Communication method and communication device

Abstract

The invention provides a communication method and a communication device, and the method can be suitable for a communication perception integration (ISAC) scene. According to the method, a sensing node sends a sensing measurement signal to a to-be-recognized target (such as a first target) and receives an echo signal of the sensing measurement signal; and finally, acquiring an identification result of the first target by using the micro-Doppler characteristics of the echo signal, wherein the identification result comprises micro-motion information and / or target classification information. Compared with a target identification mode without considering the micro-Doppler effect, the communication method provided by the embodiment of the invention can obtain a finer identification result, so that the accuracy of target identification is improved on the basis of considering the 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) is a hot topic in the current communication field. It can also be referred to as ISAC. By introducing perception capabilities, the service types and application scenarios of mobile communication systems can be expanded. The system's perception capabilities can model the spatial structure, mobility, and surrounding environment of unconnected and connected devices. For example, in a scenario where the target is moving at high speed, by performing channel modeling analysis on the moving target, relevant information that can reflect the moving target can be obtained.

[0003] In the current research topics on ISAC, perception is generally achieved through the macroscopic Doppler effect. With the development of user needs, the current perception 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, which can be executed by a sensing node, or can be executed by a component (such as a circuit, a chip, or a chip system, etc.) configured in the sensing node, or can be implemented by a logic module or software that can implement all or part of the sensing node functions. This application does not limit this. The following description takes the 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 sends a sensing measurement signal, and after receiving the echo signal, uses the micro-Doppler feature of the echo signal to determine the recognition result of the first target. Compared with only considering the macroscopic Doppler effect, the embodiment of the present application considers the micro-Doppler feature and uses the micro-Doppler feature to obtain a more refined recognition result of the first target, thereby improving the accuracy of target recognition on the basis of considering the micro-Doppler feature.

[0008] In a second aspect, a communication method is provided, which can be executed by a UE, or can be executed by a component configured in the UE (such as a circuit, a chip, or a chip system, etc.), or can be implemented by a logic module or software that can implement all or part of the UE functions. This application is not limited to this. The following description takes the 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 detailed recognition result of the first target, the UE can more accurately understand the behavior or movement intention of the surrounding targets (such as pedestrians and / or vehicles), thereby assisting the UE to make relevant decisions using the recognition result of the first target. For example, in the case where the UE is an autonomous vehicle, the autonomous vehicle can use the recognition result of the first target to more accurately understand the behavior or intention of the first target, thereby improving the safety of the autonomous vehicle.

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

[0012] The method includes: a core network element receives a third LPP message from a UE, the third LPP message includes first information, and the first information is used to request an identification result of a first target; the identification result of the first target is sent 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.

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

[0014] In a fourth aspect, a communication device is provided, which includes a processing module and a transceiver module. The transceiver module is used to send a sensing measurement signal to a first target; the processing module is used to obtain a 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 sensing 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.

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

[0016] In a sixth aspect, a communication device is provided, the communication device comprising a transceiver module. The transceiver module is used to receive a third LPP message from a UE, the third LPP message comprising first information, the first information being used to request an identification result of a first target; the transceiver module is also used to send an identification result of the first target to the UE, the identification result comprising 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.

[0017] The fourth, fifth and sixth aspects are the implementations on the device side 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 used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect. Optionally, the communication device also includes a memory. Optionally, the communication device also includes a communication interface, and the processor is 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 used to execute instructions or data in the memory to implement the method in any possible implementation of the second aspect. Optionally, the communication device also includes a memory. Optionally, the communication device also includes a communication interface, and the processor is 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 the UE. When the communication device is a chip configured in the 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 used to execute instructions or data in the memory to implement the method in any possible implementation of the third aspect. Optionally, the communication device also includes a memory. Optionally, the communication device also includes a communication interface, and the processor is 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 used 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 the specific implementation process, the processor can be one or more chips, the input circuit can be an input pin, the output circuit can be an output pin, and the processing circuit can be a transistor, a gate circuit, a trigger, and various logic circuits. The input signal received by the input circuit can be, for example, but not limited to, received and input by a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to a transmitter and transmitted by the transmitter, and the input circuit and the output circuit can be the same circuit, which is used as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation methods 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 used to read instructions stored in the memory, and can receive signals through a receiver and transmit signals through a transmitter to execute the method in any possible implementation of any of the above aspects.

[0030] Optionally, the number of the processors is one or more, and the number of the memories is one or more.

[0031] In a twelfth aspect, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code, or instruction), 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 instruction) which, when executed 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, an embodiment of the present application provides a chip system, which includes one or more processors for calling and running instructions stored in a memory from a memory, so that the method in any possible implementation of each aspect or each aspect is executed. The chip system can be composed of a chip, or it can 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 a fifteenth aspect, a communication system is provided, comprising the aforementioned perception node, UE and core network element.

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

[0037] Optionally, the communication system may also include other devices for communicating with the sensing node. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic diagram of a communication system applied in an embodiment of the present application; Figure 2 is a schematic diagram of another communication system applied in an embodiment of the present application; Figure 3 is a schematic block diagram of a sensing node in an embodiment of the present application; Figure 4 is a schematic diagram of a communication method according to an embodiment of the present application; Figure 5 is a schematic interaction diagram of a communication method in an application scenario of an embodiment of the present application; Figure 6 is a schematic interaction diagram of a communication method in another application scenario of an embodiment of the present application; Figure 7 is a schematic interaction diagram of a communication method for another application scenario of an embodiment of the present application; Figure 8 is a schematic diagram of a method for determining a recognition result in an embodiment of the present application; Fig. 9 is a schematic diagram of another method for determining a recognition result in an embodiment of the present application; Fig.10 is another schematic diagram of a method for determining a recognition result in an embodiment of the present application; Fig.11 is a schematic block diagram of a communication device provided in an embodiment of the present application; Fig.12 It is another schematic block diagram of the communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0040] The technical solution 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), 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, the 5G mobile communication system may include non-standalone (NSA) and / or standalone (SA). The technical solution provided in this application can also be applied to future communication systems, such as the sixth generation (6G) mobile communication system, which is not limited in this application.

[0041] The embodiments of the present application are applied to a communication system supporting integrated sensing and communication (ISAC). In some embodiments, ISAC is an important application scenario of 6G.

[0042] Figure 1 1 is a schematic diagram of a communication system 100 applied in an embodiment of the present application. The communication system 100 may include a sensing node 110 and one or more first devices 120.

[0043] In the embodiment of the present application, the sensing node is a device with sensing and communication capabilities, or a sensing and communication integrated device. When the sensing function is activated, the sensing node covers the target area by sending a sensing measurement signal, and extracts the characteristics of each target in the target area by receiving an echo signal. The embodiment of the present application does not specifically limit the hardware form of the sensing node, and any device with the sensing function can be used as a sensing node.

[0044] The embodiment of the present application does not specifically limit the type of the first device. The first device may be a terminal device, or a core network device or other device that expects to obtain the recognition result of the first target. Based on the device type of the first device, the sensing node and the first device may interact using messages or signaling corresponding to the corresponding communication protocol.

[0045] In some embodiments, the sensing node 110 is an access network device or other device with sensing functions, and the first device 120 is a terminal device.

[0046] In other embodiments, the sensing node 110 is an access network device or other device with sensing functions, and the first device 120 is a core network device.

[0047] Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0048] The network device in this application may be an access network, a core network device (or) and other network-side devices. The access network device is sometimes also referred to as an access node. The access network device has a wireless transceiver function and is used to communicate with the terminal. The access network device includes but is not limited to the base station (base station) in the above-mentioned communication system, the evolved NodeB (eNodeB), the transmission reception point (TRP), the next generation NodeB (gNB) in the 5G mobile communication system, the access network device or the module of the access network device in the open access network (open RAN, ORAN) system, the satellite in the NTN communication system, the base station in the future mobile communication system or the access node in the WiFi system, etc. The access network device may also be a module or unit that can realize some functions of the base station. The access network device may be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (cloud radio access network, CRAN) scenario. Optionally, the access network device may also be a server, a wearable device, or a vehicle-mounted device. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU). Multiple access network devices in a communication system may be base stations of the same type or different types. A base station may communicate with a terminal or communicate with the terminal through a relay station. A terminal may communicate with multiple base stations in different access technologies. The embodiments of the present application do not limit the specific technology and specific device form adopted by the access network device.

[0049] The core network equipment is a general term for various functional entities used to manage users, data transmission, and network equipment configuration. The core network equipment may include one or more network elements. For example, in the 5G system, the core network equipment may include location management function (LMF), access and mobility management function (AMF), user plane function (UPF), and session management function (SMF). For another example, in the 6G system, the core network equipment may include other functions such as perception network elements, LMF, AMF, UPF, and SMF.

[0050] LMF is responsible for supporting different types of location services related to UE, including positioning of UE and transmitting 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 interact with RAN, such as ng-eNB or gNB, and UE. For example, LMF and ng-eNB or gNB interact with each other through the new radio positioning protocol annex (NRPPa) message, for example, to obtain the configuration information of the positioning reference signal (PRS), the sounding reference signal (SRS), the cell timing, the 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.

[0051] 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, the communication system includes a core network, base stations, relay nodes, and various types of terminal devices (e.g., smart phones, vehicles, driverless vehicles, smart screens, notebooks, routing devices, smart homes, drones, etc.).

[0052] A sensing node can be Figure 2 The first device may be a base station with integrated sensing function, or a terminal device with integrated sensing function (such as a drone), or a relay node with integrated sensing function, or other devices with sensing function. Figure 2 Any terminal device in.

[0053] 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.

[0054] In the embodiments of the present application, for example, Figure 3As shown, the sensing node at least includes a feature extraction module, a sensing speed compensation module and a target classification module. The feature extraction module is used to extract micro-Doppler features based on the received signal. The sensing speed compensation module is used to compensate the sensing speed of the traditional Doppler based on the micro-Doppler feature to improve the accuracy of the sensing speed. The target classification module is used to identify the classification of the target to be measured based on the micro-Doppler feature to obtain a more accurate target recognition result.

[0055] It should be understood that Figure 3 The modules shown in FIG. are described exemplarily, 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.

[0056] In the present application, the device for realizing the perception function (or the perception function and the communication function) may be a perception node, or may be a device capable of supporting the perception node to realize the function, such as a processor, a circuit, a chip, or a chip system, etc. The device may be installed in the perception node or connected to the perception node for use. In the technical solution provided in the present application, the technical solution provided in the present application is described by taking the device for realizing the perception function as a perception node as an example.

[0057] The terminal device in the present application may be a wireless terminal device capable of receiving network device scheduling and indication information. The wireless terminal device may be a device that provides voice and / or data connectivity to a user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device may also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. 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 wear, smart transportation, smart city, or satellite communication. The terminal may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, an aircraft (such as a drone, a helicopter, an airplane), a hot air balloon, a ship, a robot, a mechanical arm, or a smart home device, etc. The embodiments of the present application do not limit the form of the terminal device.

[0058] In the present application, the device for realizing the function of the terminal device may be the terminal device, or may be a device capable of supporting the terminal device to realize the function, such as a processor, a circuit, a chip, a chip system, etc. The device may be installed in the terminal device or connected to the terminal device for use. In the technical solution provided in the present application, the technical solution provided in the present application is described by taking the terminal device as an example in which the device for realizing the function of the terminal device is the terminal device.

[0059] 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 aircraft, 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 examples are not given one by one.

[0060] In actual applications, multiple network devices can collaborate to assist the terminal in achieving wireless access, and different network devices can respectively implement part of the functions of the base station. For example, the 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 set separately, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0061] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. Any unit in the CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module. CU (or CU-CP and CU-UP), DU and RU can implement different protocol layer functions.

[0062] To facilitate understanding of the embodiments of the present application, the terms involved in the present application are briefly explained first. Optionally, the explanation of some terms can also refer to the explanation in the third generation partnership project (3rd generation partnership project, 3GPP) standard protocol.

[0063] 1. Microscopic Doppler effect 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 for the channel modeling of high-speed moving objects remains to be further discussed. In an embodiment of the present application, in order to accurately identify the target, the micro-Doppler effect is taken as an important consideration.

[0064] 2. Micro-Doppler model The types of micro-Doppler include a vibration micro-Doppler type and a rotation micro-Doppler type. Accordingly, the micro-Doppler model includes a vibration micro-Doppler model and a rotation micro-Doppler model.

[0065] Exemplarily, the equation of the vibration micro-Doppler model satisfies the following formula: ; in, represents the vibration micro-Doppler shift, represents the frequency of the transmitted signal, is the vibration amplitude, is the speed of light, represents the azimuth of the radar 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.

[0066] Exemplarily, the equation of the rotational micro-Doppler model satisfies the following equation: ; 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 local coordinate system of the target, represents the radar line of sight direction unit vector, The identity matrix used to describe identity operations in coordinate transformations.

[0067] 3. Hausdorff distance Hausdorff distance is a measure of the similarity between two sets of points. Hausdorff distance is a definition of the distance between two sets of points. For example, Hausdorff distance evaluates the similarity between two sets by measuring the maximum distance (or minimum distance) from each point in one set to the nearest point in the other set. For the specific calculation method of Hausdorff distance, please refer to the description in the relevant technology, which will not be repeated here.

[0068] 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.

[0069] It should be understood that the technical terms in this application are only examples and not limitations. For example, as technology evolves, technical terms will also change. In the case of the same technical meaning, other technical terms should also apply to this application. In the current ISAC research topics, perception is generally achieved through the macroscopic Doppler effect. With the development of user needs, the current perception accuracy needs to be improved, and a specific solution is urgently needed to improve the accuracy of target recognition.

[0070] 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 an identification result of the first target based on the micro-Doppler characteristics of an echo signal of the received sensing measurement signal, wherein the identification result includes micro-motion information and / or target classification information, and a more refined identification result can be obtained, thereby improving the accuracy of target identification based on considering the micro-Doppler characteristics.

[0071] The scheme provided by the present application is described in detail below in conjunction with the corresponding flowchart. It can be understood that the schematic flowchart provided by the present application mainly uses different devices (e.g., sensing nodes, first devices) as examples of the execution subjects of the interactive schematic to illustrate the method, but the present application does not limit the execution subjects of the interactive schematic. For example, the device (e.g., sensing node, first device) in the schematic flowchart may 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.

[0072] Here, a unified explanation is given. 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.

[0073] Figure 4 4 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 term "terminal device" may also refer to a device in the terminal device (such as a processor, a chip, or a chip system, etc.); 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, a chip, or a chip system, etc.). 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, a chip, or a chip system, etc.); or, the sensing node may be a network device or a UE or other device with a sensing function; or, Figure 3 As shown in the sensor node. Figure 4 As shown, the method 400 includes the following steps: Step 410: The sensing node sends a sensing measurement signal to the first target.

[0074] 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.

[0075] 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.

[0076] The embodiment of the present application 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; receiving an echo signal of the sensing measurement signal to identify the target to be measured, thereby obtaining a recognition result.

[0077] In a possible implementation, the sensing node may actively configure a periodic sensing task to continuously perform sensing measurements. For example, the sensing node sends a sensing measurement signal in a certain area according to a certain period to detect one or more targets in the target area.

[0078] Alternatively, in another possible implementation, the sensing node may start the sensing function based on a request from another device. For example, the sensing node starts the sensing function when receiving a request message from a UE, wherein the request message specifies that the recognition result of one or more targets in a certain area is desired to be obtained.

[0079] 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.

[0080] Exemplarily, the perception measurement signal includes, but is not limited to, one or more of the following signals: a downlink positioning reference signal (DL-PRS), a channel state information-reference signal (CSI-RS).

[0081] It can be understood that after the sensing node sends the sensing measurement signal to the first target, it can receive the echo signal of the sensing measurement signal. The echo signal of the sensing measurement signal refers to the signal reflected back by the scatterer in the target space after the sensing node sends the sensing measurement signal in the target space. The scatterer refers to a target or object that can reflect or scatter the sensing measurement signal.

[0082] For example, the sensing node continuously sends downlink sensing measurement signals at a certain intersection to cover the entire intersection area, and continuously receives reflected signals from all targets in the intersection. Targets in the intersection include but are not limited to pedestrians, bicycles, various types of vehicles and other transportation vehicles.

[0083] The embodiments of the present application do not limit the perception mode adopted by the perception node. Exemplarily, the perception mode includes a self-transmitting and self-receiving mode and a self-transmitting and other-receiving mode. Taking the perception measurement signal as the perception signal as an example, the self-transmitting and self-receiving mode can be understood as the perception node itself sending the perception measurement signal and receiving the echo signal of the perception measurement signal by itself. The self-transmitting and other-receiving mode can be understood as the perception node itself only sending the perception signal to another perception node, and not receiving the echo signal of the perception signal, that is, the other perception node receives the signal after the perception signal sent by the perception node passes through the wireless channel.

[0084] Step 420, the sensing node obtains the recognition result of the first target, the recognition result of the first target is determined based on the micro-Doppler feature of the echo signal of the sensing measurement signal; the recognition result includes one or more of the following: micro-motion information, target classification information.

[0085] The micro-motion information is used to characterize the micro-motion characteristics of the first target. It should be noted that the description of "micro-motion characteristics" is introduced here to distinguish it from traditional Doppler characteristics. "Microscopic characteristics" can be understood as microscopic characteristics further determined on the basis of micro-Doppler characteristics or micro-Doppler information.

[0086] It should be understood that the terms "micro features" and "micro motion information" are only introduced for the convenience of description, and the names of these terms can also be equivalently replaced by other terms with the same function or meaning, and the embodiments of the present application do not specifically limit this.

[0087] The embodiment of the present application does not specifically limit the expression form of micro-motion information (or micro-motion features). 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 features.

[0088] The above 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, running and other motion states. For another example, the category classification 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.

[0089] 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 identification result of each target to be identified can refer to the method for determining the identification result of the first target.

[0090] Taking the first target as an example, the recognition result of the first target may include micro-motion information, target classification information, or both micro-motion information and target classification information. Figure 8 and Fig. 9 Describe the methods for determining micro-motion information and target classification information respectively.

[0091] 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.

[0092] The embodiment of the present application does not specifically limit the expression form or acquisition method of the micro-Doppler feature. Optionally, the micro-Doppler feature can be obtained by the following method: short-time Fourier transform (STFT) is performed on the echo signal to obtain a time-frequency spectrum, and the time-frequency spectrum is used to characterize the micro-Doppler feature of the echo signal.

[0093] Since micro-Doppler information is time-varying and non-stationary, STFT is selected to process the received echo signal to obtain a time-frequency spectrum. Micro-Doppler information (micro-Doppler characteristics) can be obtained based on the phase shift of the time-frequency spectrum. STFT is an extension of Fourier transform in the time-frequency domain, which is used to analyze non-stationary signals in the frequency domain that vary with time. STFT specifically processes the signal in segments through a window function.

[0094] 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.

[0095] For the explanation or implementation of STFT, please refer to the description in the relevant technology, and this application does not make any specific limitation on this.

[0096] 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 feature. After obtaining the micro-Doppler feature, 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.

[0097] After obtaining the recognition result of the target, the sensing node may save the recognition result of the target locally, or send the recognition result of the target to other network elements, which is not specifically limited.

[0098] Optionally, in step 430, the sensing node stores the recognition result of the first target, or updates the local information base based on the recognition result of the first target.

[0099] Exemplarily, if the recognition result of the first target is obtained for the first time, the perception node saves the recognition result of the first target locally; if it is not the first time to obtain the recognition result of the first target, 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 base.

[0100] The embodiment of the present application does not limit the content stored in the target information library. 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 library and updates it in real time.

[0101] 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.

[0102] The embodiment of the present application does not limit the execution order of step 430 and step 440. For example, step 430 is performed first and step 440 is performed later; or, step 430 is performed first and step 440 is performed later; or, step 430 and step 440 are performed simultaneously.

[0103] In the embodiment of the present application, the sensing node sends a sensing measurement signal, and after receiving the echo signal, uses the micro-Doppler feature of the echo signal to determine the recognition result of the first target. Compared with only considering the macroscopic Doppler effect, the embodiment of the present application considers the micro-Doppler feature and uses the micro-Doppler feature to obtain a more refined recognition result of the first target, thereby improving the accuracy of target recognition on the basis of considering the micro-Doppler feature.

[0104] For example, if the micro-Doppler effect is not considered in the channel modeling process of high-speed moving targets and a simplified channel model is used for modeling, the missed detection rate of the ISAC system will be greatly increased. However, by adopting the communication method of the embodiment of the present application, a more refined recognition result can be obtained by considering the micro-Doppler characteristics, which greatly reduces the missed detection rate of the ISAC system.

[0105] As mentioned above, the embodiments of the present application do not specifically limit the types of the sensing node and / or the first device. The following description is made in combination with different application scenarios.

[0106] Application scenario 1, assuming that the first device is a UE, the perception node is an access network device (with integrated perception function), and the access network device sends the recognition 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 the present application does not specifically limit the type of signaling used for the interaction between the access network device and the UE. Figure 5 The air interface signaling interaction between the UE and the access network device is described as an example.

[0107] refer to Figure 5 , Figure 5 FIG. 5 shows an example diagram of an interaction of a communication method 500 for application scenario 1. Figure 5 As shown, the communication method 500 at least includes the following steps: Steps 510 to 530 and Figure 4 Steps 410 to 430 shown in FIG. 1 are similar and will not be described in detail here.

[0108] Step 540: The access network device sends a first air interface signaling to the UE. Correspondingly, the UE receives the first air interface signaling. The first air interface signaling includes the identification result of the first target.

[0109] 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 the identification result of the first target.

[0110] Optionally, the second air interface signaling includes one or more of the following: information about the target area, information about the target to be identified. For example, the information about the target area includes a geographic location, or a road section name, etc. For another example, the information about the target to be identified includes image information of the target to be identified collected by the UE through a camera or other sensor, location information of the target to be identified, etc.

[0111] 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.

[0112] Exemplarily, the UE is an autonomous vehicle. When the perception system of the autonomous vehicle detects that there are multiple pedestrians and / or vehicles at the intersection ahead, the autonomous vehicle cannot accurately determine the direction and speed of movement of the pedestrians due to factors such as occlusion or long distance. The autonomous vehicle determines that it needs to obtain the identification results of pedestrians and / or vehicles in the intersection area based on the needs of safe driving. At this time, the autonomous vehicle sends air interface signaling to the access network equipment deployed near the intersection through the cellular vehicle-to-everything (C-V2X) communication protocol to request the measurement of one or more targets in a certain area. After obtaining the identification results of the target, the autonomous vehicle can more accurately understand the movement state or intention of the surrounding targets (including but not limited to pedestrians and vehicles), so as to plan the driving path more safely.

[0113] Application scenario 2, assuming that the first device is LMF, the sensing node is an access network device, and the access network device sends the identification result of the first target to LMF. LMF sends the identification result of the first target to UE. LMF and UE can interact through LPP messages.

[0114] refer to Figure 6 , Figure 6 FIG. 6 shows an example diagram of an interaction of a communication method 600 for application scenario 2. Figure 6 As shown, the communication method 600 at least includes the following steps: Steps 610 to 630 and Figure 4 Steps 410 to 430 shown in FIG. 1 are similar and will not be described in detail here.

[0115] Optionally, before step 610, the method 600 further includes: step 602, 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.

[0116] The embodiment of the present application does not specifically limit the triggering condition of step 602. The LMF may actively 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 the request of other devices.

[0117] 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.

[0118] 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 an identification result of a certain target.

[0119] Optionally, the third LPP message includes the geographic location information of the UE (such as the latitude and longitude information of the UE), and / or the geographic location information of the target area to be identified (such as the road name or latitude and longitude information of the target area), etc. In other words, the UE reports the relevant geographic location information to the LMF, and specifically requests the identification result of the target to be detected.

[0120] Step 640: The access network device sends a first LPP message to the LMF. Correspondingly, the LMF receives the first LPP message. The first LPP message includes the identification result of the first target.

[0121] 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.

[0122] Step 650: The LMF sends a first LPP message to the UE. Correspondingly, the UE receives the first LPP message. The first LPP message includes the identification result of the first target.

[0123] 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.

[0124] Above Figure 6 The interaction process shown in involves UE, access network equipment and LMF. Figure 6 In the interactive process shown, both UE and LMF can obtain the recognition result of the first target. Taking the case where UE is an autonomous vehicle, after receiving the recognition result of the first target, UE can more accurately understand the behavior or movement intention of surrounding targets (such as pedestrians and / or vehicles) in combination with the data collected by its own sensors (such as cameras, lidar, etc.), thereby improving the safety of the autonomous vehicle.

[0125] Application scenario 3, assuming that the first device is a smart car, the sensing node is a drone, and the drone sends the recognition result of the first target to the smart car (for example, an autonomous car). It should be understood that application scenario 3 is an example of a UE-UE interconnection scenario, that is, a UE with a sensing function can also detect the target and send the recognition result to another UE through a related message or signaling, and the embodiments of the present application are not limited to this.

[0126] refer to Figure 7 , Figure 7 FIG. 7 shows an example diagram of an interaction of a communication method 700 for application scenario 3. Figure 7 As shown, the communication method 700 at least includes the following steps: Steps 710 to 730 and Figure 4 Steps 410 to 430 shown in FIG. 1 are similar and will not be described in detail here.

[0127] Optionally, before step 710, the method 700 further includes: step 701, the smart car sends a second communication signal to the drone. Correspondingly, the drone receives the second communication signal. The second communication signal is used to request the recognition result of the first target.

[0128] Step 740: The drone sends a first communication signal to the smart car. Correspondingly, the smart car receives the first communication signal. The first communication signal includes the recognition result of the first target.

[0129] Step 750: The drone sends a third communication signaling to a network device (access network device or core network device). Correspondingly, the network device receives the third communication signaling. The third communication signaling includes the recognition result of the first target.

[0130] After the drone determines the recognition result of the first target through the method of the embodiment of the present application, the recognition result of the first target can be sent to the UE, or the recognition result of the first target can be sent 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 the route in time, or make other plans.

[0131] 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.

[0132] 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 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 classification module can be the one described above. Figure 3 The object classification module shown in .

[0133] It should be noted that the above-mentioned application scenarios 1 to 3 are merely exemplary descriptions, and the embodiments of the present application are not limited thereto.

[0134] 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, at least the following steps are included: 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.

[0135] The specific implementation of the time-spectrogram feature in step 810 can refer to the description of step 420 above, and will not be described here for brevity. On the basis of obtaining the time-spectrogram, further, micro-Doppler information of multiple scattering points can be extracted based on the features of the time-spectrogram.

[0136] The embodiment of the present application does not specifically limit the method of extracting micro-Doppler information based on the characteristics of the time-spectrogram. Optionally, the characteristics of the time-spectrogram are extracted using a peak search method to obtain micro-Doppler information of multiple scattering points.

[0137] Exemplarily, the micro-Doppler information of multiple scattering points satisfies the formula ,in, is the amplitude function of the spectrum graph when receiving the signal, Indicates the maximum value of the amplitude.

[0138] 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.

[0139] The sensing node can preliminarily determine the type of micro-Doppler according to the characteristics of the time-frequency spectrum in step 810. If the characteristics of the time-frequency spectrum conform to the characteristics of the vibration micro-Doppler, the vibration Doppler model equation is selected; if the characteristics of the time-frequency spectrum conform to the characteristics of the rotation micro-Doppler, the rotation Doppler model equation is selected. For the formula expression of the vibration Doppler model equation and the rotation Doppler model equation, please refer to the previous description, and for the sake of brevity, it will not be repeated here.

[0140] Furthermore, the sensing node can also estimate the frequency distribution range of the micro-Doppler according to the characteristics of the time-frequency spectrum. For example, the sensing node estimates the frequency distribution range of the vibration micro-Doppler or the frequency distribution range of the rotation micro-Doppler in combination with the prior information.

[0141] For example, the frequency distribution range of the micro-Doppler estimated here can be used as constraints.

[0142] Step 830: The sensing node determines an estimation parameter according to the echo signal of the sensing measurement signal, where the estimation parameter includes at least one or more of the following: a Doppler parameter, a delay parameter, and an angle parameter.

[0143] The purpose of introducing step 830 is to estimate one or more of the Doppler parameters, delay parameters and angle (for example, angle-of-arrival (AOA)) parameters of the scatterer in combination with the channel estimation technology. 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Exemplarily, the following steps 1) to 5) are used to estimate the parameters: 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 the multi-domain channel characteristics.

[0148] Step 2) Uniformly discretize the delay, Doppler and angle domains to construct the perception matrix ; where each atom corresponds to a discretized triple of time domain, Doppler and angle, which can be expressed as ; Step 3) Convert the channel estimation into a sparse recovery problem, that is, solve it through a sparse recovery algorithm. The signal modeled above can be expressed as the formula ; Step 4), by solving the sparse vector h, h includes L non-zero elements, corresponding to the effective scatterers; Step 5), by solving the optimization problem of minimizing sparsity, a sparse representation result is obtained; For example, solve the following formula: ; in, represents the perception matrix, Used to constrain and ensure solution sparsity; 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 .

[0149] In the scenario where the target moves at high speed, the micro-Doppler information of the target is extracted by using sparse-driven compressed sensing, which can recover sparse signals with fewer measurements, thereby extracting micro-Doppler information from fewer measurements, reducing the complexity of conventional STFT signal processing that requires a large number of signals. By solving the sparse vector, the motion state of each part of the target can be obtained, which makes it possible to achieve more refined target classification and / or target recognition in the future. Compared with the principal component analysis (PCA) and deep convolutional neural network (DCNN) methods, which are relatively less dependent on the amount of data, the above method of converting channel estimation into a sparse recovery problem is suitable for situations where less data is obtained; and the extracted time-frequency trajectory also directly corresponds to the energy distribution of the signal in the time-frequency domain, and the physical meaning is clearer.

[0150] It should be understood that the above-mentioned method for determining the estimated parameters is only an exemplary description, and the embodiments of the present application are not limited thereto. After the estimated parameters are obtained, it can be preliminarily determined whether the micro-Doppler model selected in step 820 is accurate.

[0151] 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.

[0152] 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 estimated parameters (for example, the angle parameters) based on the estimated parameters. ) to further determine and , the sensing node will and Substitute into the vibration micro-Doppler model equation, and replace 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 .

[0153] 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 rotational micro-Doppler model equation based on the estimated parameters (for example, the Doppler parameters , delay parameters , Angle parameters ) to further determine and , the sensing node will and Substitute it into the rotating micro-Doppler model equation, 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 rotating micro-Doppler model equation to preliminarily determine whether the rotating micro-Doppler model equation is accurate. Among them, You can use the angle parameter Sure. You can use the delay parameter and angle parameters Sure.

[0154] The embodiments of the present application do not specifically limit the method for determining whether the micro-Doppler model is accurate. For example, after substituting the estimated parameters and the parameters obtained based on the prior information into the micro-Doppler model, the characteristics or shape of the curve can be judged by the naked eye to see whether it conforms to the characteristics of the corresponding micro-Doppler model; if the direction of the curve shape conforms to or is the same as the selected micro-Doppler model, it means that the micro-Doppler model is accurate. Alternatively, those skilled in the art can also determine whether the micro-Doppler model used is accurate by means of other quantitative indicators.

[0155] Optionally, when it is determined that the used micro-Doppler model is accurate, the parameters of the model equation may be solved by curve fitting to obtain the micro-motion information.

[0156] Step 850: The sensing node determines the target function and performs model fitting.

[0157] 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.

[0158] Optionally, step 850 includes: determining an objective function according to 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 corresponding objective function is constructed in combination with the function expression of the micro-Doppler information obtained by the peak search method.

[0159] 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.

[0160] Exemplarily, 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.

[0161] Optionally, the parameters of the model equation are optimized by using a gradient descent method of adaptive learning to solve the values ​​of the parameters of the micro-Doppler model equation.

[0162] It can be understood that for different types of micro-Doppler model equations, solving the objective function needs to satisfy corresponding constraints. Exemplarily, 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. For example, The maximum value satisfied and minimum value , which can be the frequency distribution range estimated based on the time-spectrogram.

[0163] Exemplarily, when the first model equation is a rotating micro-Doppler model equation, the objective function satisfies the following constraints: ;

[0164] in, represents the angular velocity of rotation, , , Used to determine the initial rotation matrix.

[0165] Based on the above constraints, the parameters in the micro-Doppler model equation can be solved.

[0166] Step 860: The sensing node determines micro-motion information according to the first model equation and the estimated parameters. The micro-motion information can be characterized by micro-motion characteristic parameters.

[0167] 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.

[0168] The 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 rotation micro-Doppler model equation, the micro-motion characteristic parameters include a rotation angular velocity parameter. That is to say, for different micro-Doppler models, different micro-motion characteristic parameters can be obtained to characterize the micro-motion information.

[0169] Traditional Doppler includes both micro-Doppler and macro-Doppler. Due to the existence of micro-Doppler, the perceived speed obtained by using the traditional Doppler frequency shift is too large. In order to obtain a more accurate perceived speed, further, the embodiment of the present application uses the micro-Doppler perceived speed to compensate the traditional Doppler speed to improve the speed perception capability.

[0170] 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 process of steps 810 to 860 shown in the figure may be implemented in combination or independently, and there is no specific limitation on this.

[0171] Step 870: The sensing node determines the micro-Doppler frequency shift based on the micro-motion characteristic parameter and the first association relationship.

[0172] 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.

[0173] The embodiment of the present application does not specifically limit the correlation relationship satisfied between the micro-motion characteristic parameter and the micro-Doppler shift. Optionally, the correlation relationship between the micro-motion characteristic parameter and the micro-Doppler shift is a certain mathematical formula relationship.

[0174] Exemplarily, the first association relationship is a first model equation; the sensing node substitutes the micro-motion characteristic parameter into the first model equation to obtain the micro-Doppler frequency shift. It should be understood that the above description is only based on the first model equation as an example, and the embodiment of the present application does not specifically limit this.

[0175] The first model equation is the micro-Doppler model equation determined in step 820 above. Figure 8 After the micro-motion characteristic parameters are calculated by the method shown, the micro-motion characteristic parameters can be substituted into the corresponding micro-Doppler model equation to calculate the corresponding micro-Doppler frequency shift. For example, the micro-motion characteristic parameters are substituted into the vibration micro-Doppler model equation to obtain the vibration micro-Doppler frequency shift. For another example, the micro-motion characteristic parameters are substituted into the rotation micro-Doppler model equation to obtain the rotation micro-Doppler frequency shift.

[0176] Step 880: The sensing node uses the micro-Doppler sensing speed to compensate the first sensing speed to obtain a second sensing speed, wherein the micro-Doppler sensing speed is determined according to the micro-Doppler frequency shift.

[0177] After obtaining the micro-Doppler frequency shift, the sensing node can calculate the micro-Doppler sensing speed based on the micro-Doppler frequency shift.

[0178] Optionally, the micro-Doppler sensing speed satisfies the following formula: ; in, represents the micro-Doppler sensing speed, represents the micro-Doppler frequency shift, represents 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.

[0179] The first perceived speed is the perceived speed of the traditional Doppler. The traditional Doppler includes both micro-Doppler and macro-Doppler. In order to obtain a more accurate perceived speed, the embodiment of the present application uses the micro-Doppler perceived speed to compensate the traditional Doppler to obtain the compensated perceived speed, that is, the second perceived speed.

[0180] That is to say, after solving the micro-motion characteristic parameters, the micro-motion characteristic parameters can be substituted into the corresponding Doppler model equation to calculate the corresponding micro-Doppler frequency shift; then the micro-Doppler frequency shift is used to calculate the micro-Doppler perceived speed. After obtaining the micro-Doppler perceived speed, the micro-Doppler perceived speed is used to compensate the traditional Doppler speed to obtain the compensated perceived speed, thereby obtaining a more accurate perceived speed, which helps to improve the speed perception resolution.

[0181] Exemplarily, the second perceived speed satisfies the following formula: ; in, represents the second perceived speed (or the compensated perceived speed), represents the micro-Doppler sensing speed, represents the first perceived speed (or the conventional Doppler perceived speed, i.e. the perceived speed before compensation). It can be the parameter obtained by channel estimation in the previous text .

[0182] The present application embodiment is 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.

[0183] 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.

[0184] 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.

[0185] The following combination Fig. 9 Describes the target classification information in the recognition result of the first target determined by the perception node. Fig. 9 As shown, at least the following steps are included: 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 to obtain 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.

[0186] Exemplarily, the sensing node performs STFT processing on the echo signal to obtain a time-frequency spectrum diagram, and then extracts corresponding time-frequency domain information based on the time-frequency spectrum diagram.

[0187] 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 .

[0188] 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 method of the similarity parameter.

[0189] Optionally, the similarity parameter is the 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 in the previous text, which will not be repeated here.

[0190] 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.

[0191] Step 920: Determine a classification result of the first target according to the multiple similarity parameters.

[0192] 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).

[0193] Optionally, step 920 includes: determining a first central time-frequency trajectory based on multiple Hausdorff distances, wherein the first central time-frequency trajectory is the central time-frequency trajectory corresponding to a distance among the multiple Hausdorff distances that meets preset conditions; and determining the classification corresponding to the first central time-frequency trajectory as the classification result of the first target.

[0194] The embodiment of the present application does not specifically limit the specific implementation method 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.

[0195] Exemplarily, the first central time-frequency trajectory is the central time-frequency trajectory corresponding to the minimum Hausdorff distance among the multiple Hausdorff distances.

[0196] To facilitate understanding by those skilled in the art, the following is an explanation in conjunction with a specific formula. Optionally, the classification result of the first target satisfies the following formula: ; 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; Said Satisfy the following formula: ; is the time-frequency point in the echo signal arrive The Hausdorff distance of in, Satisfy the following formula: ; in, Represents a time-frequency point of the central time-frequency trajectory of the g-th action category.

[0197] 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.

[0198] Based on the processes shown in the above steps 910 and 920, 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.

[0199] Optionally, the sample library described in step 910 includes central time-frequency tracks of multiple categories, each category having a corresponding central time-frequency track. The sample library may be pre-established or trained. The sample library may include various types of categories, for example, including but not limited to: action categories, object categories, vehicle categories, etc.

[0200] This embodiment of the application does not specifically limit how to construct a sample library. Fig.10 As shown, the perception node constructs the central time-frequency trajectory of each category according to the following steps 101 to 104.

[0201] Step 101: for a first category among multiple categories, generate multiple training samples of the first category.

[0202] Exemplarily, the first category includes multiple training samples, which can be expressed as .

[0203] Step 102: extracting the time-frequency trajectory of each training sample from the plurality of training samples of the first category based on a compressed sensing orthogonal matching pursuit (OMP) algorithm.

[0204] Optionally, before step b, a time-frequency library Φ is constructed first.

[0205] For example, the Gabor dictionary is used to construct the time-frequency library Φ; Gabor atoms Defined as: , where the dictionary matrix Φ is composed of Gabor atoms: ; 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 combinations and construct a sparse representation that represents the time-frequency characteristics of the sample, namely the time-frequency trajectory .

[0206] Optionally, the expression formula of the above OMP algorithm is: ;in, represents sparsity, is a non-zero term.

[0207] 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.

[0208] Step 103: Perform 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.

[0209] 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 trajectories of multiple training samples Use K-means clustering algorithm to perform cluster analysis, iteratively divide the time-frequency points into K clusters, 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. Among them, Satisfy the formula ;in, Indicates the action category, Indicates The central time-frequency position and its intensity.

[0210] It should be understood that the above clustering algorithm may also be replaced by other algorithms with similar functions, and the embodiments of the present application are not limited to this.

[0211] Step 104, using the method of steps 101 to 103, generate central time-frequency trajectories of multiple categories.

[0212] Steps 101 to 103 are described by taking the first category as an example, and the embodiments of the present application are not limited thereto. That is, for other categories, the corresponding central time-frequency trajectory can also be determined by referring to the above steps.

[0213] Therefore, based on the above steps 101 to 104, the sample library can be constructed, thereby providing a basis for the subsequent target classification and recognition.

[0214] It should be noted that Fig.10 Steps 101 to 104 shown in FIG. 1 may be independent of Fig. 9 The method shown can also be implemented with Fig. 9 The methods shown are implemented in combination, and no specific limitation is given to this. For example, Fig.10 The method shown in also includes the following steps: Step 11: The sensing node receives the echo signal.

[0215] Step 12: The sensing node processes the echo signal through STFT transformation to map the echo signal to the time-frequency domain.

[0216] Step 13: The sensing node extracts the time-frequency trajectory based on the OMP algorithm.

[0217] In step 14, the perception node calculates the Hausdorff distance between the time-frequency trajectory and the central time-frequency trajectory of each category.

[0218] 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.

[0219] The above steps 11 to 15 can be understood as the above Fig. 9 An example of this, for related descriptions, please refer to the previous article Fig. 9 The description will not be elaborated here.

[0220] It is understandable that the above Fig.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 classify and identify targets, including the above steps 11 to 15.

[0221] It should be understood that Figures 1 to 10 The flowcharts or scene 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. Figures 1 to 10 The examples in can be equivalently transformed to obtain more implementation methods.

[0222] Combination of the above Figures 1 to 10 , describes in detail the communication method provided by the embodiment of the present application. Fig.11 and Fig.12 It should be understood that the communication device of the present application embodiment can execute various communication methods of the present application embodiment, that is, the specific working process of the following various products can refer to the corresponding process in the above method embodiment. In each of the above embodiments, the perception node may execute some or all of the steps in each embodiment; the UE may execute some or all of the steps in each embodiment; the core network device may execute some or all of the steps in each embodiment. These steps or operations are only examples, and the embodiments of the present application may also execute other operations or variations of various operations. In addition, the various steps may be executed in different orders as presented in the embodiments, and it may not be necessary to execute all of the operations in the embodiments of the present application. 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.

[0223] Fig.11 1 is a schematic block diagram of a communication device 1100 provided in an embodiment of the present application. Fig.11 As shown, the communication device 1100 may include a communication module 1120. The communication module 1120 may implement a corresponding communication function, which may be an internal communication function of the communication device 1100 or a communication function between the communication device 1100 and other devices. Optionally, the communication module 1120 may also be referred to as a communication interface or a transceiver module. Optionally, the communication device 1100 further includes a processing module 1110. The processing module 1110 may implement a corresponding processing function.

[0224] 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.

[0225] In a possible design, the communication device 1100 may correspond to the sensing node in the above method embodiment, or a component (such as a circuit, a chip, or a chip system, etc.) configured in the sensing node. The communication device 1100 may be used to execute the steps or processes executed by the sensing node in any of the above method embodiments.

[0226] For example, the communication module 1120 is used to send a perception measurement signal to the first target; 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.

[0227] 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.

[0228] 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.

[0229] Optionally, as an embodiment, the communication module 1120 is further used to send a first LPP message to a core network element, where the first LPP message includes an identification result of the first target.

[0230] 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.

[0231] 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 according to 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 according to the first model equation and estimated parameters, the micro-motion information being characterized by micro-motion characteristic parameters; wherein the estimated parameters are determined according to 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 rotation angular velocity parameters.

[0232] 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; Among them, the processing module 1110 is used to determine the micro-motion information according to 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 according to the first model equation, the estimated parameters and the micro-Doppler information.

[0233] Optionally, as an embodiment, 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: ;

[0234] in, represents the angular velocity of rotation, , , Used to determine the initial rotation matrix.

[0235] 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 association relationship; and use the micro-Doppler perceived speed to compensate the first perceived speed to obtain a second perceived speed, wherein the micro-Doppler perceived speed is determined based on the micro-Doppler frequency shift.

[0236] Optionally, as an embodiment, the second perceived 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, represents the frequency of the transmitted signal, Represents the speed of light.

[0237] 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; 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 of the multiple central time-frequency trajectories to obtain multiple similarity parameters, wherein the central time-frequency trajectory is the central 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; determining the classification result of the first target based on the multiple similarity parameters.

[0238] 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.

[0239] 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.

[0240] The above are only examples, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0241] Alternatively, in a possible design, the communication device 1100 may correspond to the UE in the above method embodiment, or a component (such as a circuit, a chip, or a chip system, etc.) configured in the UE. The communication device 1100 may be used to execute the steps or processes executed by the UE in any of the above method embodiments.

[0242] 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.

[0243] Optionally, as an embodiment, the communication module 1120 is used to send a first message, including: sending a third LPP message to a core network network element, the third LPP message being the first message; wherein the communication module 1120 is used to receive an 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.

[0244] Optionally, as an embodiment, the communication module 1120 is used to send a first message, including: sending a second signaling to an access network device, the second signaling being the first message; wherein the communication module 1120 is used to receive an identification result of the first target, including: receiving a first signaling from the access network device, the first signaling including the identification result of the first target.

[0245] The above are only examples, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0246] Alternatively, in a possible design, the communication device 1100 may correspond to the core network element (such as LMF) in the above method embodiment, or a component (such as a circuit, a chip or a chip system, etc.) configured in the core network element. 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.

[0247] The communication module 1120 is used 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; 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.

[0248] Optionally, as an embodiment, the communication module 1120 is further used to receive a first LPP message sent from an access network device, where the first LPP message includes the identification result.

[0249] 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.

[0250] The above are only examples, and for detailed steps or processes, please refer to the description of the aforementioned embodiments.

[0251] Fig.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, a chip system, or a processor, etc., for implementing the above method by a sensing node or a first device. The communication device 1200 may be used to implement the method described in the above method embodiment, and the details may refer to the description in the above method embodiment.

[0252] like Fig.12As shown, the communication device 1200 may include one or more processors 1210, which may also be referred to as a processing unit or a 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 the communication protocol and the 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 a software program, and process the data of the software program.

[0253] In an optional design, the processor 1210 may also store instructions and / or data, which can be executed by the processor 1210 so that the communication device 1200 executes the method described in the above method embodiment.

[0254] In another optional design, the communication device 1200 may include a communication interface 1220 for implementing the receiving and sending 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 sending functions may be separate or integrated. The above-mentioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or the above-mentioned transceiver circuit, interface, interface circuit, or transceiver may be used for transmitting or delivering signals.

[0255] Optionally, the communication device 1200 may include one or more memories 1230, on which instructions may be stored, and the instructions may be executed on the processor 1210, so that the communication device 1200 performs the method described in the above method embodiment. Optionally, data may also be stored in the memory 1230. Optionally, instructions and / or data may also be stored in the processor 1210. The processor 1210 and the memory 1230 may be provided separately or integrated together.

[0256] It should be understood that in a possible design, each step in the method embodiment provided by the present application can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a mature storage medium 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 a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0257] In one implementation, the communication device 1200 may correspond to the sensing node in the above method embodiment, and may be used to execute the various steps and / or processes executed by the sensing node in the above 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 method embodiment corresponding to the sensing node.

[0258] In another implementation, the communication device 1200 may correspond to the first device in the above method embodiment, and may be used to execute the various steps and / or processes executed by the first device in the above 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 method embodiment corresponding to the first device.

[0259] It should be understood that the above-mentioned 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 micro controller unit (MCU), a programmable logic device (PLD) or other integrated chips.

[0260] It can be understood that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can 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 can be a random access memory (RAM), which is used as an external cache. By way of example but 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.

[0261] According to the method provided in the embodiment of the present application, the present application also provides a chip system, which includes one or more processors for calling and running instructions stored in the memory from the memory, so that the method of the embodiment of the present application is executed. The chip system can be composed of a chip, or it can include a chip and other discrete devices.

[0262] 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.

[0263] 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).

[0264] 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, when the computer program code is run on a computer, the computer executes the various steps or processes executed by the perception node and the first device (such as UE, network device) in any of the aforementioned method embodiments.

[0265] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores a 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.

[0266] The computer-readable storage medium may be the volatile memory or the nonvolatile memory mentioned above, or may include both the volatile memory and the nonvolatile memory.

[0267] In the embodiments of the present application, each term and English abbreviation is provided for the convenience of description and shall not constitute any limitation to the present application. The present application does not exclude the possibility of defining other terms that can achieve the same or similar functions in existing or future protocols.

[0268] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by 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, all or part of the processes or functions described in the embodiments of the present application are generated.

[0269] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0270] 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.

[0271] In short, the above is only a preferred embodiment of the technical solution of this application, and is not intended to limit the protection scope of this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application should be included in the protection scope 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 according to 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.

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, characterized in that: 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 perception node to perform perception measurement.

6. The method according to any one of claims 1 to 4, characterized in that The recognition result includes the micro-motion information; and the obtaining of the recognition result of the first target includes: Determine a first model equation according to the micro-Doppler characteristics, where the first model equation is a vibration micro-Doppler model equation or a rotation micro-Doppler model equation; According to the first model equation and the estimated parameters, the micro-motion information is determined, and the micro-motion information is characterized by micro-motion characteristic parameters; wherein the estimated parameters are determined according to the echo signal, and the estimated parameters include at least one or more of the following: Doppler parameters, delay parameters and angle 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 in the target vibration direction, an azimuth angle parameter in 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.

7. The method according to claim 6, 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; Wherein, determining the micro-motion information according to the first model equation and the estimated parameters includes: The objective function is solved based on the gradient descent method to obtain the 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.

8. The method according to claim 7, 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.

9. The method according to claim 6, characterized in that The method further comprises: Determining a micro-Doppler frequency shift based on the micro-motion characteristic parameter and the first association relationship; The first perceived speed is compensated by using the 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.

10. The method according to claim 9, 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, represents the frequency of the transmitted signal, Represents the speed of light.

11. The method according to any one of claims 1 to 4, characterized in that The recognition result includes the target classification information, and the target classification information includes the classification result of the first target; The obtaining the recognition result of the first target includes: Determine a similarity parameter between the time-frequency trajectory of the echo signal and each of a plurality of central time-frequency trajectories to obtain a plurality of similarity parameters, wherein the central time-frequency trajectory is a central time-frequency trajectory corresponding to a training sample in a sample library, and the time-frequency trajectory of the echo signal is determined according to the time-frequency domain information of the echo signal; A classification result of the first target is determined according to the multiple similarity parameters.

12. The method according to claim 11, characterized in that The similarity parameter is Hausdorff distance; Wherein, determining the classification result of the first target according to the multiple similarity parameters includes: Determine a first central time-frequency trajectory according to a plurality of Hausdorff distances, wherein the first central time-frequency trajectory is a central time-frequency trajectory corresponding to a distance that satisfies a preset condition among the plurality of Hausdorff distances; The classification corresponding to the first central time-frequency trajectory is determined as the classification result of the first target.

13. The method according to claim 12, 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.

14. 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 a recognition result of a first target in the target space; Receive a recognition result of the first target, the recognition result comprising 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.

15. The method according to claim 14, characterized in that The sending of the first message comprises: Sending a third LPP message to a core network element, where the third LPP message is the first message; The receiving 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.

16. The method according to claim 14, characterized in that The sending of the first message comprises: Sending a second signaling to the access network device, where the second signaling is the first message; The receiving 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.

17. A communication method, characterized in that: Applied to a core network element, the method comprises: receiving a third LPP message from the UE, the third LPP message including first information, wherein the first information is used to request an identification result of the first target; The identification result of the first target is sent 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.

18. The method according to claim 17, 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.

19. The method according to claim 17 or 18, characterized in that The method further comprises: Send configuration information to the access network device, where the configuration information is used to configure the access network device to perform perception measurement.

20. A communication device, characterized in that: The device comprises at least one processor, wherein the at least one processor is 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 execute the method as claimed in any one of claims 1 to 13; or, the device is used to execute the method as claimed in any one of claims 14 to 16; or, the device is used to execute the method as claimed in any one of claims 17 to 19.

21. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the computer executes the method as claimed in any one of claims 1 to 13; or, the computer executes the method as claimed in any one of claims 14 to 16; or, the computer executes the method as claimed in any one of claims 17 to 19.

Images