Communication method and communication device

Abstract

The present application provides a communication method and communication device. For example, the method can be applied to a communication-aware integrated ISAC scenario. In this method, a sensing node sends a sensing measurement signal to a target to be identified (e.g., a first target) and receives an echo signal of the sensing measurement signal. Finally, the sensing node uses the micro-Doppler characteristics of the echo signal to obtain an identification result for the first target. The identification result includes micro-motion information and / or target classification information. Compared to target identification methods that do not consider the micro-Doppler effect, the communication method of the embodiment of the present application can obtain more refined identification results, thereby improving the accuracy of target identification based on the consideration of micro-Doppler characteristics.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular, to a communication method and a communication device. Background Art

[0002] Integrated Sensing and Communication (ISAC), also known as ISAC, is a current research hotspot in the communications field. By introducing sensing capabilities, the service types and application scenarios of mobile communication systems can be expanded. The system's sensing capabilities enable modeling of the spatial structure, mobility, and surrounding environment of both connected and unconnected devices. For example, in scenarios involving high-speed movement, channel modeling and analysis can provide relevant information about the moving target.

[0003] Current ISAC research focuses on sensing through the macroscopic Doppler effect. However, as user needs evolve, the current sensing accuracy needs to be improved. Summary of the Invention

[0004] In view of this, the present application provides a communication method, a communication device, a chip system, a computer-readable storage medium, a computer program product and a communication system, which can obtain more refined recognition results, thereby improving the accuracy of target recognition based on considering micro-Doppler characteristics.

[0005] In a first aspect, a communication method is provided. This method can be executed, for example, by a sensing node, or by components configured within the sensing node (such as circuits, chips, or chip systems). It can also be implemented by a logic module or software that implements all or part of the sensing node's functionality. This application is not limited to this. The following description uses a sensing node as an example.

[0006] The method includes: a sensing node sends a sensing measurement signal to a first target; obtaining an identification result of the first target, wherein the identification result of the first target is determined based on the micro-Doppler characteristics of the echo signal of the sensing measurement signal; the identification result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0007] Based on the above technical solution, the sensing node transmits a sensing measurement signal and, upon receiving an echo signal, utilizes the micro-Doppler characteristics of the echo signal to determine the recognition result of the first target. Compared to considering only the macro-Doppler effect, the embodiments of the present application utilize the micro-Doppler characteristics to obtain a more refined recognition result of the first target, thereby improving the accuracy of target recognition by considering the micro-Doppler characteristics.

[0008] In a second aspect, a communication method is provided. This method can be performed, for example, by a UE, or by a component configured in the UE (such as a circuit, chip, or chip system), or by a logic module or software capable of implementing all or part of the UE's functions. This application is not limited to this. The following description uses a UE as an example.

[0009] The method includes: the UE sends a first message, the first message includes first information, and the first information is used to request the recognition result of the first target in the target space; receives the recognition result of the first target, and the recognition result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0010] Based on the above technical solution, after receiving a more refined first-target recognition result, the UE can more accurately understand the behavior or movement intentions of surrounding objects (such as pedestrians and / or vehicles), thereby assisting the UE in making relevant decisions based on the first-target recognition result. For example, if the UE is an autonomous vehicle, the autonomous vehicle can use the first-target recognition result to more accurately understand the first-target behavior or intention, thereby improving the safety of the autonomous vehicle.

[0011] In a third aspect, a communication method is provided. This method can be performed, for example, by a core network element, or by a component configured within the core network element (such as a circuit, chip, or chip system). It can also be implemented by a logic module or software that implements all or part of the core network element's functions. This application is not limited to this. The following description uses a core network element (such as a LMF) as an example.

[0012] The method includes: a core network element receives a third LPP message from a UE, the third LPP message including first information, the first information being used to request an identification result of a first target; sending an identification result of the first target to the UE, the identification result including one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0013] Based on the above technical solution, after receiving a more refined first target recognition result, the core network element can send the first target recognition result to the UE, enabling the UE to more accurately understand the behavior or movement intentions of surrounding targets (such as pedestrians and / or vehicles), thereby assisting the UE in making relevant decisions based on the first target recognition result. For example, if the UE is an autonomous vehicle, the autonomous vehicle can use the first target recognition result to more accurately understand the first target's behavior or intentions, thereby improving the safety of the autonomous vehicle.

[0014] In a fourth aspect, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is configured to transmit a sensing measurement signal to a first target; the processing module is configured to obtain an identification result of the first target, wherein the identification result of the first target is determined based on the micro-Doppler characteristics of an echo signal of the sensing measurement signal; the identification result includes one or more of the following: micro-motion information and target classification information; the micro-motion information is configured to characterize the micro-motion characteristics of the first target; and the target classification information is configured to characterize the motion classification and / or category classification corresponding to the first target.

[0015] In a fifth aspect, a communication device is provided, comprising a transceiver module. The transceiver module is configured to send a first message, the first message including first information, the first information being used to request an identification result of a first target in a target space; the transceiver module is further configured to receive an identification result of the first target, the identification result including one or more of the following: micro-motion information representing micro-motion characteristics of the first target; and target classification information representing an action classification and / or category classification corresponding to the first target.

[0016] In a sixth aspect, a communication device is provided, comprising a transceiver module. The transceiver module is configured to receive a third LPP message from a UE, the third LPP message including first information, the first information being used to request an identification result of a first target; the transceiver module is further configured to send the identification result of the first target to the UE, the identification result including one or more of the following: micro-motion information and target classification information; the micro-motion information being used to characterize micro-motion characteristics of the first target; and the target classification information being used to characterize an action classification and / or category classification corresponding to the first target.

[0017] The fourth, fifth and sixth aspects are the device-side implementations corresponding to the first, second and third aspects. The explanations, supplements and descriptions of the beneficial effects of the first, second and third aspects are also applicable to the fourth, fifth and sixth aspects and will not be repeated here.

[0018] In a seventh aspect, a communication device is provided, comprising a processor. The processor is coupled to a memory and can be configured to execute instructions or data in the memory to implement the method of any possible implementation of the first aspect. Optionally, the communication device further comprises a memory. Optionally, the communication device further comprises a communication interface, the processor being coupled to the communication interface.

[0019] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0020] In another implementation, the communication device is a chip configured in a sensing node. When the communication device is a chip configured in a sensing node, the communication interface may be an input / output interface.

[0021] In an eighth aspect, a communication device is provided, comprising a processor. The processor is coupled to a memory and can be configured to execute instructions or data in the memory to implement the method of any possible implementation of the second aspect. Optionally, the communication device further comprises a memory. Optionally, the communication device further comprises a communication interface, the processor being coupled to the communication interface.

[0022] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0023] In another implementation, the communication device is a chip configured in a UE. When the communication device is a chip configured in a UE, the communication interface may be an input / output interface.

[0024] In a ninth aspect, a communication device is provided, comprising a processor. The processor is coupled to a memory and can be configured to execute instructions or data in the memory to implement the method of any possible implementation of the third aspect. Optionally, the communication device further comprises a memory. Optionally, the communication device further comprises a communication interface, the processor being coupled to the communication interface.

[0025] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0026] In another implementation, the communication device is a chip configured in a core network element (such as LMF). When the communication device is a chip configured in a core network element, the communication interface may be an input / output interface.

[0027] In a tenth aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method in any possible implementation of any aspect.

[0028] In a specific implementation, the processor may be one or more chips, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, or various logic circuits. The input signal received by the input circuit may be, for example, but not limited to, received and input by a receiver, and the signal output by the output circuit may be, for example, but not limited to, output to and transmitted by a transmitter. The input circuit and the output circuit may be the same circuit, which functions as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation of the processor and various circuits.

[0029] In an eleventh aspect, a communication device is provided, comprising a processor and a memory. The processor is configured to read instructions stored in the memory and receive signals via a receiver and transmit signals via a transmitter to execute the method of any possible implementation of any of the above aspects.

[0030] Optionally, there are one or more processors and one or more memories.

[0031] In the twelfth aspect, a computer program product is provided, which includes: a computer program (also referred to as code, or instructions), which, when executed, enables a computer to execute a method in any possible implementation of any of the above aspects.

[0032] In the thirteenth aspect, a computer-readable storage medium is provided, which stores a computer program (also referred to as code, or instructions) which, when run on a computer, enables the computer to execute a method in any possible implementation of any of the above aspects.

[0033] In a fourteenth aspect, embodiments of the present application provide a chip system, comprising one or more processors configured to retrieve and execute instructions stored in a memory, thereby executing the method of any of the above aspects or any possible implementations of each aspect. The chip system may be composed of a chip or may include a chip and other discrete devices.

[0034] Among them, the chip system may include an input circuit or interface for sending information or data, and an output circuit or interface for receiving information or data.

[0035] In the fifteenth aspect, a communication system is provided, including the aforementioned perception node, UE and core network element.

[0036] Optionally, the communication system may further include other devices that communicate with the UE and / or core network elements.

[0037] Optionally, the communication system may further include other devices that communicate with the sensing node. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic diagram of a communication system used in an embodiment of the present application;

[0039] Figure 2 is a schematic diagram of another communication system used in an embodiment of the present application;

[0040] Figure 3 This is a schematic block diagram of a sensing node according to an embodiment of the present application;

[0041] Figure 4 is a schematic diagram of a communication method according to an embodiment of the present application;

[0042] Figure 5 This is a schematic interaction diagram of a communication method for an application scenario of an embodiment of the present application;

[0043] Figure 6 is a schematic interaction diagram of a communication method in another application scenario of an embodiment of the present application;

[0044] Figure 7 This is a schematic interaction diagram of a communication method for another application scenario of an embodiment of the present application;

[0045] Figure 8 This is a schematic diagram of a method for determining a recognition result according to an embodiment of the present application;

[0046] Figure 9 is a schematic diagram of another method for determining a recognition result according to an embodiment of the present application;

[0047] Figure 10 This is another schematic diagram of a method for determining a recognition result according to an embodiment of the present application;

[0048] Figure 11 is a schematic block diagram of a communication device provided in an embodiment of the present application;

[0049] Figure 12 This is another schematic block diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0051] The technical solutions provided in this application can be applied to various communication systems, such as: global system for mobile communications (GSM) system, general packet radio service (GPRS), wireless local area network (WLAN), long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD) system, sidelink communication system, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) communication system, non-terrestrial network (NTN) communication system, fifth generation (5G) mobile communication system or new radio access technology (NR). Among them, 5G mobile communication system can include non-standalone (NSA) and / or standalone (SA) networking. The technical solution provided in this application may also be applied to future communication systems, such as the sixth generation (6G) mobile communication system, but this application does not limit this.

[0052] The embodiments of the present application are applied to a communication system that supports integrated sensing and communication (ISAC). In some embodiments, ISAC is an important application scenario of 6G.

[0053] Figure 1 FIG2 is a schematic diagram of a communication system 100 used in an embodiment of the present application. The communication system 100 may include a sensing node 110 and one or more first devices 120.

[0054] In the embodiments of this application, a sensing node is a device with both sensing and communication capabilities, or a device with integrated sensing and communication capabilities. With the sensing function enabled, the sensing node transmits sensing measurement signals covering a target area and receives echo signals to extract the characteristics of each target within the target area. The embodiments of this application do not specifically limit the hardware form of the sensing node; any device capable of sensing can serve as a sensing node.

[0055] The embodiments of the present application do not specifically limit the type of the first device. The first device may be a terminal device, a core network device, or another device for which the identification result of the first target is desired. Based on the device type of the first device, the sensing node and the first device may interact using messages or signaling corresponding to a corresponding communication protocol.

[0056] In some embodiments, the sensing node 110 is an access network device or other device with sensing capabilities, and the first device 120 is a terminal device.

[0057] In other embodiments, the sensing node 110 is an access network device or other device with sensing capabilities, and the first device 120 is a core network device.

[0058] Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0059] The network devices in this application may be network-side devices such as access networks, core network devices, or (or) other devices. Access network devices are sometimes also referred to as access nodes. Access network devices have wireless transceiver functions and are used to communicate with terminals. Access network devices include, but are not limited to, base stations (base stations), evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the above-mentioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, access network devices or modules of access network devices in open access networks (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in WiFi systems. Access network devices may also be modules or units that can implement some of the functions of a base station. Access network devices may be macro base stations, micro base stations, indoor stations, relay nodes, donor nodes, or wireless controllers in cloud radio access network (CRAN) scenarios. Optionally, access network devices may also be servers, wearable devices, or in-vehicle devices. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. A base station can communicate with a terminal or through a relay station. A terminal can communicate with multiple base stations using different access technologies. The embodiments of this application do not limit the specific technology or device form factor used by the access network device.

[0060] Core network equipment is a general term for the various functional entities used to manage users, data transmission, and network equipment configuration. Core network equipment can include one or more network elements. For example, in a 5G system, core network equipment may include a location management function (LMF), an access and mobility management function (AMF), a user plane function (UPF), and a session management function (SMF). For another example, in a 6G system, core network equipment may include other functions such as perception network elements, LMF, AMF, UPF, and SMF.

[0061] LMF is responsible for supporting different types of location services related to UE, including positioning of UE and transmission of auxiliary data to UE. The control plane and user plane of LMF are the enhanced-serving mobile location center (E-SMLC) and the secure user plane locator platform (SLP), respectively. LMF may exchange signals with RAN, such as ng-eNB or gNB, and UE. For example, LMF and ng-eNB or gNB exchange information through the new radio positioning protocol annex (NRPPa) message, such as obtaining the configuration information of the positioning reference signal (PRS), the sounding reference signal (SRS), cell timing, cell location information, etc. For another example, LMF and UE transmit UE capability information, auxiliary information, measurement information, etc. through the LTE positioning protocol (LPP) message.

[0062] For example, Figure 2 Another example diagram of a communication system according to an embodiment of the present application is shown. Figure 2 As shown in the figure, the communication system includes a core network, base stations, relay nodes, and various types of terminal devices (such as smartphones, vehicles, unmanned vehicles, smart screens, notebooks, routing devices, smart homes, drones, etc.).

[0063] A sensing node can be Figure 2 The first device may be a base station with integrated sensing function, a terminal device with integrated sensing function (such as a drone), a relay node with integrated sensing function, or other devices with sensing function. Figure 2 Any terminal device in.

[0064] It should be understood that Figure 2 The communication system shown in the description is only an example, and the embodiments of the present application are not limited thereto. For example, Figure 2 The examples of network elements or nodes involved in the description may also be replaced by other devices, without specific limitation.

[0065] In the embodiments of the present application, for example, Figure 3As shown, the sensing node includes at least a feature extraction module, a perception velocity compensation module, and a target classification module. The feature extraction module extracts micro-Doppler features from received signals. The perception velocity compensation module compensates for the traditional Doppler perception velocity based on the micro-Doppler features to improve the accuracy of velocity perception. The target classification module classifies the target under test based on the micro-Doppler features to achieve more accurate target recognition results.

[0066] It should be understood that Figure 3 The modules shown in FIG are exemplarily described, and the embodiments of the present application are not limited thereto. In fact, the sensing node may include Figure 3 The modules shown may be more or less modules. For example, the sensing node may also include a communication module, which is used to implement communication functions.

[0067] In this application, a device for implementing a perception function (or a perception function and a communication function) may be a perception node, or a device capable of supporting a perception node in implementing such a function, such as a processor, circuit, chip, or chip system. This device may be installed in a perception node or used in conjunction with a perception node. In the technical solutions provided in this application, the technical solutions provided in this application are described using the example of a perception node as the device for implementing a perception function.

[0068] The terminal device in this application may be a wireless terminal device capable of receiving network device scheduling and instruction information. A wireless terminal device may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing device connected to a wireless modem. For example, a terminal device may communicate with one or more core networks or the Internet via a radio access network (RAN). A terminal device may also be referred to as a terminal, user equipment (UE), mobile station, or mobile terminal. The terminal device can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, or satellite communication. The terminal may be a mobile phone, tablet computer, computer with wireless transceiver function, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of the present application do not limit the form of the terminal device.

[0069] In this application, the device for implementing the function of a terminal device can be a terminal device, or a device that can support the terminal device to implement the function, such as a processor, circuit, chip, chip system, etc. The device can be installed in the terminal device or connected to the terminal device for use. In the technical solution provided in this application, the technical solution provided in this application is described by taking the terminal device as an example in which the device for implementing the function of the terminal device is a terminal device.

[0070] The access network equipment and / or the terminal can be fixed or movable. The access network equipment and / or the terminal can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on airplanes, balloons and artificial satellites in the air. The embodiments of the present application do not limit the application scenarios of the access network equipment and terminals. The access network equipment and the terminal equipment can be deployed in the same scenario or different scenarios. For example, the access network equipment and the terminal equipment are deployed on land at the same time; or, the access network equipment is deployed on land and the terminal equipment is deployed on the water surface, etc., and no further examples are given.

[0071] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing portions of a base station's functionality. For example, a network device can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be separate or included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0072] In different systems, the CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meanings. For example, in the ORAN system, the CU may also be called an O-CU (Open CU), the DU may also be called an O-DU, the CU-CP may also be called an O-CU-CP, the CU-UP may also be called an O-CU-UP, and the RU may also be called an O-RU. Any of the CU (or CU-CP, CU-UP), DU, and RU in this application may be implemented as a software module, a hardware module, or a combination of software and hardware modules. The CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0073] To facilitate understanding of the embodiments of the present application, a brief description of the terms used in the present application is first provided. Alternatively, the interpretation of some terms may refer to the interpretations in the 3rd Generation Partnership Project (3GPP) standard protocols.

[0074] 1. Microscopic Doppler effect

[0075] The micro-Doppler effect refers to the change in the frequency of the reflected radar wave when the detected target moves at different speeds inside the radar beam. This change is the micro-Doppler effect, which can also be referred to as the micro-Doppler effect. In other words, the micro-Doppler effect refers to the Doppler frequency shift caused by subtle movements such as vibration and / or rotation of the target components. The micro-Doppler effect is of great significance for the identification and / or differentiation of targets. In some application scenarios, due to the complexity of the micro-Doppler effect and its impact on system modeling, whether the micro-Doppler effect should be taken into account in the channel modeling of high-speed moving objects remains to be further discussed. In the embodiment of the present application, in order to accurately identify the target, the micro-Doppler effect is taken as an important consideration.

[0076] 2. Micro-Doppler model

[0077] The types of micro-Doppler include a vibration micro-Doppler type and a rotation micro-Doppler type. Correspondingly, the micro-Doppler model includes a vibration micro-Doppler model and a rotation micro-Doppler model.

[0078] Exemplarily, the equation of the vibration micro-Doppler model satisfies the following formula:

[0079] ;

[0080] in, represents the vibration micro-Doppler shift, Indicates the frequency of the transmitted signal, is the vibration amplitude, is the speed of light, The azimuth angle of the radar's line of sight, represents the azimuth of the target vibration direction, Indicates the elevation angle of the radar line of sight, represents the pitch angle of the target vibration direction, represents the vibration frequency, and t represents the time.

[0081] Exemplarily, the equation of the rotating micro-Doppler model satisfies the following equation:

[0082] ;

[0083] in, represents the rotational micro-Doppler shift, represents the radar operating frequency, represents the angular velocity vector, represents the modulus of the angular velocity vector, represents a skew-symmetric matrix constructed based on unit angular velocity, represents the initial rotation matrix, represents the position vector of the scatterer in the target local coordinate system, represents the radar line of sight direction unit vector, The identity matrix used to describe identity operations in coordinate transformations.

[0084] 3. Hausdorff distance

[0085] The Hausdorff distance is a measure of similarity between two sets of points. It's a definition of the distance between two sets of points. For example, the Hausdorff distance measures the maximum (or minimum) distance between each point in one set and the nearest point in the other set, assessing the similarity between the two sets. For detailed calculations of the Hausdorff distance, please refer to related literature and will not be elaborated here.

[0086] In the embodiment of the present application, the Hausdorff distance may be used to identify the classification of the target, but the embodiment of the present application is not limited thereto.

[0087] It should be understood that the technical terms in this application are for illustration only and are not intended to limit the scope of the present invention. For example, as technology evolves, technical terms may also change. In the case of the same technical meaning, other technical terms should also apply to this application.

[0088] Current ISAC research focuses on using the macroscopic Doppler effect for sensing. However, with evolving user demands, current sensing accuracy needs to be improved, and a specific solution is urgently needed to enhance target recognition accuracy.

[0089] In view of this, the present application provides a communication method, in which a sensing node sends a sensing measurement signal to a first target, and then determines the recognition result of the first target based on the micro-Doppler characteristics of the echo signal of the received sensing measurement signal. The recognition result includes micro-motion information and / or target classification information, and can obtain a more refined recognition result, thereby improving the accuracy of target recognition based on considering the micro-Doppler characteristics.

[0090] The solution provided by this application is described in detail below in conjunction with the corresponding flowchart. It is understood that the schematic flowchart provided by this application mainly uses different devices (e.g., sensing nodes, first devices) as examples of the execution subjects of the interaction diagram to illustrate the method, but this application does not limit the execution subjects of the interaction diagram. For example, the device (e.g., sensing node, first device) in the schematic flowchart can also be a chip, chip system, or processor that supports the device to implement the method, or a logic module or software that can implement all or part of the functions of the device.

[0091] For a unified explanation here, in the interaction process of the embodiment of the present application, the message or signaling interaction involved can adopt the message or signaling in the standard, or it can be a newly introduced message or signaling, and the embodiment of the present application does not make specific limitations on this.

[0092] Figure 4 It is a schematic diagram of a communication method 400 according to an embodiment of the present application. It can be understood that Figure 4 The first device in can be Figure 1 or Figure 2 Any terminal device in the above, or a device in the terminal device (such as a processor, chip, or chip system); or Figure 4 The first device in the above may be a core network device (such as LMF), or may refer to a device in the core network device (such as a processor, chip, or chip system). The sensing node may be Figure 1 or Figure 2 Any access network device in the access network device may also refer to a device in the access network device (such as a processor, chip, or chip system, etc.); or, the sensing node may be a network device or UE or other device with sensing function; or, Figure 3 As shown in the sensor node. Figure 4 As shown, the method 400 includes the following steps:

[0093] Step 410: The sensing node sends a sensing measurement signal to the first target.

[0094] The first target is used to generally refer to a target to be detected or identified. The embodiment of the present application does not limit the area or space where the first target is located. The first target can be any target in the target space or target area. Figure 4 The first target is not shown in FIG.

[0095] It is understood that the embodiment of the present application does not specifically limit the number of targets included in the target space. The target space can also be understood as a target area.

[0096] The present embodiment does not specifically limit the triggering conditions for the sensing node to start the sensing function. "Starting the sensing function" means sending a sensing measurement signal and receiving an echo signal of the sensing measurement signal to identify the target to be measured, thereby obtaining an identification result.

[0097] In one possible implementation, a sensing node can proactively configure periodic sensing tasks to continuously perform sensing measurements. For example, a sensing node can periodically send sensing measurement signals within a certain area to detect one or more targets within the target area.

[0098] Alternatively, in another possible implementation, the sensing node may initiate the sensing function based on a request from another device. For example, the sensing node initiates the sensing function upon receiving a request message from a UE, where the request message specifies that the node desires to obtain identification results for one or more targets in a certain area.

[0099] The embodiment of the present application does not specifically limit the type of the perception measurement signal. For example, the perception measurement signal is a perception signal dedicated to the perception function. For another example, the perception measurement signal is a communication perception integrated signal.

[0100] Exemplarily, the sensing measurement signal includes but is not limited to one or more of the following signals: a downlink positioning reference signal (DL-PRS) and a channel state information-reference signal (CSI-RS).

[0101] It is understood that after the sensing node transmits the sensing measurement signal to the first target, it may receive an echo signal of the sensing measurement signal. The echo signal of the sensing measurement signal refers to the signal reflected by a scatterer in the target space after the sensing node transmits the sensing measurement signal in the target space. A scatterer is a target or object that can reflect or scatter the sensing measurement signal.

[0102] For example, a sensing node at an intersection continuously transmits downlink sensing measurement signals to cover the entire intersection area, and continuously receives reflected signals from all targets within the intersection. Targets within the intersection include, but are not limited to, pedestrians, bicycles, and various types of vehicles.

[0103] The embodiments of the present application do not limit the sensing mode adopted by the sensing node. For example, the sensing mode includes a self-transmitting and self-receiving mode and a self-transmitting and receiving mode. For the purpose of description, assuming that the sensing measurement signal is the sensing signal, the self-transmitting and receiving mode can be understood as the sensing node itself transmitting the sensing measurement signal and receiving the echo signal of the sensing measurement signal. The self-transmitting and receiving mode can be understood as the sensing node itself transmitting the sensing measurement signal to another sensing node without receiving the echo signal of the sensing signal. In other words, the other sensing node receives the signal of the sensing signal sent by the sensing node after it has passed through the wireless channel.

[0104] In step 420 , the sensing node obtains a recognition result of the first target. The recognition result of the first target is determined based on the micro-Doppler characteristics of the echo signal of the sensing measurement signal. The recognition result includes one or more of the following: micro-motion information and target classification information.

[0105] The micro-motion information is used to characterize the micro-motion characteristics of the first target. It should be noted that the term "micro-motion characteristics" is used here to distinguish it from traditional Doppler characteristics. "Microscopic characteristics" can be understood as microscopic characteristics further determined based on micro-Doppler characteristics or micro-Doppler information.

[0106] It should be understood that the terms "micro features" and "micro motion information" are only introduced for the convenience of description. The names of these terms can also be equivalently replaced by other terms with the same function or meaning. The embodiments of this application do not make specific limitations on this.

[0107] The embodiments of the present application do not specifically limit the form of expression of micro-motion information (or micro-motion characteristics). For example, micro-motion information includes one or more of the following parameters: rotation angle, pitch angle, azimuth angle, vibration amplitude, vibration frequency and other microscopic characteristics.

[0108] The target classification information is used to characterize the action classification and / or category classification corresponding to the first target. For example, the action classification includes, but is not limited to, one or more of the following: walking, standing, accelerating, waving, sprinting, and other motion states. Another example includes, but is not limited to, one or more of the following: people, objects, vehicles (including large vehicles, small vehicles, bicycles, motorcycles, and other types of vehicles), and other types of targets.

[0109] It is understood that the embodiment of the present application is described only by taking the first target as an example, and the embodiment of the present application is not limited thereto. In fact, if there may be multiple targets to be identified in the target space, the method for obtaining the recognition result of each target to be identified can refer to the method for determining the recognition result of the first target.

[0110] Taking the first target as an example, the recognition result of the first target may include micro-motion information, target classification information, or both. Figure 8 and Figure 9 Describe the methods for determining micro-motion information and target classification information respectively.

[0111] The recognition result of the first target is determined by the sensing node according to the micro-Doppler characteristics of the echo signal of the received sensing measurement signal.

[0112] The embodiments of the present application do not specifically limit the representation or acquisition method of the micro-Doppler feature. Optionally, the micro-Doppler feature can be obtained by performing a short-time Fourier transform (STFT) on the echo signal to obtain a time-frequency spectrum, which is used to characterize the micro-Doppler feature of the echo signal.

[0113] Because micro-Doppler information is time-varying and non-stationary, the STFT is used to process the received echo signal to produce a time-spectrogram. Micro-Doppler information (micro-Doppler signature) can be derived based on the phase shift in the time-spectrogram. The STFT is an extension of the Fourier transform in the time-frequency domain and is used to analyze non-stationary signals that vary over time in the frequency domain. Specifically, the STFT uses a window function to segment the signal.

[0114] For example, the formula Perform STFT processing on the echo signal to obtain a time-frequency spectrum. Where x(t) is the received signal and h(t) is the window function.

[0115] For the explanation or implementation of STFT, please refer to the description in the relevant technology, and this application does not make any specific limitations on this.

[0116] The above-mentioned STFT processing of the echo signal can be understood as preliminary processing of the received echo signal to obtain the micro-Doppler characteristics. After obtaining the micro-Doppler characteristics, the embodiment of the present application can further determine more refined information, such as micro-motion information and / or target classification information, which will be described in detail below.

[0117] After obtaining the recognition result of the target, the sensing node may store the recognition result of the target locally or send the recognition result of the target to other network elements, which is not specifically limited.

[0118] Optionally, in step 430, the sensing node stores the recognition result of the first target, or updates the local information database based on the recognition result of the first target.

[0119] Exemplarily, if this is the first time that the recognition result of the first target is obtained, the perception node saves the recognition result of the first target locally; if this is not the first time that the recognition result of the first target is obtained, the perception node uses the latest recognition result of the first target to update the recognition result of the first target stored in the local information database.

[0120] The present embodiment does not limit the content stored in the target information database. For example, the sensing node stores the target information of multiple identified targets (including but not limited to: target type, target location, target speed, target movement trajectory, etc.) in the local target information database and updates it in real time.

[0121] Optionally, in step 440, the sensing node sends the recognition result of the first target to the first device. Correspondingly, the first device receives the recognition result of the first target.

[0122] The embodiment of the present application does not limit the execution order of step 430 and step 440. For example, step 430 may be performed first and step 440 may be performed later; or step 430 may be performed first and step 440 may be performed later; or step 430 and step 440 may be performed simultaneously.

[0123] In this embodiment of the present application, the sensing node transmits a sensing measurement signal and, upon receiving an echo signal, utilizes the micro-Doppler characteristics of the echo signal to determine the identification result of the first target. Compared to considering only the macro-Doppler effect, this embodiment of the present application utilizes the micro-Doppler characteristics to obtain a more refined identification result of the first target, thereby improving the accuracy of target recognition by considering the micro-Doppler characteristics.

[0124] For example, if the channel modeling process for high-speed moving targets does not consider the micro-Doppler effect and uses a simplified channel model, the ISAC system's missed detection rate will increase significantly. However, the communication method of the present application, by considering micro-Doppler characteristics, can obtain more refined recognition results, significantly reducing the ISAC system's missed detection rate.

[0125] As mentioned above, the embodiments of the present application do not specifically limit the types of the sensing node and / or the first device.

[0126] Application scenario 1, assuming that the first device is a UE, the sensing node is an access network device (with integrated sensing function), and the access network device sends the identification result of the first target to the UE. The interaction between the access network device and the UE can be achieved through signaling. The embodiment of this application does not specifically limit the type of signaling used for the interaction between the access network device and the UE. Figure 5 The interaction between the UE and the access network device through air interface signaling is described as an example.

[0127] refer to Figure 5 , Figure 5 FIG1 shows an example diagram of an interaction of a communication method 500 for application scenario 1. Figure 5 As shown, the communication method 500 includes at least the following steps:

[0128] Steps 510 to 530 are Figure 4 Steps 410 to 430 shown in FIG are similar and will not be described in detail here.

[0129] In step 540, the access network device sends a first air interface signaling to the UE. Correspondingly, the UE receives the first air interface signaling, which includes the identification result of the first target.

[0130] Optionally, before step 510, the method 500 further includes: step 501: the UE sends a second air interface signaling to the access network device. Correspondingly, the access network device receives the second air interface signaling. The second air interface signaling is used to request an identification result of the first target.

[0131] Optionally, the second air interface signaling includes one or more of the following: information about the target area, and information about the target to be identified. For example, the information about the target area includes a geographic location or a road section name. For another example, the information about the target to be identified includes image information of the target to be identified captured by the UE via a camera or other sensor, location information of the target to be identified, and the like.

[0132] That is, the access network device can perform perception measurement on the first target based on the request of the UE, so that the perception measurement can be performed in a targeted manner, thereby meeting the needs of the UE.

[0133] For example, the UE is an autonomous vehicle. When the autonomous vehicle's perception system detects multiple pedestrians and / or vehicles at an intersection ahead, it cannot accurately determine the pedestrians' direction of movement and speed due to factors such as obstruction or long distance. Based on the needs of safe driving, the autonomous vehicle determines that it needs to obtain identification results of pedestrians and / or vehicles in the intersection area. At this time, the autonomous vehicle sends air interface signaling to access network equipment deployed near the intersection via the cellular vehicle-to-everything (C-V2X) communication protocol to request measurement of one or more targets in a certain area. After obtaining the target identification results, the autonomous vehicle can more accurately understand the movement status or intentions of surrounding targets (including but not limited to pedestrians and vehicles), thereby planning the driving path more safely.

[0134] In application scenario 2, assume that the first device is an LMF and the sensing node is an access network device. The access network device sends the identification result of the first target to the LMF. The LMF sends the identification result of the first target to the UE. The LMF and UE can interact through LPP messages.

[0135] refer to Figure 6 , Figure 6 FIG2 shows an example diagram of an interaction of a communication method 600 for application scenario 2. Figure 6 As shown, the communication method 600 includes at least the following steps:

[0136] Steps 610 to 630 are Figure 4 Steps 410 to 430 shown in FIG are similar and will not be described in detail here.

[0137] Optionally, before step 610, the method 600 further includes: step 602, where the LMF sends perception measurement configuration information to the access network device. Correspondingly, the access network device receives the perception measurement configuration information. The perception measurement configuration information is used to configure the access network device to perform perception measurement.

[0138] This embodiment of the application does not specifically limit the triggering condition of step 602. The LMF may proactively send the perception measurement configuration information to the access network device based on its own information collection needs, or may send the perception measurement configuration information to the access network device based on a request from another device.

[0139] Optionally, before step 602, the method 600 further includes: step 601: the UE sends a third LPP message to the LMF. Correspondingly, the LMF receives the third LPP message. The third LPP message is used to request an identification result of the first target.

[0140] The embodiment of the present application does not specifically limit the content included in the third LPP message. Exemplarily, a parameter or information (such as the first information) is added to the third LPP message to request the recognition result of a certain target.

[0141] Optionally, the third LPP message includes the UE's geographic location information (e.g., the UE's latitude and longitude), and / or geographic location information of the target area to be identified (e.g., the road name or latitude and longitude information of the target area). In other words, the UE reports the relevant geographic location information to the LMF, specifically requesting identification results for the target to be detected.

[0142] In step 640, the access network device sends a first LPP message to the LMF. In response, the LMF receives the first LPP message. The first LPP message includes the identification result of the first target.

[0143] After receiving the recognition result of the first target, the LMF may send the recognition result of the first target to the UE, providing a data basis for the UE to make relevant decisions.

[0144] In step 650, the LMF sends a first LPP message to the UE. In response, the UE receives the first LPP message. The first LPP message includes the identification result of the first target.

[0145] After receiving the recognition result of the first target, the UE can use the recognition result of the first target to more accurately understand the behavior or intention of the first target, thereby assisting the UE to make relevant decisions using the recognition result of the first target.

[0146] above Figure 6 The interaction process shown in involves UE, access network equipment and LMF, through Figure 6In the interactive process shown, both the UE and the LMF can obtain the recognition result of the first target. For example, if the UE is an autonomous vehicle, after receiving the recognition result of the first target, the UE combines it with data collected by its own sensors (such as cameras and lidar) to more accurately understand the behavior or movement intentions of surrounding targets (such as pedestrians and / or vehicles), thereby improving the safety of the autonomous vehicle.

[0147] In application scenario 3, assuming the first device is a smart car and the sensing node is a drone, the drone sends the recognition result of the first target to the smart car (e.g., a self-driving car). It should be understood that application scenario 3 is an example of a UE-UE interconnection scenario, i.e., a UE with sensing capabilities can also detect targets and send the recognition result to another UE via relevant messages or signaling. The embodiments of the present application are not limited to this.

[0148] refer to Figure 7 , Figure 7 FIG3 shows an example diagram of an interaction of a communication method 700 for application scenario 3. Figure 7 As shown, the communication method 700 includes at least the following steps:

[0149] Steps 710 to 730 are Figure 4 Steps 410 to 430 shown in FIG are similar and will not be described in detail here.

[0150] Optionally, before step 710, the method 700 further includes: step 701, the smart car sends a second communication signaling to the drone. In response, the drone receives the second communication signaling. The second communication signaling is used to request the recognition result of the first target.

[0151] In step 740, the drone sends a first communication signal to the smart car. In response, the smart car receives the first communication signal. The first communication signal includes the recognition result of the first target.

[0152] In step 750, the drone sends a third communication signaling to a network device (access network device or core network device). In response, the network device receives the third communication signaling. The third communication signaling includes the recognition result of the first target.

[0153] After the drone determines the recognition result of the first target through the method of the embodiment of the present application, it can send the recognition result of the first target to the UE, or it can also send the recognition result of the first target to the network device. For example, in a low-altitude detection scenario, the drone collects detection information from different intersections and reports the recognition result to the network so that the network can make traffic warnings or macro decisions based on the collected information, thereby helping to alleviate public transportation pressure or realize intelligent transportation applications. For another example, the drone sends the recognized detection information to vehicles around the intersection so that the vehicles can understand the real-time road conditions, adjust routes in time, or make other plans.

[0154] It should be understood that the embodiments of the present application are for Figure 7 The form of the message or signaling involved is not specifically limited, and specific reference may be made to the definition in the relevant standards.

[0155] It can be seen that in the examples of application scenarios 1 to 3 above, the sensing node may be an access network device or a drone. Of course, regardless of the hardware form of the sensing node, the micro-Doppler measurement and classification module can be deployed in the sensing node to determine the identification result of the target to be identified. For example, the micro-Doppler measurement and classification module can be the one mentioned above. Figure 3 The object classification module shown in .

[0156] It should be noted that the above application scenarios 1 to 3 are merely exemplary descriptions, and the embodiments of the present application are not limited thereto.

[0157] The following combination Figure 8 Describes the micro-motion information in the recognition result of the first target determined by the perception node. Figure 8 As shown, it at least includes the following steps:

[0158] In step 810 , the sensing node performs STFT processing on the echo signal to obtain a time-frequency spectrum diagram, and extracts micro-Doppler information of multiple scattering points of the first target based on the characteristics of the time-frequency spectrum diagram.

[0159] The specific implementation of the time-spectrogram feature in step 810 can be referred to the description of step 420 above, and will not be further described here for the sake of brevity. Based on the obtained time-spectrogram, micro-Doppler information of multiple scattering points can be further extracted based on the features of the time-spectrogram.

[0160] The embodiment of the present application does not specifically limit the method for extracting micro-Doppler information based on the features of the time-spectrogram. Optionally, a peak search method is used to extract the features of the time-spectrogram to obtain micro-Doppler information of multiple scattering points.

[0161] For example, the micro-Doppler information of multiple scattering points satisfies the formula ,in, is the amplitude function of the spectrum when receiving the signal, Indicates the maximum value of the amplitude.

[0162] In step 820 , the sensing node determines a first model equation according to the time-frequency spectrum characteristics, where the first model equation is a vibration micro-Doppler model equation or a rotation micro-Doppler model equation.

[0163] Based on the characteristics of the time-spectrogram in step 810, the sensing node can preliminarily determine the type of micro-Doppler. If the characteristics of the time-spectrogram match those of a vibration micro-Doppler, the vibration Doppler model equation is selected; if the characteristics of the time-spectrogram match those of a rotation micro-Doppler, the rotation Doppler model equation is selected. The formulas for the vibration and rotation Doppler model equations can be found in the previous section and are omitted here for brevity.

[0164] Furthermore, the sensing node can estimate the frequency distribution range of the micro-Doppler based on the characteristics of the time-spectrum graph. For example, the sensing node can estimate the frequency distribution range of the vibration micro-Doppler or the frequency distribution range of the rotation micro-Doppler based on prior information.

[0165] For example, the frequency distribution range of the micro-Doppler estimated here can be used as constraints.

[0166] Step 830: The sensing node determines an estimated parameter based on the echo signal of the sensed measurement signal. The estimated parameter includes at least one or more of the following: a Doppler parameter, a delay parameter, and an angle parameter.

[0167] The purpose of introducing step 830 is to combine channel estimation technology to estimate one or more of the Doppler parameters, delay parameters, and angle parameters (for example, angle-of-arrival (AOA)) of the scatterer. The parameters estimated by the channel estimation technology can be substituted into the Doppler model equation to determine whether the Doppler model equation is accurate or to solve the Doppler model equation.

[0168] Optionally, step 830 includes: determining estimated parameters of the scatterer based on a multi-frame joint super-resolution channel parameter estimation technology based on compressed sensing.

[0169] Regarding the multi-frame joint super-resolution channel parameter estimation technology based on compressed sensing, please refer to the description in the relevant technology. For the sake of brevity, it will not be elaborated here.

[0170] Since the millimeter wave channel is sparse, the sparsity of the millimeter wave channel can be used to model the echo signal in order to estimate the Doppler parameters, delay parameters and angle parameters.

[0171] For example, the following steps 1) to 5) are used to estimate the parameters:

[0172] Step 1), the echo signal is modeled as ,in, represents the Doppler steering vector parameter, represents the time domain steering vector parameter, represents the angle steering vector parameter, represents the Kronecker product, which jointly characterizes multi-domain channel characteristics.

[0173] Step 2) Perform uniform discretization sampling on the delay, Doppler and angle domains to construct the perception matrix ; where each atom corresponds to a triplet of discretized time domain, Doppler, and angle, which can be expressed as ;

[0174] Step 3) Convert the channel estimation into a sparse recovery problem, that is, solve it through the sparse recovery algorithm. The signal modeled above can be expressed as the formula ;

[0175] Step 4), by solving the sparse vector h, h includes L non-zero elements, corresponding to the effective scatterers;

[0176] Step 5) By solving the optimization problem of minimizing sparsity, a sparse representation result is obtained;

[0177] For example, solve the following formula:

[0178] ;

[0179] in, represents the perception matrix, Used to constrain and ensure solution sparsity;

[0180] Through step 5), the sparse vector h can be calculated, and the Doppler parameters can be obtained based on the sparse vector h , delay parameters , angle parameters .

[0181] In scenarios involving high-speed target motion, sparse-driven compressed sensing is used to extract the target's micro-Doppler information. This allows for the recovery of sparse signals using fewer measurements, thereby reducing the complexity of conventional STFT processing, which requires a large amount of signal processing. By solving for the sparse vectors, it is possible to obtain information reflecting the motion state of each part of the target, thereby enabling more refined target classification and / or recognition. Compared to principal component analysis (PCA) and deep convolutional neural network (DCNN) methods, which are relatively less dependent on data volume, this method, which converts channel estimation into a sparse recovery problem, is suitable for situations with less data. Furthermore, the extracted time-frequency trajectory directly corresponds to the signal's energy distribution in the time-frequency domain, providing a clearer physical meaning.

[0182] It should be understood that the above-mentioned method for determining the estimated parameters is merely an exemplary description, and the present application is not limited thereto. After obtaining the estimated parameters, it is possible to preliminarily determine whether the micro-Doppler model selected in step 820 is accurate.

[0183] Optionally, in step 840 , the sensing node fuses the estimated parameters in step 830 with the prior information to determine whether the micro-Doppler model type is accurate.

[0184] In a possible implementation, when the first model equation is a vibration micro-Doppler model equation, after obtaining the estimated parameters, the sensing node may calculate the value of the vibration micro-Doppler model equation based on the estimated parameters (for example, the angle parameters mentioned above). ) to further determine and , the sensing node will and Substitute into the vibration micro-Doppler model equation, and the parameters obtained based on prior information (including but not limited to: vibration amplitude , vibration frequency , , , speed of light) are also substituted into the vibration micro-Doppler model equation to preliminarily determine whether the vibration micro-Doppler model equation is accurate. and Including: based on the position of the sensing node and the position of the first target, combined with the estimated angle parameters , the angle parameters in the three-dimensional coordinate system can be calculated, such as and .

[0185] In another possible implementation, when the first model equation is a rotating micro-Doppler model equation, after obtaining the estimated parameters, the sensing node may calculate the Doppler parameters based on the estimated parameters (for example, the Doppler parameters mentioned above). , delay parameters , angle parameters ) to further determine and , the sensing node will and Substitute the parameters obtained based on prior information (including but not limited to: rotation angular velocity vector (such as propeller speed commonly used by drones), initial rotation matrix) into the rotation micro-Doppler model equation to preliminarily determine whether the rotation micro-Doppler model equation is accurate. You can use the angle parameter Sure. You can use the delay parameter and angle parameters Sure.

[0186] The embodiments of this application do not specifically limit the method for determining whether a micro-Doppler model is accurate. For example, after substituting the estimated parameters and parameters obtained based on prior information into the micro-Doppler model, the characteristics or shape of the curve can be visually determined to determine whether it conforms to the characteristics of the corresponding micro-Doppler model. If the direction of the curve shape conforms to or is identical to the selected micro-Doppler model, the micro-Doppler model is accurate. Alternatively, those skilled in the art may use other quantitative indicators to determine whether the micro-Doppler model being used is accurate.

[0187] Optionally, when it is determined that the used micro-Doppler model is accurate, the parameters of the model equation may be solved by using a curve fitting method to obtain the micro-motion information.

[0188] In step 850, the sensing node determines the target function and performs model fitting.

[0189] That is, the sensing node can construct a corresponding objective function based on the selected micro-Doppler model equation in order to solve the parameters of the micro-Doppler model equation.

[0190] Optionally, step 850 includes determining an objective function based on the first model equation, the estimated parameters, and the micro-Doppler information. Exemplarily, the estimated parameters are substituted into the first model equation to obtain a corresponding function expression, and the function expression of the micro-Doppler information obtained by the peak search method is combined to construct a corresponding objective function.

[0191] The embodiment of the present application does not limit the form of the objective function. Optionally, a mean square error function is used as the objective function, and the objective function is solved in combination with an optimization technique.

[0192] For example, the objective function satisfies the following formula:

[0193] ;

[0194] in, is the function after substituting the estimated parameters into the first model equation, The function representing the micro-Doppler information at the i-th moment, ; is the amplitude function of the time-frequency spectrum of the echo signal, and argmax represents the maximum amplitude.

[0195] Optionally, the parameters of the model equation are optimized using a gradient descent method of adaptive learning to solve the values ​​of the parameters of the micro-Doppler model equation.

[0196] It is understood that for different types of micro-Doppler model equations, solving the objective function needs to satisfy corresponding constraints. For example, when the first model equation is a vibration micro-Doppler model equation, the objective function satisfies the following constraints:

[0197] ;

[0198] ;

[0199] ;

[0200] ;

[0201] in, represents the vibration frequency, represents the vibration amplitude, is the azimuth of the target vibration direction, is the pitch angle of the target vibration direction. For example, The maximum value satisfied and minimum value , which can be the frequency distribution range estimated based on the time-spectrogram.

[0202] Exemplarily, when the first model equation is a rotating micro-Doppler model equation, the objective function satisfies the following constraints:

[0203] ;

[0204]

[0205] in, represents the angular velocity of rotation, , , Used to determine the initial rotation matrix.

[0206] Based on the above constraints, the parameters in the micro-Doppler model equation can be solved.

[0207] In step 860, the sensing node determines micro-motion information based on the first model equation and the estimated parameters. The micro-motion information can be represented by micro-motion characteristic parameters.

[0208] For example, the parameters of the micro-Doppler model equation obtained in step 850 (also referred to as micro-motion characteristic parameters) are used to characterize micro-motion information.

[0209] Micro-motion characteristic parameters include different parameters in different situations. Case A: When the first model equation is a vibration micro-Doppler model equation, the micro-motion characteristic parameters include one or more of the following: a pitch angle parameter of the target vibration direction, an azimuth angle parameter of the target vibration direction, a vibration amplitude parameter, and a vibration frequency parameter. Case B: When the first model equation is a rotational micro-Doppler model equation, the micro-motion characteristic parameters include a rotational angular velocity parameter. In other words, different micro-Doppler models can produce different micro-motion characteristic parameters to characterize micro-motion information.

[0210] Traditional Doppler includes both micro-Doppler and macro-Doppler. The presence of micro-Doppler can lead to an overestimation of the perceived velocity derived using traditional Doppler frequency shift. To achieve a more accurate perceived velocity, embodiments of the present application further utilize micro-Doppler perceived velocity to compensate for traditional Doppler velocity, thereby improving velocity perception.

[0211] Optionally, the communication method of the embodiment of the present application further includes the method flow shown in step 870 and step 880. It should be understood that the method flow shown in step 870 and step 880 can be Figure 8 The processes of steps 810 to 860 shown in the figure may be implemented in combination or independently, and there is no specific limitation on this.

[0212] Step 870: The sensing node determines the micro-Doppler frequency shift based on the micro-motion characteristic parameter and the first association relationship.

[0213] The first correlation relationship is a correlation relationship between the first micro-motion characteristic parameter and the micro-Doppler frequency shift. After the micro-motion characteristic parameter is determined, the micro-Doppler frequency shift can be further determined based on the correlation relationship between the two.

[0214] The embodiment of the present application does not specifically limit the correlation relationship satisfied between the micro-motion characteristic parameters and the micro-Doppler shift. Optionally, the correlation relationship between the micro-motion characteristic parameters and the micro-Doppler shift is a certain mathematical formula relationship.

[0215] Exemplarily, the first association is a first model equation; the sensing node substitutes the micro-motion characteristic parameter into the first model equation to obtain the micro-Doppler shift. It should be understood that the above description is based solely on the first model equation as an example, and the present embodiment does not specifically limit this.

[0216] The first model equation is the micro-Doppler model equation determined in step 820 above. Figure 8 After calculating the micro-motion characteristic parameters using the method shown, these parameters can be substituted into the corresponding micro-Doppler model equation to calculate the corresponding micro-Doppler frequency shift. For example, substituting the micro-motion characteristic parameters into the vibration micro-Doppler model equation yields the vibration micro-Doppler frequency shift. Another example is substituting the micro-motion characteristic parameters into the rotational micro-Doppler model equation yields the rotational micro-Doppler frequency shift.

[0217] In step 880 , the sensing node compensates the first sensing speed using the micro-Doppler sensing speed to obtain a second sensing speed, where the micro-Doppler sensing speed is determined according to the micro-Doppler frequency shift.

[0218] After obtaining the micro-Doppler frequency shift, the sensing node can calculate the micro-Doppler sensing speed based on the micro-Doppler frequency shift.

[0219] Optionally, the micro-Doppler sensing speed satisfies the following formula:

[0220] ;

[0221] in, represents the micro-Doppler sensing speed, represents the micro-Doppler frequency shift, Indicates the frequency of the transmitted signal, Represents the speed of light. That is, the aforementioned micro-motion characteristic parameters are substituted into the first model equation to obtain the vibration micro-Doppler frequency shift or the rotation micro-Doppler frequency shift.

[0222] The first perceived speed is the conventional Doppler speed. Conventional Doppler includes both micro-Doppler and macro-Doppler. To obtain a more accurate perceived speed, the present embodiment uses micro-Doppler perceived speed to compensate for conventional Doppler to obtain a compensated perceived speed, i.e., the second perceived speed.

[0223] In other words, after solving for the micro-motion characteristic parameters, they can be substituted into the corresponding Doppler model equation to calculate the corresponding micro-Doppler frequency shift. This frequency shift is then used to calculate the micro-Doppler perceived velocity. After obtaining the micro-Doppler perceived velocity, it is used to compensate for the traditional Doppler velocity, resulting in a more accurate perceived velocity and helping to improve velocity perception resolution.

[0224] Exemplarily, the second perceived speed satisfies the following formula:

[0225] ;

[0226] in, represents the second perceived speed (or the compensated perceived speed), represents the micro-Doppler sensing speed, represents the first perceived speed (or the traditional Doppler perceived speed, i.e. the perceived speed before compensation). It can be the parameter obtained by channel estimation above .

[0227] The embodiments of this application are for The method of obtaining is not limited. For example, You can also use the formula Calculate, where is the frequency of the transmitted signal, is the traditional Doppler shift.

[0228] Since the micro-Doppler frequency shift caused by the high-speed rotation or vibration of the target will interfere with the traditional perception speed, the communication method of the embodiment of the present application uses the micro-Doppler perception speed obtained by the micro-Doppler frequency shift to compensate for the traditional perception speed, which can make the measurement of the macro-Doppler frequency shift more accurate, thereby improving the perception accuracy or improving the speed perception resolution.

[0229] Optionally, as an embodiment, the recognition result of the first target includes target classification information, and the target classification information includes the classification result of the first target.

[0230] The following combination Figure 9 Describes the target classification information in the recognition result of the first target determined by the perception node. Figure 9 As shown, it at least includes the following steps:

[0231] In step 910, the perception node determines the similarity parameters between the time-frequency trajectory of the echo signal and each of the multiple center time-frequency trajectories, and obtains multiple similarity parameters, wherein the center time-frequency trajectory is the center time-frequency trajectory corresponding to the training sample in the sample library, and the time-frequency trajectory of the echo signal is determined based on the time-frequency domain information of the echo signal.

[0232] Exemplarily, the sensing node performs STFT processing on the echo signal to obtain a time-frequency spectrum, and then extracts corresponding time-frequency domain information based on the time-frequency spectrum.

[0233] The embodiment of the present application does not specifically limit the specific method for determining the time-frequency trajectory of the echo signal. Optionally, the time-frequency trajectory of the echo signal is extracted based on the Gabor dictionary and the orthogonal matching pursuit (OMP) algorithm. For example, the echo signal is represented as , which can also be called the signal to be measured, and its time-frequency trajectory is expressed as .

[0234] The similarity parameter can be used to characterize the similarity between the time-frequency trajectory of the signal to be tested and the central time-frequency trajectory of a certain category. The embodiment of the present application does not limit the specific implementation of the similarity parameter.

[0235] Optionally, the similarity parameter is a Hausdorff distance. That is, the classification result corresponding to the target to be detected can be determined by calculating the Hausdorff distance. The relevant explanation of the Hausdorff distance can be referred to above and will not be repeated here.

[0236] It should be understood that the description here is based on the example that the similarity parameter is the Hausdorff distance, and the embodiments of the present application are not limited thereto. Those skilled in the art may use other similarity parameters.

[0237] Step 920: Determine a classification result of the first target based on the multiple similarity parameters.

[0238] That is, after obtaining multiple similarity parameters, the parameter with the highest similarity can be selected from the multiple similarity parameters as the final similarity parameter, and the target result corresponding to the similarity parameter is used as the classification result of the target to be tested (such as the first target).

[0239] Optionally, step 920 includes: determining a first central time-frequency trajectory based on multiple Hausdorff distances, where the first central time-frequency trajectory is the central time-frequency trajectory corresponding to the distance that meets preset conditions among the multiple Hausdorff distances; and determining the classification corresponding to the first central time-frequency trajectory as the classification result of the first target.

[0240] The embodiment of the present application does not specifically limit the specific implementation of the above-mentioned "distances satisfying the preset conditions among multiple Hausdorff distances". For example, the distances satisfying the preset conditions among multiple Hausdorff distances refer to: multiple Hausdorff distances with the smallest values ​​among multiple Hausdorff distances.

[0241] Exemplarily, the first central time-frequency trajectory is the central time-frequency trajectory corresponding to the minimum Hausdorff distance among the multiple Hausdorff distances.

[0242] To facilitate understanding by those skilled in the art, the following is an explanation with reference to a specific formula. Optionally, the classification result of the first target satisfies the following formula:

[0243] ;

[0244] in, represents the action category corresponding to the first target, is the total number of action categories, represents the Hausdorff distance between the time-frequency trajectory of the echo signal and the central time-frequency trajectory of the g-th action category; represents the time-frequency trajectory of the echo signal, represents the central time-frequency trajectory of the g-th action category;

[0245] described Satisfy the following formula:

[0246] ;

[0247] is the time-frequency point in the echo signal arrive Hausdorff distance;

[0248] in, Satisfy the following formula: ;

[0249] in, Represents a time-frequency point of the central time-frequency trajectory of the g-th action category.

[0250] It should be understood that the expressions of the above formulas are only exemplary descriptions, and the embodiments of the present application are not limited thereto. For example, Other representations are possible.

[0251] Based on the processes shown in steps 910 and 920 above, by using the micro-Doppler feature to identify the classification result of the target, the accuracy of identifying the target can be improved, thereby achieving more refined target classification or identification.

[0252] Optionally, the sample library described in step 910 includes central time-frequency trajectories of multiple categories, each category having a corresponding central time-frequency trajectory. The sample library can be pre-established or trained. The sample library can include various types of categories, including but not limited to action categories, object categories, vehicle categories, etc.

[0253] This embodiment of the application does not specifically limit how to construct the sample library. Figure 10 As shown, the perception node constructs the central time-frequency trajectory of each category according to the following steps 101 to 104.

[0254] Step 101: For a first category among multiple categories, generate multiple training samples of the first category.

[0255] For example, the first category includes multiple training samples, which can be expressed as .

[0256] Step 102 : extracting the time-frequency trajectory of each training sample from the plurality of training samples of the first category based on an orthogonal matching pursuit (OMP) algorithm based on compressed sensing.

[0257] Optionally, before step b, a time-frequency library Φ is constructed first.

[0258] For example, the time-frequency library Φ is constructed using the Gabor dictionary; Gabor atoms Defined as: , where the dictionary matrix Φ is composed of Gabor atoms: ;

[0259] Furthermore, after constructing the time domain library Φ, the OMP algorithm based on compressed sensing can be used to obtain the Extract the key time-frequency combination and construct a sparse representation of the time-frequency characteristics of the sample, that is, the time-frequency trajectory .

[0260] Optionally, the OMP algorithm is expressed as follows:

[0261] ;in, represents sparsity, is a non-zero term.

[0262] The above time-frequency trajectory Can be defined as ;in, is the time-frequency position, is the corresponding intensity. After that, the central time-frequency trajectory can be further constructed. The following is described in conjunction with step 103.

[0263] Step 103 : performing cluster analysis on the time-frequency trajectories of the plurality of training samples of the first category based on a clustering algorithm to obtain the central time-frequency trajectory of the first category.

[0264] For example, the K-means clustering algorithm can be used to construct the central time-frequency trajectory, specifically including: for the first category The time-frequency trajectory of multiple training samples Use K-means clustering algorithm to perform cluster analysis, iteratively divide the time-frequency points into K clusters, and calculate the center point of each cluster, and finally output Central time-frequency points constitute the central time-frequency trajectory of this type of action , as the central feature vector of each type of target. Satisfy the formula ;in, Indicates the action category, Indicates the The central time-frequency position and its intensity.

[0265] It should be understood that the above clustering algorithm can also be replaced by other algorithms with similar functions, and the embodiments of the present application are not limited to this.

[0266] Step 104 : Using the method of steps 101 to 103 , generate central time-frequency trajectories of multiple categories.

[0267] Steps 101 to 103 are described using the first category as an example, and the present application is not limited thereto. That is, for other categories, the corresponding central time-frequency trajectory can also be determined by referring to the above steps.

[0268] Therefore, based on the above steps 101 to 104 , the sample library can be constructed, thereby providing a basis for subsequent target classification and recognition.

[0269] It should be noted that Figure 10 Steps 101 to 104 shown can be independent of Figure 9 The method shown can also be implemented with Figure 9 The methods shown are implemented in combination, and are not specifically limited to this. For example, Figure 10 The method shown in further comprises the following steps:

[0270] Step 11: The sensing node receives the echo signal.

[0271] In step 12, the sensing node maps the echo signal into the time-frequency domain through STFT transformation.

[0272] Step 13: The sensing node extracts the time-frequency trajectory based on the OMP algorithm.

[0273] In step 14, the perception node calculates the Hausdorff distance between the time-frequency trajectory and the central time-frequency trajectory of each category.

[0274] In step 15, the sensing node determines the recognition result of the time-frequency trajectory to be measured based on the nearest neighbor classification principle. The recognition result of the time-frequency trajectory to be measured is the classification corresponding to the smallest Hausdorff distance among multiple Hausdorff distances.

[0275] The above steps 11 to 15 can be understood as the above Figure 9 An example of this, for related descriptions, please refer to the previous article Figure 9 The description of , will not be elaborated here.

[0276] It is understandable that the above Figure 10 It may include two stages: the first stage is the process of building a sample library, specifically including the above steps 101 to 104; the second stage is the process of using the sample library to perform target classification and recognition, including the above steps 11 to 15.

[0277] It should be understood that Figures 1 to 10 The flowcharts or scenario diagrams shown are only for ease of understanding and are not intended to limit the embodiments of the present application to the examples shown in the diagrams. In fact, those skilled in the art will Figures 1 to 10 The examples in can be equivalently transformed to obtain more implementation methods.

[0278] Combined with the above Figures 1 to 10 , describes in detail the communication method provided by the embodiment of the present application. Figure 11 and Figure 12 It should be understood that the communication device of the present invention can execute the various communication methods of the above embodiments of the present invention, that is, the specific working processes of the following various products can refer to the corresponding processes in the above method embodiments.

[0279] In each of the above embodiments, the perception node can execute some or all of the steps in each embodiment; the UE can execute some or all of the steps in each embodiment; the core network device can execute some or all of the steps in each embodiment. These steps or operations are only examples, and the embodiments of the present application can also execute other operations or variations of various operations. In addition, the various steps can be executed in a different order as presented in each embodiment, and it is possible that not all operations in the embodiments of the present application need to be executed. Moreover, the size of the sequence number of each step does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0280] Figure 111 is a schematic block diagram of a communication device 1100 provided in an embodiment of the present application. Figure 11 As shown, the communication device 1100 may include a communication module 1120. The communication module 1120 can implement corresponding communication functions, which can be internal communication functions of the communication device 1100 or communication functions between the communication device 1100 and other devices. Optionally, the communication module 1120 can also be referred to as a communication interface or a transceiver module. Optionally, the communication device 1100 also includes a processing module 1110. The processing module 1110 can implement corresponding processing functions.

[0281] Optionally, the communication device 1100 further includes a storage module, which can be used to store instructions and / or data; the processing module 1110 can read the instructions and / or data in the storage module, so that the communication device 1100 implements the aforementioned method embodiment.

[0282] In one possible design, the communication device 1100 may correspond to the sensing node in the above method embodiments, or a component configured in the sensing node (such as a circuit, chip, or chip system). The communication device 1100 may be used to execute the steps or processes performed by the sensing node in any of the above method embodiments.

[0283] For example, the communication module 1120 is configured to send a perception measurement signal to the first target;

[0284] The processing module 1110 is used to obtain the recognition result of the first target, and the recognition result of the first target is determined based on the micro-Doppler characteristics of the echo signal of the perception measurement signal; the recognition result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0285] Optionally, as an embodiment, the communication module 1120 is further used to send a first signaling to the UE, where the first signaling includes an identification result of the first target.

[0286] Optionally, as an embodiment, the communication module 1120 is further used to receive a second signaling from the UE, where the second signaling is used to request an identification result of the first target.

[0287] Optionally, as an embodiment, the communication module 1120 is further configured to send a first LPP message to a core network element, where the first LPP message includes an identification result of the first target.

[0288] Optionally, as an embodiment, the communication module 1120 is further used to receive configuration information from a core network element, where the configuration information is used to configure the perception node to perform perception measurement.

[0289] Optionally, as an embodiment, the recognition result includes the micro-motion information; the processing module 1110 is used to obtain the recognition result of the first target, including: determining a first model equation based on the micro-Doppler feature, the first model equation being a vibration micro-Doppler model equation or a rotation micro-Doppler model equation; determining the micro-motion information based on the first model equation and estimated parameters, the micro-motion information being characterized by micro-motion characteristic parameters; wherein the estimated parameters are determined based on the echo signal, and the estimated parameters include at least one or more of the following: Doppler parameters, time delay parameters and angle parameters; in the case where the first model equation is a vibration micro-Doppler model equation, the micro-motion characteristic parameters include one or more of the following: a pitch angle parameter of the target vibration direction, an azimuth angle parameter of the target vibration direction, a vibration amplitude parameter and a vibration frequency parameter; in the case where the first model equation is a rotation micro-Doppler model equation, the micro-motion characteristic parameters include a rotation angular velocity parameter.

[0290] Optionally, as an embodiment, the processing module 1110 is further configured to extract micro-Doppler information of multiple scattering points of the first target based on a peak search method;

[0291] Among them, the processing module 1110 is used to determine the micro-motion information based on the first model equation and the estimated parameters, including: solving the objective function based on the gradient descent method to obtain the micro-motion characteristic parameters of the first model equation, and the objective function is determined based on the first model equation, the estimated parameters and the micro-Doppler information.

[0292] Optionally, as an embodiment, the objective function satisfies the following formula:

[0293] ;

[0294] in, is the function after substituting the estimated parameters into the first model equation, The function representing the micro-Doppler information at the i-th moment, ; is the amplitude function of the time-frequency spectrum of the echo signal, and argmax represents the maximum amplitude value;

[0295] When the first model equation is a vibration micro-Doppler model equation, the objective function satisfies the following constraints:

[0296] ;

[0297] ;

[0298] ;

[0299] ;

[0300] in, represents the vibration frequency, represents the vibration amplitude, is the azimuth of the target vibration direction, is the pitch angle of the target vibration direction;

[0301] When the first model equation is a rotating micro-Doppler model equation, the objective function satisfies the following constraints:

[0302] ;

[0303]

[0304] in, represents the angular velocity of rotation, , , Used to determine the initial rotation matrix.

[0305] Optionally, as an embodiment, the processing module 1110 is further used to: determine a micro-Doppler frequency shift based on the micro-motion characteristic parameter and the first correlation relationship; and use the micro-Doppler perception speed to compensate the first perception speed to obtain a second perception speed, wherein the micro-Doppler perception speed is determined based on the micro-Doppler frequency shift.

[0306] Optionally, as an embodiment, the second perceived speed satisfies the following formula:

[0307] ;

[0308] in, represents the second perceived speed, represents the micro-Doppler sensing speed, represents the first sensed speed; the micro-Doppler sensed speed satisfies the following formula:

[0309] ;

[0310] in, represents the micro-Doppler sensing speed, represents the micro-Doppler frequency shift, Indicates the frequency of the transmitted signal, Represents the speed of light.

[0311] Optionally, as an embodiment, the recognition result includes the target classification information, and the target classification information includes a classification result of the first target;

[0312] The processing module 1110 is used to obtain the recognition result of the first target, including: determining the similarity parameters between the time-frequency trajectory of the echo signal and each center time-frequency trajectory in a plurality of center time-frequency trajectories to obtain a plurality of similarity parameters, wherein the center time-frequency trajectory is the center time-frequency trajectory corresponding to the training sample in the sample library, and the time-frequency trajectory of the echo signal is determined based on the time-frequency domain information of the echo signal; and determining the classification result of the first target based on the plurality of similarity parameters.

[0313] Optionally, as an embodiment, the similarity parameter is the Hausdorff distance; wherein the processing module 1110 is used to determine the classification result of the first target based on the multiple similarity parameters, including: determining a first central time-frequency trajectory based on multiple Hausdorff distances, the first central time-frequency trajectory being the central time-frequency trajectory corresponding to the distance that meets preset conditions among the multiple Hausdorff distances; and determining the classification corresponding to the first central time-frequency trajectory as the classification result of the first target.

[0314] Optionally, as an embodiment, the first central time-frequency trajectory is the central time-frequency trajectory corresponding to the minimum Hausdorff distance among the multiple Hausdorff distances.

[0315] The above is only an example, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0316] Alternatively, in one possible design, the communication device 1100 may correspond to the UE in the above method embodiments, or a component configured in the UE (such as a circuit, chip, or chip system). The communication device 1100 may be used to execute the steps or processes executed by the UE in any of the above method embodiments.

[0317] For example, the communication module 1120 is used to send a first message, which includes first information, and the first information is used to request the recognition result of the first target in the target space; the communication module 1120 is also used to receive the recognition result of the first target, and the recognition result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0318] Optionally, as an embodiment, the communication module 1120 is used to send a first message, including: sending a third LPP message to the core network network element, the third LPP message being the first message; wherein, the communication module 1120 is used to receive the identification result of the first target, including: receiving a second LPP message from the core network network element, the second LPP message being used to respond to the third LPP message, the second LPP message including the identification result of the first target.

[0319] Optionally, as an embodiment, the communication module 1120 is used to send a first message, including: sending a second signaling to the access network device, the second signaling is the first message; wherein, the communication module 1120 is used to receive the identification result of the first target, including: receiving a first signaling from the access network device, the first signaling includes the identification result of the first target.

[0320] The above is only an example, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0321] Alternatively, in one possible design, the communication device 1100 may correspond to the core network element (e.g., LMF) in the above method embodiments, or a component configured in the core network element (e.g., a circuit, chip, or chip system). The communication device 1100 may be used to execute the steps or processes executed by the core network element in any of the above method embodiments.

[0322] The communication module 1120 is configured to receive a third LPP message from the UE, where the third LPP message includes first information, where the first information is used to request an identification result of the first target;

[0323] The communication module 1120 is also used to send the identification result of the first target to the UE, and the identification result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target.

[0324] Optionally, as an embodiment, the communication module 1120 is further configured to receive a first LPP message sent from an access network device, where the first LPP message includes the identification result.

[0325] Optionally, as an embodiment, the communication module 1120 is further used to send configuration information to the access network device, where the configuration information is used to configure the access network device to perform perception measurement.

[0326] The above is only an example, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0327] Figure 12 1 is another schematic block diagram of a communication device 1200 provided in an embodiment of the present application. The communication device 1200 may be a chip, chip system, or processor, etc., that implements the above-described method in a sensing node or a first device. The communication device 1200 may be used to implement the method described in the above-described method embodiment. For details, please refer to the description of the above-described method embodiment.

[0328] like Figure 12 As shown, the communication device 1200 may include one or more processors 1210, which may also be referred to as a processing unit or processing module, and may implement certain control functions. The processor 1210 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device 1200 (e.g., a base station, a baseband chip, a user, a user chip), execute software programs, and process data in the software programs.

[0329] In an optional design, the processor 1210 may also store instructions and / or data, which can be executed by the processor 1210 to enable the communication device 1200 to perform the method described in the above method embodiment.

[0330] In another optional design, the communication device 1200 may include a communication interface 1220 for implementing receiving and transmitting functions. For example, the communication interface 1220 may be a transceiver circuit, an interface, an interface circuit, or a transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing the receiving and transmitting functions may be separate or integrated. The transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or the transceiver circuit, interface, interface circuit, or transceiver may be used for transmitting or delivering signals.

[0331] Optionally, the communication device 1200 may include one or more memories 1230, which may store instructions. These instructions may be executed on the processor 1210, causing the communication device 1200 to perform the method described in the above method embodiment. Optionally, the memory 1230 may also store data. Optionally, the processor 1210 may also store instructions and / or data. The processor 1210 and memory 1230 may be provided separately or integrated together.

[0332] It should be understood that, in one possible design, each step in the method embodiment provided in the present application can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0333] In one implementation, the communication device 1200 may correspond to a sensing node in the above-described method embodiment and may be configured to execute the various steps and / or processes performed by the sensing node in the above-described method embodiment. The processor 1210 may be configured to execute instructions stored in the memory 1230, and when the processor 1210 executes the instructions stored in the memory, the processor 1210 is configured to execute the various steps and / or processes of the above-described method embodiment corresponding to the sensing node.

[0334] In another implementation, the communication device 1200 may correspond to the first device in the above-mentioned method embodiment, and may be used to execute the various steps and / or processes performed by the first device in the above-mentioned method embodiment. The processor 1210 may be used to execute instructions stored in the memory 1230, and when the processor 1210 executes the instructions stored in the memory, the processor 1210 is used to execute the various steps and / or processes of the above-mentioned method embodiment corresponding to the first device.

[0335] It should be understood that the processing device may be one or more chips. For example, the processing device may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0336] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0337] Based on the methods provided in the embodiments of the present application, the present application also provides a chip system, which includes one or more processors configured to retrieve and execute instructions stored in a memory, thereby executing the methods of the embodiments of the present application. The chip system can be composed of a chip or can include a chip and other discrete devices.

[0338] Among them, the chip system may include an input circuit or interface for sending information or data, and an output circuit or interface for receiving information or data.

[0339] According to the method provided in the embodiment of the present application, the present application also provides a communication system, which includes the aforementioned perception node and a first device (such as UE, network device).

[0340] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, which, when the computer program code is run on a computer, enables the computer to execute the various steps or processes performed by the perception node and the first device (such as UE, network device) in any of the aforementioned method embodiments.

[0341] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores program code. When the program code runs on a computer, the computer executes the various steps or processes performed by the perception node and the first device (such as UE, network device) in any of the aforementioned method embodiments.

[0342] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory.

[0343] In the embodiments of this application, each term and English abbreviation is provided for convenience of description and shall not constitute any limitation to this application. This application does not exclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0344] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0345] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0346] It should be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0347] In short, the above description is only a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included in the scope of protection of this application.

Claims

1. A communication method, characterized in that: Applied to a sensing node, the method includes: sending a perception measurement signal to a first target; Acquire a recognition result of the first target, where the recognition result of the first target is determined based on a micro-Doppler feature of an echo signal of the sensed measurement signal; The recognition result includes one or more of the following: micro-motion information, target classification information; the micro-motion information is used to characterize the micro-motion characteristics of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target; The micro-motion information is determined based on a first model equation and estimated parameters, wherein the first model equation is determined based on the micro-Doppler characteristics; wherein the estimated parameters are determined based on the echo signal, and the estimated parameters include at least one or more of the following: a Doppler parameter, a time delay parameter, and an angle parameter; The micro-motion information is characterized by micro-motion characteristic parameters; when the first model equation is a vibration micro-Doppler model equation, the micro-motion characteristic parameters include one or more of the following: a pitch angle parameter of the target vibration direction, an azimuth angle parameter of the target vibration direction, a vibration amplitude parameter, and a vibration frequency parameter; when the first model equation is a rotational micro-Doppler model equation, the micro-motion characteristic parameters include a rotational angular velocity parameter; The target classification information includes the classification result of the first target; the classification result of the first target is determined based on multiple similarity parameters; the multiple similarity parameters are obtained by determining the similarity parameters between the time-frequency trajectory of the echo signal and each center time-frequency trajectory in multiple center time-frequency trajectories, the center time-frequency trajectory is the center time-frequency trajectory corresponding to the training sample in the sample library, and the time-frequency trajectory of the echo signal is determined based on the time-frequency domain information of the echo signal.

2. The method according to claim 1, characterized in that The method further comprises: A first signaling is sent to the UE, where the first signaling includes an identification result of the first target.

3. The method according to claim 2, characterized in that Before sending the first signaling to the UE, the method further includes: A second signaling is received from the UE, where the second signaling is used to request an identification result of the first target.

4. The method according to claim 1, wherein The method further comprises: A first Long Term Evolution Positioning Protocol (LPP) message is sent to a core network element, where the first LPP message includes an identification result of the first target.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Configuration information is received from a core network element, where the configuration information is used to configure the sensing node to perform sensing measurements.

6. The method according to claim 1, characterized in that The method further comprises: extracting micro-Doppler information of multiple scattering points of the first target based on a peak search method; The determining of the micro-motion information according to the first model equation and the estimated parameters includes: An objective function is solved based on a gradient descent method to obtain micro-motion characteristic parameters of the first model equation, wherein the objective function is determined according to the first model equation, the estimated parameters, and the micro-Doppler information.

7. The method according to claim 6, characterized in that The objective function satisfies the following formula: ； in, is the function after substituting the estimated parameters into the first model equation, The function representing the micro-Doppler information at the i-th moment, ; is the amplitude function of the time-frequency spectrum of the echo signal, and argmax represents the maximum amplitude value; When the first model equation is a vibration micro-Doppler model equation, the objective function satisfies the following constraints: ； ； ； ； in, represents the vibration frequency, represents the vibration amplitude, is the azimuth of the target vibration direction, is the pitch angle of the target vibration direction; When the first model equation is a rotating micro-Doppler model equation, the objective function satisfies the following constraints: ； in, represents the angular velocity of rotation, , , Used to determine the initial rotation matrix.

8. The method according to claim 1, characterized in that The method further comprises: determining a micro-Doppler frequency shift based on the micro-motion characteristic parameter and the first correlation relationship; The first perceived speed is compensated by using a micro-Doppler perceived speed to obtain a second perceived speed, wherein the micro-Doppler perceived speed is determined according to the micro-Doppler frequency shift.

9. The method according to claim 8, characterized in that The second perception speed satisfies the following formula: ； in, represents the second perceived speed, represents the micro-Doppler sensing speed, represents the first sensed speed; the micro-Doppler sensed speed satisfies the following formula: in, represents the micro-Doppler sensing speed, represents the micro-Doppler frequency shift, Indicates the frequency of the transmitted signal, Represents the speed of light.

10. The method according to claim 1, characterized in that The similarity parameter is Hausdorff distance; The step of determining the classification result of the first target according to the multiple similarity parameters includes: Determine a first central time-frequency trajectory according to the multiple Hausdorff distances, where the first central time-frequency trajectory is the central time-frequency trajectory corresponding to a distance that meets a preset condition among the multiple Hausdorff distances; The classification corresponding to the first central time-frequency trajectory is determined as the classification result of the first target.

11. The method according to claim 10, characterized in that The first central time-frequency trajectory is the central time-frequency trajectory corresponding to the minimum Hausdorff distance among the multiple Hausdorff distances.

12. A communication method, characterized in that: Applied to user equipment UE, the method includes: Sending a first message, where the first message includes first information, and the first information is used to request an identification result of a first target in the target space; receiving a recognition result of the first target, where the recognition result of the first target is determined based on a micro-Doppler feature of an echo signal of a sensed measurement signal, and the recognition result includes one or more of the following: micro-motion information and target classification information; the micro-motion information is used to characterize the micro-motion feature of the first target; and the target classification information is used to characterize the motion classification and / or category classification corresponding to the first target; The micro-motion information is determined based on a first model equation and estimated parameters, wherein the first model equation is determined based on the micro-Doppler characteristics; wherein the estimated parameters are determined based on the echo signal, and the estimated parameters include at least one or more of the following: a Doppler parameter, a time delay parameter, and an angle parameter; The micro-motion information is characterized by micro-motion characteristic parameters; when the first model equation is a vibration micro-Doppler model equation, the micro-motion characteristic parameters include one or more of the following: a pitch angle parameter of the target vibration direction, an azimuth angle parameter of the target vibration direction, a vibration amplitude parameter, and a vibration frequency parameter; when the first model equation is a rotational micro-Doppler model equation, the micro-motion characteristic parameters include a rotational angular velocity parameter; The target classification information includes the classification result of the first target; the classification result of the first target is determined based on multiple similarity parameters; the multiple similarity parameters are obtained by determining the similarity parameters between the time-frequency trajectory of the echo signal and each center time-frequency trajectory in multiple center time-frequency trajectories, the center time-frequency trajectory is the center time-frequency trajectory corresponding to the training sample in the sample library, and the time-frequency trajectory of the echo signal is determined based on the time-frequency domain information of the echo signal.

13. The method according to claim 12, characterized in that The sending of the first message includes: Sending a third LPP message to a core network element, where the third LPP message is the first message; The receiving of the recognition result of the first target includes: A second LPP message is received from the core network element, where the second LPP message is used to respond to the third LPP message, and the second LPP message includes an identification result of the first target.

14. The method according to claim 12, characterized in that The sending of the first message includes: Sending a second signaling to the access network device, where the second signaling is the first message; The receiving of the recognition result of the first target includes: A first signaling is received from the access network device, where the first signaling includes an identification result of the first target.

15. A communication method, characterized in that: Applied to a core network element, the method includes: receiving a third LPP message from the UE, where the third LPP message includes first information, where the first information is used to request an identification result of the first target; Sending an identification result of the first target to the UE, where the identification result of the first target is determined based on a micro-Doppler feature of an echo signal of the sensed measurement signal, and the identification result includes one or more of the following: micro-motion information and target classification information; the micro-motion information is used to characterize the micro-motion feature of the first target; the target classification information is used to characterize the action classification and / or category classification corresponding to the first target; The micro-motion information is determined based on a first model equation and estimated parameters, wherein the first model equation is determined based on the micro-Doppler characteristics; wherein the estimated parameters are determined based on the echo signal, and the estimated parameters include at least one or more of the following: a Doppler parameter, a time delay parameter, and an angle parameter; The micro-motion information is characterized by micro-motion characteristic parameters; when the first model equation is a vibration micro-Doppler model equation, the micro-motion characteristic parameters include one or more of the following: a pitch angle parameter of the target vibration direction, an azimuth angle parameter of the target vibration direction, a vibration amplitude parameter, and a vibration frequency parameter; when the first model equation is a rotational micro-Doppler model equation, the micro-motion characteristic parameters include a rotational angular velocity parameter; The target classification information includes the classification result of the first target; the classification result of the first target is determined based on multiple similarity parameters; the multiple similarity parameters are obtained by determining the similarity parameters between the time-frequency trajectory of the echo signal and each center time-frequency trajectory in multiple center time-frequency trajectories, the center time-frequency trajectory is the center time-frequency trajectory corresponding to the training sample in the sample library, and the time-frequency trajectory of the echo signal is determined based on the time-frequency domain information of the echo signal.

16. The method according to claim 15, characterized in that Before sending the identification result of the first target to the UE, the method further includes: A first LPP message sent from an access network device is received, where the first LPP message includes the identification result.

17. The method according to claim 15 or 16, characterized in that The method further comprises: Configuration information is sent to an access network device, where the configuration information is used to configure the access network device to perform perception measurement.

18. A communication device, characterized in that: The device comprises at least one processor coupled to a memory, wherein the memory stores a program or instruction, and wherein the processor executes the program or instruction so that the device is used to perform the method according to any one of claims 1 to 11; or, the device is used to perform the method according to any one of claims 12 to 14; or, the device is used to perform the method according to any one of claims 15 to 17.

19. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instructions are executed, the computer is caused to perform the method according to any one of claims 1 to 11; or, the computer is caused to perform the method according to any one of claims 12 to 14; or, the computer is caused to perform the method according to any one of claims 15 to 17.

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