP to sensing entities 102 that are implemented via UEs, or transmitted via NRPPa to sensing entities 102 that are implemented via UEs orgNBs. In other examples, other types of protocols or messages may be used at 106 to configure the sensing entities 102.

[0084] In some example embodiments, the configuration may indicate information that may be used to transmit the probing signals and / or to receive the responsive signals e.g., information relating to frame, subframe, offset, beamforming, bandwidth, burst size, duration, resource elements, resource allocations, and / or other information to be used for transmitting probing signals and / or receiving responsive signals. In some example embodiments, the configuration may enable and / or include additional parameters or information that may be used by receiver sensing entities 102, e.g., information related to a confidence value, a radial speed measurement, a target identifier, and / or the like. As will be discussed in more detail further below, such additional parameters or information may be used to further enhance sensing and localization measurement reports. Additionally or alternatively, one or more of the receiver sensing entities 102 may have already been preconfigured or enabled with such additional parameters or information. In such cases, the managing entity 104 may not need to enable and / or include such additional parameters or information in the configuration transmitted at 106 to the sensing entities 102.

[0085] As shown in FIG. 1, at 108, the sensing entities 102 may transmit a response to the managing entity 104 for accepting or rejecting the configuration. At 110, the sensing entities 102 accepting the configuration may begin scanning the network environment. For example, transmitter sensing entities 102 may begin transmitting the probing signals, and receiver sensing entities 102 may begin monitoring for or receiving the responsive signals. In particular, if a target entity exists within the network environment or an area therein, one or more of the probing signals may be scattered by the target entity and received by the receiver sensing entities 102 as responsive signals. Based on the received responsive signals, the receiver sensing entities 102 may be able to determine or extract a set of object information (e.g., measurement information, sensing information, and / or the like) relating to the target entity (e.g., ToA, AoA, Doppler, channel estimations, range, and / or the like). According to embodiments of the present disclosure, the receiver sensing entities 102 may further determine or extract additional information associated with the target entity, e.g., a confidence value, a radial speed measurement, a target identifier, and / or the like, that can be used for enhanced sensing and localization, as will be discussed in more detail further below. In some examples,the response at 108 may be omitted e.g., in cases where a measurement response or a measurement report may be transmitted in lieu of the response.

[0086] As further shown in FIG. 1, at 112, the sensing entities 102 may transmit a measurement response, a measurement report, and / or the like including the object information, the additional information, and / or any other relevant information determined at 110 to the managing entity 104. The measurement report may be transmitted over LPP (e.g., in the case the sensing entity 102 is a UE) or over NRPPa (e.g., in the case the sensing entity 102 is a UE or a gNB). In some examples, the measurement report may further include scan information (e.g., a scan identifier, a scan timestamp, a measured and / or an estimated scan path, etc.) for associating the measurement report with the specific scan performed. As mentioned above, in some examples, the measurement response or measurement report at 112 may be transmitted in lieu of the response at 108. At 114, the managing entity 104 may receive the measurement report, and further process the information to gain a better understanding of the network environment and any sensed target entities therein. In cases where the managing entity 104 receives measurement reports from multiple sensing entities 102, the managing entity 104 may further aggregate the obtained information prior to and / or after processing the information. As will be discussed in more detail further below, based on the received measurement reports, the managing entity 104 may at 116 further perform one or more classifications relating to the network environment and / or the target entities therein.

[0087] Referring now to FIG. 2, one example process 200 for enhancing sensing and localization measurement reports using additional information, such as a confidence value, is provided. Similar to the embodiment of FIG. 1 , the managing entity 204 at 206 of FIG. 2 may transmit a configuration to request or configure one or more of the sensing entities 202 to scan the network environment. At 208, the sensing entities 202 may transmit a response to the managing entity 204 for accepting or rejecting the configuration. At 210, the sensing entities 202 may begin scanning the network environment to determine object information to be reported to the managing entity 204. Similar to the embodiment of FIG. 1, the object information may include measurement information, sensing information, and / or the like relating to a target entity. In addition to the embodiment of FIG. 1 , the sensing entities 202 at 210 may further determine a confidence value. The confidence value, e.g., a detection confidence, a presence confidence, etc. may correspond to a likelihood that the target entity exists and / or a likelihood that a path associated with the target entity exists. For instance, theconfidence value may indicate that measurements related to the target entity corresponds to a true detection of the target entity (sensing) and / or a path thereto (localization).

[0088] In some example embodiments, the sensing entities 202 may derive the confidence value based on responsive signals received from the target entity. For example, the confidence value for a target entity may be determined based at least partially on a received power of the responsive signal received from the target entity or from an area where the target entity is estimated to be (e.g., defined in intervals of range and / or angle from the sensing entity 202 acting as a sensing receiver, defined in terms of absolute position, and / or the like). In some examples, the confidence value may be determined using an estimation algorithm, e.g., based on backscattered power in a periodogram, that may optionally be compared against noise power. For example, a sensing entity 202 (e.g., a sensing receiver) may apply a Constant_False_Alarm_Rate (CFAR) method and determine the confidence value based on a detected peak power P of a sensed signal at specific range, Doppler, angle of arrival (e.g., azimuth angle of arrival and zenith angle of arrival), in relation to a noise floor power sigma squared c as shown by expression (1) below.In case of multiple snapshots, the sensing receiver may concatenate multiple such values by fusing, e.g., exponential smoothing, to better determine the confidence value. At 212, the sensing entities 202 may transmit to the managing entity 204 a measurement report including an indication of the confidence value along with the object information. In some example embodiments, the indication of the confidence value may be transmitted in one or more IES (e.g., referred to as a DetectionConfidence, a PresenceConfidence, a LocationConfidence, and / or the like) and indicative of a percentage value, a ratio, a fraction, and / or some other indication of likelihood (e.g., PD) that the target entity exists and / or a likelihood that a path associated with the target entity exists. Moreover, similar to the embodiment of FIG. 1, the measurement report may be transmitted via LPP or NRPPa e.g., depending on the type of sensing entity 202 sending the measurement report.

[0089] In some example embodiments, LPP procedures (e.g., via a ProvideLocationlnformation or some other LPP message) may be extended to incorporate the confidence value IE as part of the common LPP message IEs (e.g.,CommonlEsProvideLocationlnformation). As shown in the example table 300 of FIG. 3, the confidence value IE may be introduced as a new Detection Confidence IE that is representedas an integer ranging between 0 and 100 corresponding to a likelihood that a measurement result of a target entity is due to an actual physical target entity. In this example, a value of 0 may indicate a spurious detection and a value of 100 may indicate a confirmed detection. In some other example embodiments, the confidence value IE may be incorporated as part of Observed Time Difference of Arrival (OTDOA) Location Information in LTE (e.g., OTDOA- SignalMeasurementlnformatiori), as part of Downlink Time Difference of Arrival (DL-TDOA) Location Information in NR (e.g., NR-DL-TDOA-ProvideLocationlnformatiori), as part of Downlink Angle of Departure (DL-AoD) Location Information in NR (e.g., NR-DL-AoD- ProvideLocationlnformation), as part of Multi-Round Trip Time (Multi-RTT) Location Information (e.g., NR-Multi-RTT-ProvideLocationlnformation) in NR, and / or the like.

[0090] In some example embodiments, NRPPa procedures for reporting object information (e.g., ToA and / or AoA measurements, etc.) may be extended to incorporate the confidence value IE. For example, the confidence value IE may be mandatorily or optionally introduced as an additional IE and / or incorporated into an existing IE. As shown in the example tables 400 of FIGS. 4A-4B, the confidence value IE may be introduced as a Detection Confidence IE that is represented as an integer ranging between 0 and 100 corresponding to a likelihood (e.g., in percentage value) that a measurement result is due to an actual physical target entity. A value of 0 may indicate a spurious detection and a value of 100 may indicate a confirmed detection. As further shown in the example tables 400 of FIGS. 4A-4B, the confidence value IE may also be incorporated as part of a TRP Measurement Result IE, an Uplink (UL) Angle of Arrival IE, an UL Relative Time of Arrival (RTOA) Measurement IE, Multiple UL-AoA IE, etc. For the UL RTOA Measurement IE, the Detection Confidence IE may be added in the Additional Path List and / or the Extended Additional Path List per reported path. For the Multiple UL-AoA IE, the Detection Confidence IE may be added per reported angle.

[0091] Referring back to FIG. 2, at 214, the managing entity 204 may receive the measurement report including the indication of the confidence value along with the object information, and at 216 perform one or more classifications relating to the network environment and / or target entities detected therein based on the confidence value, the object information, etc. In some examples, the managing entity 204 may classify a target entity as spurious if the confidence value does not satisfy a confidence value threshold, and classify the target entity as confirmed if the confidence value satisfies the confidence value threshold. Forexample, the managing entity 204 may employ a comparison as shown by expression (2) below:PD< TP(2) where PD may correspond to the confidence value, and Tp may correspond to the confidence value threshold which may be approximately 0.2 or any other suitable value. In some examples, the confidence threshold value may be varied depending on a given task or use case, and used to adaptively discard certain target entities. In blockage prediction, for instance, a target entity that is barely detectable or visible may be considered to be of low interest (compared to other stronger scatterers) if the confidence value PD is less than the confidence value threshold Tp and discarded.

[0092] In some examples, the managing entity 204 may fuse information received from multiple sensing entities 202. For example, the managing entity 204 may receive a first indication of a first confidence value relating to a target entity from a first sensing entity 202, and receive a second indication of a second confidence value relating to the same target entity from a second sensing entity 202. In such cases, the managing entity 204 may perform a classification based on a comparison between an overall confidence value (e.g., derived based on a fusion of the first and second confidence values) and a confidence value threshold. For example, the overall confidence value may be determined based on expression (3) below: i - (n =op(i - ) (3) where #SAP may correspond to a number of the sensing entities 202 indicating confidence values, and PD may correspond to the confidence value indicated by each of the sensing entities 202. The managing entity 204 may perform a comparison as shown by expression (4) below: i - (nLsop(i - )> rw where Tp may correspond to the confidence value threshold, which may be approximately 0.5 or any other suitable threshold value. The target entity may be classified as spurious if expression (4) is not satisfied, or classified as confirmed if expression (4) is satisfied.

[0093] In further example embodiments, the managing entity 204 may use more than one confidence value threshold (e.g., an upper confidence value threshold and a lower confidence value threshold) in order to classify a target entity. For example, the managing entity 204 may classify a target entity as spurious if a confidence value does not satisfy a lower confidence value threshold, and classify the target entity as confirmed if the confidence value satisfies an upper confidence value threshold, where the upper confidence value threshold is different fromand greater than the lower confidence value threshold. When the confidence value is a fused or overall confidence value derived from multiple confidence values received from multiple sensing entities 202, the managing entity 204 may similarly classify the target entity as spurious if the overall confidence value does not satisfy a lower confidence value threshold, and classify the target entity as confirmed if an overall confidence value satisfies an upper confidence value threshold. In a related example, the managing entity 204 may classify the target entity as spurious if neither the first confidence value nor the second confidence value satisfies the lower confidence value threshold, and classify the target entity as confirmed if either the first confidence value or the second confidence value satisfies the upper confidence value threshold.

[0094] In still further examples, classifications performed by a managing entity 204 may be at least partially influenced by complementary information. Complementary information may include one or more previously obtained information (e.g., confidence value), classifications, or assessments related to the same target entity. For example, if a current confidence value suggests a relatively weak or low likelihood of existence of a target entity in a given area, and a previously obtained confidence value suggests none or a rare presence with a low likelihood of such a target entity (e.g., occurring only once, a few times, or otherwise sparsely), the managing entity 204 may conclude that there is an overall weak or low likelihood of such a target entity. Correspondingly, if a current confidence value suggests a relatively weak or low likelihood of existence of such a target entity, but a previously obtained confidence value suggests constant, almost constant, or otherwise persistent presence of such a target entity (e.g., occurring several times and / or consistently), the managing entity 204 may conclude that there is an overall strong or high likelihood of an existing target entity. In other examples, the complementary information may include a current or a previously obtained confidence value related to another target entity that may be located proximate to the target entity or otherwise situated in the same area or vicinity of the target entity. In such a manner, the managing entity 204 may iteratively continue to update previous classifications or assessments relating to a given target entity based on additional confidence values received from sensing entities 202.

[0095] Referring now to FIG. 5, one example process 500 for enhancing sensing and localization measurement reports using additional information, such as a radial speed measurement, is provided. Similar to previous embodiments, the managing entity 504 at 506 of FIG. 5 may transmit a configuration to request or configure one or more of the sensing entities 502 to scan the network environment. At 508, the sensing entities 502 may transmit aresponse to the managing entity 504 for accepting or rejecting the configuration. At 510, the sensing entities 502 may begin scanning the network environment to determine object information to be reported to the managing entity 504. Similar to previous embodiments, the object information may include measurement information, sensing information, and / or the like relating to a target entity. In contrast to previous embodiments, the sensing entities 502 at 510 may further determine a radial speed measurement of the target entity. The radial speed measurement may be used in addition to or in conjunction with other object information (e.g., ToA and / or AoA measurements, etc.) and / or other additional information (e.g., a confidence value as shown in the example embodiment of FIG. 2).

[0096] In some example embodiments, the sensing entities 502 may derive the radial speed measurement based on a frequency shift and / or a phase shift in the responsive signals received from the target entity. For example, the radial speed measurement of the target entity may be deduced from Doppler frequency shifts in the responsive signals leading to a constant phase shift over time. While an absolute speed of the target entity may be unknown, Doppler can be used to project a radial speed component of the target entity onto the respective responsive signal. In some examples, the radial speed measurement may further include or indicate a quality of the radial speed measurement, e.g., an error value corresponding to an uncertainty of the radial speed measurement, a resolution of the error value, a number of measurements used to determine the error value, etc. At 512, the sensing entities 502 may transmit to the managing entity 504 a measurement report including an indication of the radial speed measurement along with other object information and / or additional information. In some examples, the sensing entities 502 may transmit to the managing entity 504 a list of a plurality of radial speed measurements corresponding to a plurality of target entities.

[0097] In some embodiments, the indication of the radial speed measurement may be communicated to the managing entity 504 using new signaling, an extension of existing signaling, new IES, an extension of existing IES, and / or the like. As shown for example in tables 600 of FIG. 6, a radial speed measurement quality (e.g., MeasQuality) may be introduced according to LPP procedures defining one or more fields (e.g., error-Value, error-Resolution, error-NumSamples, etc.). For instance, an error-Value may correspond to an estimate of an uncertainty of the radial speed measurement, an error-Resolution may correspond to a resolution used for the error-Value, and an error-NumSamples may correspond to a number of measurements used to determine the error-Value. The error-Resolution may be encoded usingone or more bits (e.g., 2 bits) to indicate resolution R in terms of radial speed (e.g., 1 m / s, 3 m / s, etc.). The error-Value may be encoded using one or more bits (e.g., 4 bits) to indicate uncertainty as a range of radial speed (e.g., 0-2 m / s, 3-5 m / s, etc. when the R is 3 m / s). The error-NumSamples may be encoded using one or more bits (e.g., 3 bits) to indicate the number of samples or ranges thereof used for determining the error-Value. The error-NumSamples may further indicate whether the uncertainty (e.g., error- Value) was estimated based on a baseline metric or other measurements (e.g., signal-to-noise ratio, signal strength, etc.).

[0098] In some example embodiments, a fixed resolution (e.g., of 1 km / h, etc.) may be applied for the radial speed measurement. In related embodiments, a fixed resolution may additionally or alternatively be applied for determining a quality or an uncertainty of the radial speed measurement. As further shown in the example tables 600 of FIG. 6, an IE (e.g., RadialSpeedWithUncertainty , and / or the like) corresponding to the quality or the uncertainty of the radial speed measurement may be introduced according to LPP procedures. In some related embodiments, a positive radial speed measurement value may indicate that a radial component of a velocity of a target entity points away from an antenna location of the sensing entity 502 (e.g., a TRP) measuring and reporting the radial speed of the target entity, while a negative radial speed measurement value may indicate that the radial component of the velocity of the target entity points toward the antenna location of the sensing entity 502 measuring and reporting the radial speed of the target entity. In other example embodiments, a different IE, an existing IE, or a combination of different IES may be introduced or extended to indicate the radial speed measurements and / or a quality metric thereof according to LPP procedures.

[0099] In some embodiments, the indication of the radial speed measurement may be similarly communicated to the managing entity 504 according to NRPPa procedures. As shown for example in tables 700 of FIGS. 7A-7C, one or more dedicated IEs may be introduced for indicating radial speed measurements and / or quality metrics thereof in a NRPPa message (e.g., a measurement response, a measurement report, etc.) to the managing entity 504. For example, a radial speed measurement (e.g., Radial Speed IE) may indicate a velocity component in a radial direction relative to the sensing entity 502 (e.g., TRP). The Radial Speed IE and / or an uncertainty thereof (e.g., Speed Uncertainty) may be expressed in units of m / s, km / h, etc. Positive radial speed measurement values may indicate that the target entity is moving away from the sensing entity 502, while negative radial speed measurement values may indicate that the target entity is moving toward the sensing entity 502. In other examples,a radial speed measurement quality (e.g., Speed Measurement Quality IE) may be indicated instead of the uncertainty. Similar to the embodiments of FIG. 6, the Speed Measurement Quality IE as shown in FIGS. 7A-7C may indicate a Speed Error Resolution, a Speed Error Value, a Speed Error NumSamples, and / or the like.

[0100] As further shown in FIGS. 7A-7C, one or more IES may be extended to indicate the radial speed measurements or quality metrics thereof. For example, a TRP Measurement Result IE, a UL RTOA Measurement IE, an Additional Path List IE, an Extended Additional Path List IE, and / or the like may be extended to include the radial speed measurement. In some example embodiments, multiple radial speed measurements (e.g., a list of velocity components in radial direction away from the sensing entity 502) may be indicated (e.g., one per corresponding UL-AoA) in an additional IE (e.g., Multiple Radial Speed IE). For example, the Multiple Radial Speed IE may indicate a list of radial speed measurements (e.g., Radial Speed List IE) including each radial speed measurement (e.g., Radial Speed IE) and the corresponding target entity measured (e.g., Radial Speed Item IE). The Multiple Radial Speed IE may be indicated if the number of radial speed measurements is the same as the number of UL-AoA measurements obtained. In still further example embodiments, a Radial Speed IE may be added to each Relative Path Delay that is reported by the sensing entities 502 (e.g., via the Additional Path List IE and / or the Extended Additional Path list IE).

[0101] In some example embodiments, new measurement reports and / or extensions of existing measurement reports may be used to include the radial speed measurement and / or a quality metric thereof in addition to a confidence value and any other object information measured by the sensing entities 502. For example, such measurement reports may be defined specifically for reporting sensed passive target objects, and include relative ToA measurements, Ao A measurements, radial speed measurements, and confidence values. As shown in the example tables of FIG. 8, a Sensing Target Measurement IE may be defined to indicate a path through which a responsive signal is received from a passive target entity (e.g., ToA, Azimuth AoA, Zenith AoA, Local Coordinate System (LCS) to Global Coordinate System (GCS) Translation, etc.) in addition to radial speed measurements (e.g., Radial Speed) and confidence values (e.g., Detection Confidence). As further shown in FIG. 8, the Sensing Target Measurement IE may be indicated via a measurement report (e.g., TRP Measurement Result IE). In other examples, one or more other IEs may be introduced and / or extended toindicate different combinations of radial speed measurements, confidence values, and / or the like to the managing entity 504.

[0102] Referring back to FIG. 5, at 514, the managing entity 504 may receive the measurement report including the indication of the radial speed measurement, and at 516 perform one or more classifications relating to the network environment and / or target entities detected therein at least partially based on the radial speed measurements. Moreover, the managing entity 504 may use the radial speed measurements to classify information and / or measurements relating to a sensed target entity as one or more of spurious, confirmed, active, passive, mobile, static, and / or the like. For example, based on the radial speed measurements and / or the quality metrics thereof, the managing entity 504 may be able to distinguish between moving target entities from static target entities or clutters. The managing entity 504 may also be able to leverage the radial speed measurements and / or corresponding quality metrics for security and / or convenience applications in which movement of target entities has to be detected by motion detectors. The managing entity 504 may further use the radial speed measurements and / or the quality metrics for improved tracking of moving target entities. The managing entity 504 may also use any other available object information and / or additional information (e.g., confidence values, and / or the like) in combination with the radial speed measurements to better classify sensed target entities.

[0103] Referring now to FIG. 9, one example process 900 for enhancing sensing and localization measurement reports using additional information, such as a target identifier, is provided. Similar to previous embodiments, the managing entity 904 at 906 of FIG. 9 may transmit a configuration to request or configure one or more of the sensing entities 902 to scan the network environment. At 908, the sensing entities 902 may transmit a response to the managing entity 904 for accepting or rejecting the configuration. At 910, the sensing entities 902 may begin scanning the network environment for any target entities. Similar to previous embodiments, the sensing entities 902 may determine object information (e.g., ToA and / or AoA measurements, etc.) and any additional information (e.g., confidence values, radial speed measurements, and / or the like) relating to a sensed target entity. In contrast to previous embodiments, the sensing entities 902 at 910 may further determine a target identifier for a sensed target entity. The target identifier may be used in conjunction with other object information and / or other additional information in order to better track target entities.

[0104] In some examples, a target identifier may be used to associate multiple measurement reports with a single target entity of interest. In such a way, measurement reports associated with a single target entity may be grouped using a target identifier. The target identifier may be unique to the target entity and unique to / within the measurement reports or messages associated therewith. Correspondingly, measurement reports associated with different target entities may be distinguished by different target identifiers. In that sense, target identifiers may be omitted or not needed in cases where only a single measurement report is available per target entity, e.g., when there is nothing to group. In some further examples, it may be possible to have globally or locally target identifiers that are configured within one or more network entities or across different network entities. For example, a global target identifier may be unique to a target entity across multiple messages received by different network entities (e.g., different gNBs) within a given RAN, while a local target identifier may be unique to the target entity across multiple messages received by a single network entity (e.g., a gNB). Such globally or locally unique target identifiers may be used to associate measurement reports with a single target entity irrespective of the number of measurement reports available for the target entity and / or irrespective of the sensing entity 902 providing the measurement reports. For example, based on the globally or locally unique target identifier, other sensing entities 902 and / or a managing entity 904 may be able to identify which of the measurement reports belong to which target entity.

[0105] As further shown in FIG. 9, at 914, the managing entity 904 may receive a measurement report relating to a sensed target entity. As in previous embodiments, the measurement report may include object information (e.g., ToA and / or AoA measurements, etc.) and any additional information (e.g., confidence values, radial speed measurements, etc.) relating to the target entity. The measurement report may further include the target identifier uniquely identifying the target entity. For example, the managing entity 904 may receive a plurality of measurement reports from a single sensing entity 902, and the plurality of measurement reports may be grouped or associated with a single target identifier to indicate that the measurement reports relate to a single target entity. In some examples, the managing entity 904 may receive multiple sets of measurement reports, where each set of measurement reports indicates a target identifier associated with a different target entity. In some examples, the target identifier may be determined by the sensing entities 902 and communicated to the managing entity 904 as part of or separately from the measurement reports. In other examples,the target identifier may be omitted from the measurement reports and determined by the managing entity 904 based on the measurement reports or other information.

[0106] In some example embodiments, a new IE may be introduced or an existing IE may be extended to indicate the target identifier. As shown in the example tables 1000 of FIG. 10, an IE may be used for the target identifier (e.g., Target ID). The Target ID may be encoded as an integer value or some other value suited to distinguish between a plurality of different target entities. Presence of the Target ID in a measurement report may indicate some association between measurements within the measurement report and the specific target entity upon which the measurement report is based. Absence of the Target ID form a measurement report may indicate that no known association exists between measurements within the measurement report and a specific target entity. As mentioned above, different measurement reports from a single sensing entity 902 may include different Target IDs to distinguish between several target entities sensed by the sensing entity 902 and corresponding groups of measurement reports. In some further embodiments, e.g., if globally or locally target identifiers are supported, different measurement reports from different sensing entities 902 may include the same Target ID to indicate that the same target entity was sensed by the different sensing entities 902. Other variations may further be possible and will be apparent to those of skill in the art.

[0107] As shown in FIG. 9, at 916, the managing entity 904 may perform a classification relating to the target entity based on the measurement report and the target identifier. Moreover, the managing entity 904 may use the object information (e.g., ToA and / or AoA measurements, etc.) and any additional information (e.g., confidence values, radial speed measurements, etc.) provided in the measurement report to classify the target entity as one or more of spurious, confirmed, active, passive, mobile, static, and / or the like, as referenced in the previous embodiments. The managing entity 904 may further associate each target identifier with each classification performed. For example, a first Target ID of a first target entity may be classified as spurious, while a second Target ID of a second target entity may be classified as confirmed. Accordingly, the managing entity 904 and / or the sensing entities 902 may be able to discard measurement reports related to the first Target ID and / or continue to collect measurement reports related to the second Target ID. Furthermore, classifications and corresponding target identifiers may be recorded, stored in memory, and / or communicated between different network nodes or entities to further facilitate sensing and localization efforts.

[0108] With reference to FIGS. 1, 2, 5, and 9 above, the Configuration Request (e.g., at 106, 206, 506, 906), the Response to Configuration Request (e.g., at 108, 208, 508, 905), and / or the Measurement Response / Report (e.g., at 112, 212, 512, 912) are general representations and may vary according to different implementations, protocols, etc. For instance, to configure a sensing entity 102 (e.g., a TRP or a SAP) as a sensing transmitter via NRPPa, the Configuration Request may be a PRS Configuration Request, and the Response to Configuration Request may be a PRS Configuration Response. To configure a sensing entity 102 (e.g., a UE) as a sensing transmitter via NRPPa, the Configuration Request may be a Positioning Activation Request to the gNB serving the UE, causing a subsequent RRCReconfiguration via Radio Resource Control (RRC) from the gNB to the UE, and the Response to Configuration Request may be a Positioning Activation Response via NRPPa from the gNB serving the UE after receiving a RRCReconfigurationComplete message from the UE. Further, to configure a sensing entity 102 (e.g., a TRP or a SAP) as a sensing receiver via NRPPa, the Configuration Request may be a Measurement Request, and the Response to Configuration Request may be a Measurement Response (e.g., or a set of Measurement Responses if periodic measurements have been requested). To configure a sensing entity 102 (e.g., a UE) as a sensing receiver via LPP, the Configuration Request may be a RequestLocationlnformation message, and the Response to Configuration Request may be a ProvideLocationlnformation message (e.g., or a set of ProvideLocationlnformation messages if periodic measurements have been requested).

[0109] It will be understood that further variants to the above are possible without departing from the scope of the appended claims. For example, other naming schemes, terminologies, message types, etc. may be used for the Configuration Request, the Response to Configuration Request, and / or the Measurement Response / Report illustrated in FIGS. 1, 2, 5, and 9 depending on the protocol used (e.g., LPP, NRPPa, or other future variants of such protocols). In some cases, one or more of the messages may be omitted, combined, or otherwise altered depending on the implementation or protocol used. For instance, as also noted above, a separate Response to Configuration Request may be omitted where the Measurement Response / Report (e.g., ProvideLocationlnformation via LPP) may be used in lieu of the Response to Configuration Request. In still further possible variants, the Measurement Response / Report may include the Response to Configuration Request. Nonetheless, in each case, the Measurement Response / Report may logically follow the scanning of the environment (e.g., ScanEnvironment at 110, 210, 510, 910) and also include the aforenoted confidence value, radial speed measurement, target identifier, and / or the like.

[0110] It will further be understood that any combination of the foregoing embodiments may be used. For example, in some embodiments, the environment scan may be performed in conjunction with determining any combination of the confidence value, the radial speed measurement, the target identifier, and / or other information. Similarly, the Measurement Response / Report may also include any combination of the confidence value, the radial speed measurement, the target identifier, and / or other information. Further, the classifications of the target entities and / or the network environment may be performed based on any combination of the confidence value, the radial speed measurement, the target identifier, and / or other information. In some examples, joint measurements (e.g., a combination of ToA measurements, AoA measurements, speed measurements, and / or other information) may be determined per path and used for classifications. In such cases, a confidence value may be added once per path to represent a detection confidence for the joint measurements. In further examples, a target identifier may also be used in conjunction with other measurements or information to associate such measurements or information with a target entity.

[0111] Referring now to FIG. 11, an example flow diagram of a method 1100, according to some embodiments of the present disclosure, is provided. For example, FIG. 11 may illustrate example operations of an apparatus serving as a sensing entity (e.g., sensing entities 202). Some of the operations illustrated in FIG. 11 may be similar to some operations shown in and described with respect to FIG. 2.

[0112] In an embodiment, the method 1100 may include, at 1102, receiving a responsive signal. The method 1100 may include, at 1104, determining one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal. The method 1100 may include, at 1106, transmitting, to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier. The responsive signal may be responsive to a probing signal transmitted by the sensing entity or another sensing entity. The confidence value may relate to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists. The confidence value may be determined based on a received power of the responsive signal. The indication of the confidence value may be transmitted in an information element and indicative of a percentage value of likelihood.

[0113] Referring now to FIG. 12, an example flow diagram of a method 1200, according to some embodiments of the present disclosure, is provided. For example, FIG. 12 may illustrate example operations of an apparatus serving as a managing entity (e.g., managing entity 204). Some of the operations illustrated in FIG. 12 may be similar to some operations shown in and described with respect to FIG. 2.

[0114] In an embodiment, the method 1200 may include, at 1202, receiving, from a first sensing entity, a first indication of one or more of a first confidence value, a radial speed measurement, or a target identifier relating to at least one target entity. The method 1200 may include, at 1204, performing a classification relating to the target entity based on one or more of the first confidence value, the radial speed measurement, the target identifier, or complementary information. The first confidence value may relate to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists. The first indication of the first confidence value may be received in an information element and indicative of a percentage value of the likelihood. The complementary information may include one or more of a previously obtained confidence value relating to the target entity, or a current or a previously obtained confidence value relating to another target entity located proximate to the target entity. The method 1200 may further include classifying information relating to the target entity as spurious when the first confidence value does not satisfy a lower confidence value threshold, and classifying information relating to the target entity as confirmed when the first confidence value satisfies an upper confidence value threshold.

[0115] In some examples, the method 1200 may further include receiving, from a second sensing entity, a second indication of a second confidence value relating to the target entity, and performing the classification relating to the target entity based on the first confidence value and the second confidence value. The method 1200 may further include classifying information relating to the target entity as spurious when an overall confidence value does not satisfy a lower confidence value threshold, classifying information relating to the target entity as confirmed when an overall confidence value satisfies an upper confidence value threshold. The overall confidence value may be derived based on the first confidence value and the second confidence value. The method 1200 may further include classifying information relating to the target entity as spurious when each of the first confidence value and the second confidence value does not satisfy the lower confidence value threshold, and classifying informationrelating to the target entity as confirmed when either of the first confidence value or the second confidence value satisfies the upper confidence value threshold.

[0116] Referring now to FIG. 13, an example flow diagram of a method 1300, according to some embodiments of the present disclosure, is provided. For example, FIG. 13 may illustrate example operations of an apparatus serving as a sensing entity (e.g., sensing entities 502). Some of the operations illustrated in FIG. 13 may be similar to some operations shown in and described with respect to FIG. 5.

[0117] In an embodiment, the method 1300 may include, at 1302, receiving a responsive signal. The method 1300 may include, at 1304, determining a radial speed measurement of at least one target entity based on the responsive signal. The method 1300 may include, at 1306, transmitting, to a managing entity, an indication of the radial speed measurement. The radial speed measurement may be determined based on one or more of a frequency shift or a phase shift in the responsive signal. The radial speed measurement may further include a quality of the radial speed measurement including one or more of an error value corresponding to an uncertainty of the radial speed measurement, a resolution of the error value, or a number of measurements used to determine the error value. The indication of the radial speed measurement may be transmitted in one or more information elements of a measurement report or a measurement response. The radial speed measurement may be associated with one or more of an identification of the target entity or another measurement related to the target entity. The method 1300 may further include transmitting, to the managing entity, a list of a plurality radial speed measurements corresponding to a plurality of target entities.

[0118] Referring now to FIG. 14, an example flow diagram of a method 1400, according to some embodiments of the present disclosure, is provided. For example, FIG. 14 may illustrate example operations of an apparatus serving as a managing entity (e.g., managing entity 504). Some of the operations illustrated in FIG. 14 may be similar to some operations shown in and described with respect to FIG. 5.

[0119] In an embodiment, the method 1400 may include, at 1402, receiving, from a sensing entity, an indication of a radial speed measurement of at least one target entity. The method 1400 may include, at 1404, performing a classification relating to the target entity based on the radial speed measurement. The radial speed measurement may further include a quality of the radial speed measurement including one or more of an error value corresponding to an uncertainty of the radial speed measurement, a resolution of the error value, or a number ofmeasurements used to determine the error value. The indication of the radial speed measurement may be received in one or more information elements of a measurement report or a measurement response. The radial speed measurement may be associated with one or more of an identification of the target entity or another measurement related to the target entity. The method 1400 may include receiving, from the sensing entity, a list of a plurality radial speed measurements corresponding to a plurality of target entities. The method 1400 may further include, based at least partially on the radial speed measurement, classifying information relating to the target entity as one or more of spurious, confirmed, active, passive, mobile, or static.

[0120] Referring now to FIG. 15, an example flow diagram of a method 1500, according to some embodiments of the present disclosure, is provided. For example, FIG. 15 may illustrate example operations of an apparatus serving as a sensing entity (e.g., sensing entities 902). Some of the operations illustrated in FIG. 15 may be similar to some operations shown in and described with respect to FIG. 9.

[0121] In an embodiment, the method 1500 may include, at 1502, receiving a responsive signal. The method 1500 may include, at 1504, determining measurement information relating to at least one target entity based on the responsive signal. The method 1500 may include, at 1506, transmitting, to a managing entity, an indication of the measurement information and an identifier of the target entity. The responsive signal may be responsive to a probing signal transmitted by the sensing entity or another sensing entity. The measurement information may include one or more of a confidence value relating to an existence of the target entity, a confidence value relating to a path associated with the target entity, a radial speed measurement of the target entity, a quality of the radial speed measurement, a time of arrival measurement associated with the target entity, or an angle of arrival measurement associated with the target entity. The indication of the measurement information may be transmitted in one or more of a measurement report or a measurement response comprising a plurality of measurements related to the target entity. The measurement report or the measurement response may include the identifier of the target entity. Each of the plurality of measurements may include the identifier of the target entity

[0122] Referring now to FIG. 16, an example flow diagram of a method 1600, according to some embodiments of the present disclosure, is provided. For example, FIG. 16 may illustrate example operations of an apparatus serving as a managing entity (e.g., managing entity 904).Some of the operations illustrated in FIG. 16 may be similar to some operations shown in and described with respect to FIG. 9.

[0123] In an embodiment, the method 1600 may include, at 1602, receiving, from a sensing entity, an indication of measurement information relating to at least one target entity and an identifier of the target entity. The method 1600 may include, at 1604, performing a classification relating to the target entity based on the measurement information and the identifier of the target entity. The measurement information may include one or more of a confidence value relating to an existence of the target entity, a confidence value relating to a path associated with the target entity, a radial speed measurement of the target entity, a quality of the radial speed measurement, a time of arrival measurement associated with the target entity, or an angle of arrival measurement associated with the target entity. In some examples, the indication of the measurement information may be received in one or more of a measurement report or a measurement response comprising a plurality of measurements related to the target entity. The measurement report or the measurement may include the identifier of the target entity. Each of the plurality of measurements may include the identifier of the target entity. The method 1600 may further include, based at least partially on the first measurement information, classifying information relating to the target entity as one or more of tentative, spurious, confirmed, active, passive, mobile, or static.

[0124] Although the foregoing examples may depict the sensing entities as performing scans (e.g., transmitting probing signals, receiving responsive signals, etc.), and the managing entities as performing classifications, it will be understood that these examples are not exhaustive demonstrations of the present disclosure. In some cases, the roles of the sensing entities and the managing entities may be at least partially swapped and / or shared. For example, some sensing entities may be configured to at least partially perform some classifications, and possibly also share such classification results with managing entities. Similarly, some managing entities may be configured to at least partially perform scans, and possibly also share such scan results with sensing entities and / or other managing entities. Still further arrangements, combinations, or alternatives to the foregoing embodiments may be feasible and apparent to those of ordinary skill in the art to achieve comparable results.

[0125] Based on the foregoing, it may be possible to extract and / or compress more meaningful information in a manner that not only preserves as much of the sensing information as possible, but also allows for such meaningful information to be shared across networkentities in a manner that reduces complexity, costs, and overhead. Without in any way limiting the scope, interpretation, or application of the claims appearing below, technical effects of the example embodiments disclosed herein may include increased network performance due to reduced overhead and better knowledge regarding the network environment including the presence of target objects or entities.

[0126] FIG. 17A illustrates an example of an apparatus 1700 according to an embodiment. For example, the apparatus 1700 may be configured or caused to operate as a sensing entity. In some embodiments, the apparatus 1700 may be a node or element in a communications network or associated with such a network, such as a UE, communication node, mobile equipment, mobile station, mobile device, stationary device, loT device, or other device. As described herein, a UE may alternatively be referred to as a mobile station, mobile equipment, mobile unit, mobile device, user device, subscriber station, wireless terminal, tablet, smart phone, loT device, sensor or NB-IoT device, a watch or other wearable, a vehicle, a drone, a medical device, an industrial device, a consumer electronics device, or the like. In some embodiments, the apparatus 1700 may be a node, host, or server in a communications network or serving such a network, such as a network node, a sensing node, a satellite, a base station, a Node B, an evolved Node B (eNB), 5G Node B or access point, next generation Node B (NG- NB or gNB), and / or a WLAN access point, associated with a radio access network, such as a LTE network, 5G, or NR.

[0127] In some example embodiments, the apparatus 1700 may include one or more processors, one or more computer-readable storage medium (for example, memory, storage, or the like), one or more radio access components (for example, a modem, a transceiver, or the like), and / or a user interface. In some embodiments, the apparatus 1700 may be configured to operate using one or more radio access technologies, such as GSM, LTE, LTE-A, NR, 5G, WLAN, WiFi, NB-IoT, Bluetooth, NFC, MulteFire, and / or any other radio access technologies. It should be noted that one of ordinary skill in the art would understand that the apparatus 1700 may include components or features not shown in FIG. 17 A.

[0128] As illustrated in the example of FIG. 17A, the apparatus 1700 may include or be coupled to a processor 1702 for processing information and executing instructions or operations. The processor 1702 may be any type of general or specific purpose processor. In fact, the processor 1702 may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmablegate arrays (FPGAs), application- specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples. While a single processor 1702 is shown in FIG. 17A, multiple processors may be utilized according to other embodiments. For example, it should be understood that, in certain embodiments, the apparatus 1700 may include two or more processors that may form a multiprocessor system (e.g., in this case the processor 1702 may represent a multiprocessor) that may support multiprocessing. In certain embodiments, the multiprocessor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster).

[0129] The processor 1702 may perform functions associated with the operation of the apparatus 1700 including, as some examples, precoding of antenna gain / phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the apparatus 1700, including processes related to management of communication resources.

[0130] The apparatus 1700 may further include or be coupled to a memory 1704 (internal or external), which may be coupled to the processor 1702, for storing information and instructions that may be executed by the processor 1702. The memory 1704 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and / or removable memory. For example, the memory 1704 can be comprised of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (HDD), or any other type of non-transitory machine or computer readable media. The instructions stored in the memory 1704 may include program instructions or computer program code that, when executed by the processor 1702, enable the apparatus 1700 to perform tasks as described herein.

[0131] In an embodiment, the apparatus 1700 may further include or be coupled to (internal or external) a drive or port that is configured to accept and read an external computer readable storage medium, such as an optical disc, USB drive, flash drive, or any other storage medium. For example, the external computer readable storage medium may store a computer program or software for execution by the processor 1702 and / or the apparatus 1700.

[0132] In some embodiments, the apparatus 1700 may also include or be coupled to one or more antennas 1706 for receiving a downlink signal and for transmitting via an uplink from the apparatus 1700. The apparatus 1700 may further include a transceiver 1708 configured to transmit and receive information. The transceiver 1708 may also include a radio interface (e.g., a modem) coupled to the antenna 1706. The radio interface may correspond to a plurality of radio access technologies including one or more of GSM, LTE, LTE-A, 5G, NR, WLAN, NB- loT, Bluetooth, BT-LE, NFC, RFID, UWB, and the like. The radio interface may include other components, such as filters, converters (for example, digital-to-analog converters and the like), symbol demappers, signal shaping components, an Inverse Fast Fourier Transform (IFFT) module, and the like, to process symbols, such as OFDMA symbols, carried by a downlink or an uplink.

[0133] For instance, the transceiver 1708 may be configured to modulate information on to a carrier waveform for transmission by the antenna(s) 1706 and demodulate information received via the antenna(s) 1706 for further processing by other elements of the apparatus 1700. In other embodiments, the transceiver 1708 may be capable of transmitting and receiving signals or data directly. Additionally or alternatively, in some embodiments, the apparatus 1700 may include an input and / or output device (I / O device). In certain embodiments, the apparatus 1700 may further include a user interface, such as a graphical user interface or touchscreen.

[0134] In an embodiment, the memory 1704 stores software modules that provide functionality when executed by the processor 1702. The modules may include, for example, an operating system that provides operating system functionality for the apparatus 1700. The memory 1704 may also store one or more functional modules, such as an application or program, to provide additional functionality for the apparatus 1700. The components of the apparatus 1700 may be implemented in hardware, or as any suitable combination of hardware and software. According to an example embodiment, the apparatus 1700 may optionally be configured to communicate with one or more other apparatuses via a wireless or wired communications link according to any radio access technology, such as NR.

[0135] According to some embodiments, the processor 1702 and the memory 1704 may be included in or may form a part of processing circuitry or control circuitry. In addition, in some embodiments, the transceiver 1708 may be included in or may form a part of transceiving circuitry.

[0136] As used herein, the term “circuitry” may refer to hardware-only circuitry implementations (e.g., analog and / or digital circuitry), combinations of hardware circuits and software, combinations of analog and / or digital hardware circuits with software / firmware, any portions of hardware processor(s) with software (including digital signal processors) that work together to cause an apparatus (e.g., apparatus 1700) to perform various functions, and / or hardware circuit(s) and / or processor(s), or portions thereof, that use software for operation but where the software may not be present when it is not needed for operation. As a further example, as used herein, the term “circuitry” may also cover an implementation of merely a hardware circuit or processor (or multiple processors), or portion of a hardware circuit or processor, and its accompanying software and / or firmware. The term circuitry may also cover, for example, a baseband integrated circuit in a server, cellular network node or device, or other computing or network device.

[0137] According to certain embodiments, the apparatus 1700 may be controlled by the memory 1704 and the processor 1702 to perform the functions associated with any of the embodiments described herein, such as some operations illustrated in, or described with respect to sensing entities 102, FIGS. 1, 2, 5, and 9. For instance, the apparatus 1700 may be controlled by the memory 1704 and the processor 1702 to perform the methods of FIGS. 11, 13, and 15.

[0138] FIG. 17B illustrates an example of an apparatus 1710 according to another embodiment. For example, the apparatus 1710 may be configured or caused to operate as a managing entity. In an embodiment, the apparatus 1710 may be a node, host, or server in a communications network or serving such a network. For example, apparatus 1710 may be a network node, a sensing node, satellite, base station, a Node B, an evolved Node B (eNB), 5G Node B or access point, next generation Node B (NG-NB or gNB), and / or a WLAN access point, associated with a radio access network, such as a LTE network, 5G or NR.

[0139] It should be understood that, in some example embodiments, the apparatus 1710 may be comprised of an edge cloud server as a distributed computing system where the server and the radio node may be stand-alone apparatuses communicating with each other via a radio path or via a wired connection, or they may be located in a same entity communicating via a wired connection. For instance, in certain example embodiments where the apparatus 1710 represents a gNB, it may be configured in a central unit (CU) and distributed unit (DU) architecture that divides the gNB functionality. In such an architecture, the CU may be a logical node that includes gNB functions such as transfer of user data, mobility control, radio accessnetwork sharing, positioning, and / or session management, etc. The CU may control the operation of DU(s) over a front-haul interface. The DU may be a logical node that includes a subset of the gNB functions, depending on the functional split option. It should be noted that one of ordinary skill in the art would understand that the apparatus 1710 may include components or features not shown in FIG. 17B.

[0140] As illustrated in the example of FIG. 17B, the apparatus 1710 may include a processor 1712 for processing information and executing instructions or operations. The processor 1712 may be any type of general or specific purpose processor. In fact, the processor 1712 may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples. While a single processor 1712 is shown in FIG. 17B, multiple processors may be utilized according to other embodiments. For example, it should be understood that, in certain embodiments, the apparatus 1710 may include two or more processors that may form a multiprocessor system (e.g., in this case the processor 1712 may represent a multiprocessor) that may support multiprocessing. In certain embodiments, the multiprocessor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster).

[0141] The processor 1712 may perform functions associated with the operation of the apparatus 1710, which may include, for example, precoding of antenna gain / phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the apparatus 1710, including processes related to management of communication or communication resources.

[0142] The apparatus 1710 may further include or be coupled to a memory 1714 (internal or external), which may be coupled to the processor 1712, for storing information and instructions that may be executed by the processor 1712. The memory 1714 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and / or removable memory. For example, the memory 1714 can be comprised of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (HDD),or any other type of non-transitory machine or computer readable media. The instructions stored in the memory 1714 may include program instructions or computer program code that, when executed by the processor 1712, enable the apparatus 1710 to perform tasks as described herein.

[0143] In an embodiment, the apparatus 1710 may further include or be coupled to (internal or external) a drive or port that is configured to accept and read an external computer readable storage medium, such as an optical disc, USB drive, flash drive, or any other storage medium. For example, the external computer readable storage medium may store a computer program or software for execution by the processor 1712 and / or the apparatus 1710.

[0144] In some embodiments, the apparatus 1710 may also include or be coupled to one or more antennas 1716 for transmitting and receiving signals and / or data to and from the apparatus 1710. The apparatus 1710 may further include or be coupled to a transceiver 1718 configured to transmit and receive information. The transceiver 1718 may include, for example, a plurality of radio interfaces that may be coupled to the antenna(s) 1716. The radio interfaces may correspond to a plurality of radio access technologies including one or more of GSM, NB-IoT, LTE, 5G, WLAN, Bluetooth, BT-LE, NFC, radio frequency identifier (RFID), ultrawideband (UWB), MulteFire, and the like. The radio interface may include components, such as filters, converters (for example, digital-to-analog converters and the like), mappers, a Fast Fourier Transform (FFT) module, and the like, to generate symbols for a transmission via one or more downlinks and to receive symbols (for example, via an uplink).

[0145] As such, the transceiver 1718 may be configured to modulate information on to a carrier waveform for transmission by the antenna(s) 1716 and demodulate information received via the antenna(s) 1716 for further processing by other elements of the apparatus 1710. In other embodiments, the transceiver 1718 may be capable of transmitting and receiving signals or data directly. Additionally or alternatively, in some embodiments, the apparatus 1710 may include an input and / or output device (I / O device).

[0146] In an embodiment, the memory 1714 may store software modules that provide functionality when executed by the processor 1712. The modules may include, for example, an operating system that provides operating system functionality for the apparatus 1710. The memory 1714 may also store one or more functional modules, such as an application or program, to provide additional functionality for the apparatus 1710. The components of theapparatus 1710 may be implemented in hardware, or as any suitable combination of hardware and software.

[0147] According to some embodiments, the processor 1712 and the memory 1714 may be included in or may form a part of processing circuitry or control circuitry. In addition, in some embodiments, the transceiver 1718 may be included in or may form a part of transceiver circuitry.

[0148] As introduced above, in certain embodiments, the apparatus 1710 may be a network node or RAN node, such as a base station, access point, Node B, eNB, gNB, WLAN access point, or the like.

[0149] According to certain embodiments, the apparatus 1710 may be controlled by the memory 1714 and the processor 1712 to perform the functions associated with any of the embodiments described herein, such as some operations illustrated in, or described with respect to managing entities 104, FIGS. 1, 2, 5, and 9. For instance, the apparatus 1710 may be controlled by the memory 1714 and the processor 1712 to perform the methods of FIGS. 12, 14, and 16.

[0150] In some embodiments, an apparatus (e.g., apparatus 1700 and / or apparatus 1710) may include means for performing a method or any of the variants discussed herein, e.g., a method described with reference to FIGS. 11-16. Examples of the means may include one or more processors, memory, and / or computer program code for causing the performance of the operation.

[0151] In some example embodiments, the functionality of any of the methods, processes, signaling diagrams, algorithms or flow charts described herein may be implemented by software and / or computer program code or portions of code stored in memory or other computer readable or tangible media, and executed by a processor.

[0152] In some example embodiments, an apparatus may be included or be associated with at least one software application, module, unit or entity configured as arithmetic operation(s), or as a program or portions of it (including an added or updated software routine), executed by at least one operation processor. Programs, also called program products or computer programs, including software routines, applets and macros, may be stored in any apparatus- readable data storage medium and may include program instructions to perform particular tasks.

[0153] A computer program product may include one or more computer-executable components which, when the program is run, are configured to carry out some example embodiments. The one or more computer-executable components may be at least one software code or portions of code. Modifications and configurations used for implementing functionality of an example embodiment may be performed as routine(s), which may be implemented as added or updated software routine(s). In one example, software routine(s) may be downloaded into the apparatus.

[0154] As an example, software or a computer program code or portions of code may be in a source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, distribution medium, or computer readable medium, which may be any entity or device capable of carrying the program. Such carriers may include a record medium, computer memory, read-only memory, photoelectrical and / or electrical carrier signal, telecommunications signal, and / or software distribution package, for example. Depending on the processing power needed, the computer program may be executed in a single electronic digital computer or it may be distributed amongst a number of computers. The computer readable medium or computer readable storage medium may be a non-transitory medium.

[0155] In other example embodiments, the functionality may be performed by hardware or circuitry included in an apparatus (e.g., apparatus 1700 or apparatus 1710), for example through the use of an application specific integrated circuit (ASIC), a programmable gate array (PGA), a field programmable gate array (FPGA), or any other combination of hardware and software. In yet another example embodiment, the functionality may be implemented as a signal, such as a non-tangible means that can be carried by an electromagnetic signal downloaded from the Internet or other network.

[0156] According to an example embodiment, an apparatus, such as a node, device, or a corresponding component, may be configured as circuitry, a computer or a microprocessor, such as single-chip computer element, or as a chipset, which may include at least a memory for providing storage capacity used for arithmetic operation(s) and / or an operation processor for executing the arithmetic operation(s).

[0157] Example embodiments described herein apply equally to both singular and plural implementations, regardless of whether singular or plural language is used in connection with describing certain embodiments. For example, an embodiment that describes operations of asingle network node equally applies to embodiments that include multiple instances of the network node, and vice versa.

[0158] One having ordinary skill in the art will readily understand that the example embodiments as discussed above may be practiced with operations 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 example embodiments.

Claims

CLAIMS:

1. A sensing entity, comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the sensing entity at least to: receive a responsive signal, determine one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal, and transmit, to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier.

2. The sensing entity of claim 1, wherein the confidence value relates to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists, and wherein the confidence value is determined based on a received power of the responsive signal.

3. The sensing entity of any of claims 1-2, wherein the radial speed measurement is determined based on one or more of a frequency shift or a phase shift in the responsive signal, and wherein the radial speed measurement further includes: a quality of the radial speed measurement including one or more of: an error value corresponding to an uncertainty of the radial speed measurement, a resolution of the error value, or a number of measurements used to determine the error value.

4. The sensing entity of any of claims 1-3, wherein the indication is transmitted in an information element of a measurement report or a measurement response.

5. A managing entity, comprising: at least one processor; andat least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the managing entity at least to: receive, from a first sensing entity, an indication of one or more of a first confidence value, a radial speed measurement, or a target identifier relating to at least one target entity, and perform a classification relating to the target entity based on one or more of the first confidence value, the radial speed measurement, the target identifier, or complementary information.

6. The managing entity of claim 5, wherein the first confidence value relates to one or more of a likelihood that the target entity exists or a likelihood that a path associated with the target entity exists.

7. The managing entity of any of claims 5-6, wherein the complementary information includes one or more of a previously obtained confidence value relating to the target entity, or a confidence value relating to another target entity located proximate to the target entity.

8. The managing entity of any of claims 5-7, wherein the managing entity is further caused to: classify information relating to the target entity as spurious when the first confidence value does not satisfy a lower confidence value threshold, and classify information relating to the target entity as confirmed when the first confidence value satisfies an upper confidence value threshold.

9. The managing entity of any of claims 5-8, wherein the managing entity is further caused to: receive, from a second sensing entity, an indication of a second confidence value relating to the target entity, and perform the classification relating to the target entity based on the first confidence value and the second confidence value.

10. The managing entity of claim 9, wherein the managing entity is further caused to: classify information relating to the target entity as spurious when an overall confidence value does not satisfy a lower confidence value threshold, classify information relating to the target entity as confirmed when the overall confidence value satisfies an upper confidence value threshold, and wherein the overall confidence value is derived based on the first confidence value and the second confidence value.

11. The managing entity of claim 10, wherein the managing entity is further caused to: classify information relating to the target entity as spurious when each of the first confidence value and the second confidence value does not satisfy the lower confidence value threshold, and classify information relating to the target entity as confirmed when either of the first confidence value or the second confidence value satisfies the upper confidence value threshold.

12. The managing entity of any of claims 5-7, wherein the managing entity is further caused to: based at least partially on the radial speed measurement, classify information relating to the target entity as one or more of spurious, confirmed, active, passive, mobile, or static.

13. A method, comprising: receiving, by a sensing entity, a responsive signal; determining, by the sensing entity, one or more of a confidence value, a radial speed measurement, or a target identifier relating to at least one target entity based on the responsive signal; and transmitting, by the sensing entity and to a managing entity, an indication of one or more of the confidence value, the radial speed measurement, or the target identifier.

14. A method, comprising:receiving, by a managing entity and from a first sensing entity, an indication of one or more of a first confidence value, a radial speed measurement, or a target identifier relating to at least one target entity; and performing, by the managing entity, a classification relating to the target entity based on one or more of the first confidence value, the radial speed measurement, the target identifier, or complementary information.

15. An apparatus, comprising: means for receiving a responsive signal; means for determining one or more of a confidence value relating to the target entity based on the responsive signal; and means for transmitting, to a managing entity, an indication of the confidence value.

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