Perception method and related device

Abstract

The invention provides a sensing method and a related device, which are beneficial to accurately determining the moving speed of a target object. The method comprises the following steps: a first sensing device receives a reflection signal from a target object to obtain a plurality of fuzzy velocities of the target object; the first sensing device sends a measurement result to a second sensing device, and the measurement result is used for indicating one or more fuzzy velocities in the plurality of fuzzy velocities and the respective corresponding probabilities thereof; the probability corresponding to each fuzzy speed in the one or more fuzzy speeds indicates the possibility that each fuzzy speed is the moving speed of the target object. And the second sensing device determines the fuzzy speed with the highest probability in the one or more fuzzy speeds as the moving speed of the target object.

Description

Technical Field

[0001] The present application relates to the field of perception, and in particular to a perception method and related devices. Background Art

[0002] In intelligent transportation systems, accurate and real-time vehicle speed information is crucial for ensuring traffic safety and improving efficiency. With the rapid development of wireless communication technology, sensing devices can leverage the propagation characteristics of wireless signals to measure the speed of targets (e.g., vehicles, drones, and pedestrians).

[0003] Currently, sensing devices rely on the phase difference between two measured signals to accurately determine the target's speed. This phase difference is within a range of ±180 degrees, and the device can only accurately determine the target's speed. If the phase difference exceeds this range, phase ambiguity occurs, resulting in multiple ambiguous speeds and an inability to accurately determine the target's speed. Summary of the Invention

[0004] The present application provides a sensing method and related devices, which are conducive to accurately determining the moving speed of a target object.

[0005] On the first aspect, a perception method is provided, which can be executed by a first perception device, which may be, for example, an access network device or a terminal device, or a component configured in the access network device or the terminal device (such as a processor, a chip, or a chip system, etc.), or a logic module or software that can realize all or part of the functions of the access network device or the terminal device. This application does not limit this.

[0006] The method includes: measuring a reflected signal from a target object to obtain multiple fuzzy speeds of the target object; and sending a measurement result, where the measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, wherein the probability corresponding to each of the one or more fuzzy speeds indicates the likelihood that each fuzzy speed is the moving speed of the target object.

[0007] For example, the probability corresponding to the first fuzzy speed indicates the possibility that the first fuzzy speed is the moving speed of the target object, and the first fuzzy speed is any one of the one or more fuzzy speeds.

[0008] When measuring the moving speed of the target object, if the phase difference between the reflected signal received this time and the reflected signal received last time exceeds the range of plus or minus 180 degrees, the first sensing device cannot accurately obtain the moving speed of the target object, but instead obtains multiple fuzzy speeds of the target object.

[0009] Based on the technical solution of the present application, the first perception device can send each of the one or more fuzzy speeds and its corresponding probability. Since the probability corresponding to each fuzzy speed indicates the possibility that each fuzzy speed is the moving speed of the target object, the moving speed of the target object can be accurately determined according to the probability corresponding to each fuzzy speed, thereby improving the perception performance.

[0010] In conjunction with the first aspect, in certain implementations of the first aspect, the measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, including: the measurement result is used to indicate the fuzzy speed with the highest probability among the multiple fuzzy speeds and its corresponding probability. This helps reduce signaling overhead.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, before transmitting the measurement result, the method further includes: determining at least one fuzzy speed consistent with the target's movement direction from the multiple fuzzy speeds; and determining a probability corresponding to each of the at least one fuzzy speed. The at least one fuzzy speed in this application refers to one or more fuzzy speeds indicated by the measurement result.

[0012] In the present application, after the first perception device selects the at least one fuzzy speed from the multiple fuzzy speeds, it determines the probability corresponding to each fuzzy speed in the at least one fuzzy speed. The number of the at least one fuzzy speed is less than or equal to the number of the multiple fuzzy speeds. This helps to reduce the amount of calculation of the first perception device when determining the probability corresponding to the fuzzy speed.

[0013] In combination with the first aspect, in certain implementations of the first aspect, before determining at least one blur speed consistent with the moving direction of the target object from the multiple blur speeds, the method also includes: determining the moving direction of the target object based on the positive or negative value of the difference between the first distance and the second distance, the first distance is the distance between the target object and the sensing device obtained in this measurement, the second distance is the distance between the target object and the sensing device obtained in the last measurement, and the moving direction is a direction away from the sensing device or a direction approaching the sensing device.

[0014] In combination with the first aspect, in certain implementations of the first aspect, determining the probability corresponding to each of the at least one fuzzy speed includes: determining a first moving speed of the target object, where the first moving speed is the predicted moving speed of the target object; and determining the probability corresponding to each of the at least one fuzzy speed based on a matching result between each of the at least one fuzzy speed and the first moving speed.

[0015] In combination with the first aspect, in certain implementations of the first aspect, determining the first moving speed of the target object includes: determining a first moving speed corresponding to a first distance based on first information, the first information being used to indicate a correspondence between at least one pair of distances and moving speeds, the distance being the distance between the target object and the sensing device, and the moving speed being the moving speed of the target object.

[0016] With reference to the first aspect, in certain implementations of the first aspect, the first information is a range Doppler spectrum.

[0017] In combination with the first aspect, in certain implementations of the first aspect, before determining the first moving speed corresponding to the first distance based on the first information, the method also includes: performing two Fourier transforms on the channel estimated after the perception signal is sent at the historical moment to obtain a range Doppler spectrum.

[0018] In combination with the first aspect, in certain implementations of the first aspect, determining the probability corresponding to each of the at least one fuzzy speed includes: predicting at least one position of the target object corresponding to the at least one fuzzy speed based on the point cloud obtained from historical measurements; predicting the first position of the target object through Kalman filtering; and determining the probability of the fuzzy speed corresponding to each of the at least one position based on the matching result of each of the at least one position with the first position.

[0019] In combination with the first aspect, in certain implementations of the first aspect, determining the probability corresponding to each of the at least one fuzzy speed includes: determining, based on the at least one fuzzy speed, at least one estimated value of the Doppler phase deviation of the perception signal on the receiving antenna of the perception device, the at least one estimated value of the Doppler phase deviation corresponding one-to-one to the at least one fuzzy speed; determining, based on the at least one estimated value of the Doppler phase deviation, at least one estimated value of the incident angle of the perception signal on the receiving antenna of the perception device, the at least one estimated value of the incident angle corresponding one-to-one to the at least one estimated value of the Doppler phase deviation; determining, based on a matching result between each of the at least one estimated value of the incident angle and the incident angle on the receiving antenna obtained from historical measurements, the probability of the fuzzy speed corresponding to each of the at least one estimated value of the incident angle.

[0020] On the second aspect, a perception method is provided, which can be executed by a second perception device, which may be, for example, a location management function (LMF) network element or a sensing management function (SMF) network element, or a component configured in the LMF or SMF (such as a processor, chip, or chip system, etc.), or a logic module or software that can implement all or part of the LMF or SMF functions. This application does not limit this.

[0021] The method includes: receiving a measurement result, the measurement result being used to indicate one or more fuzzy speeds among a plurality of fuzzy speeds and their corresponding probabilities, the probability corresponding to each of the one or more fuzzy speeds indicating the likelihood that each fuzzy speed is the moving speed of the target object; and determining the fuzzy speed with the highest probability among the one or more fuzzy speeds as the moving speed of the target object.

[0022] In the present application, the second perception device can receive each of the one or more fuzzy speeds and its corresponding probability. The probability corresponding to each fuzzy speed indicates the possibility that each fuzzy speed is the moving speed of the target object. In this way, the second perception device can accurately determine the moving speed of the target object based on the probability corresponding to each fuzzy speed, thereby improving the perception performance.

[0023] In conjunction with the second aspect, in certain implementations of the second aspect, the measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, including: the measurement result is used to indicate the fuzzy speed with the highest probability among the multiple fuzzy speeds and its corresponding probability. This helps reduce signaling overhead.

[0024] In a third aspect, a sensing device is provided, comprising: a module for executing the method in any possible implementation of any of the above aspects. Specifically, the device comprises a module for executing the method in any possible implementation of any of the above aspects.

[0025] In one design, the device may include a module corresponding to each of the methods / operations / steps / actions described in any of the above aspects. The module may be a hardware circuit, software, or a combination of hardware circuit and software.

[0026] In another design, the device is a communication chip, which may include an input circuit or interface for sending information or data, and an output circuit or interface for receiving information or data.

[0027] In another design, the apparatus includes a transmitter for sending information or data and a receiver for receiving information or data.

[0028] In another design, the device is used to execute the method in any possible implementation of any of the above aspects, and the device can be configured in an access network device, a terminal device, an LMF or an SMF.

[0029] In a fourth aspect, a perception device is provided, comprising: one or more processors, the one or more processors being used to call and run a computer program from a memory, so that the device executes a method in any possible implementation of any of the above aspects.

[0030] Optionally, the device further comprises a memory, which can be used to store instructions and data. The memory is coupled to the processor, and when the processor executes the instructions stored in the memory, the method described in the above aspects can be implemented.

[0031] Optionally, the device further includes: a transmitter (emitter) and a receiver (receiver), and the transmitter and the receiver can be separately provided or integrated together, and are referred to as a transceiver (transceiver).

[0032] In a fifth aspect, a computer program product is provided, which includes: a computer program (also referred to as code, or instructions), which, when executed, enables a computer to execute a method in any possible implementation of any of the above aspects.

[0033] In a sixth aspect, a computer-readable storage medium is provided, which stores a computer program (also referred to as code, or instructions) which, when run on a computer, enables the computer to execute a method in any possible implementation of any of the above aspects.

[0034] In the seventh aspect, the present application provides a chip system comprising at least one processor for supporting the functions involved in implementing any possible implementation of any of the above aspects, such as receiving or processing the data involved in the above method.

[0035] In one possible design, the chip system further includes a memory, which is used to store program instructions and data, and the memory is located inside or outside the processor.

[0036] Optionally, the chip system may consist of a chip, or may include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1A and Figure 1B is a schematic diagram of Doppler shift;

[0038] Figure 2 It is a schematic diagram of the principle of speed measurement;

[0039] Figure 3 It is a schematic diagram of phase change;

[0040] Figure 4 and Figure 5 Schematic diagram of a perception architecture based on NG-RAN applicable to an embodiment of the present application;

[0041] Figure 6 is a schematic flow chart of a perception method provided in an embodiment of the present application;

[0042] 7A to 7C Schematic diagram of the relationship between phase change and blur speed provided in an embodiment of the present application;

[0043] Figure 8 is a schematic flow chart of another sensing method provided in an embodiment of the present application;

[0044] Figure 9 is a schematic flow chart of another sensing method provided in an embodiment of the present application;

[0045] Figure 10 and Figure 11 It is a schematic block diagram of the sensing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solution in this application will be described below with reference to the accompanying drawings.

[0047] Before introducing the sensing method and related devices provided in the embodiments of the present application, the following points are explained.

[0048] First, in the embodiments described below, various terms and abbreviations, such as target, fuzzy velocity, Doppler phase deviation, and SMF, are provided for ease of description and should not be construed as limiting this application. This application does not preclude the possibility of defining other terms in existing or future protocols that can achieve the same or similar functions.

[0049] Second, the first, second and various numerical numbers in the embodiments shown below are only used for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0050] Third, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c, where a, b, c can be single or multiple.

[0051] Fourth, “sending” and “receiving” in this application indicate the direction of signal transmission. For example, “sending the measurement result to the second sensing device” can be understood as the destination end of the measurement result is the second sensing device, which can include direct sending through the air interface, and also includes indirect sending through the air interface by other units or modules. “Receiving the measurement result from the first sensing device” can be understood as the source end of the measurement result is the first sensing device, which can include direct receiving from the first sensing device through the air interface, and also includes indirect receiving from the access network device through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

[0052] In other words, sending and receiving can be carried out between devices, for example, between terminal equipment and access network equipment; or it can be carried out within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0053] The following is an introduction to the relevant technologies and concepts involved in this application.

[0054] 1. Velocity ambiguity: This refers to the phenomenon in which, when a pulse Doppler radar operates at low or medium repetition rates, the measured target's velocity becomes confused due to spectral overlap, making it difficult to discern the target's true velocity. Specifically, if a target moves too far between pulses, causing its true phase shift to exceed 180 degrees, it will be assigned a phase shift value less than 180 degrees. The velocity corresponding to this phase shift will also be less than the maximum unambiguous velocity, resulting in an incorrect velocity. This is known as velocity ambiguity.

[0055] 2. Maximum unambiguous velocity: The maximum pulse phase shift from one pulse to the next that can be measured by pulse Doppler radar is 180°. The radial velocity value of the target object corresponding to the 180° pulse phase shift is the maximum unambiguous velocity.

[0056] 3. Doppler Effect: The Doppler effect is a physical phenomenon in which the frequency of the echo signal received by the observer changes when there is relative motion between the wave source (such as electromagnetic waves emitted by a base station) and the observer (such as a moving vehicle). This phenomenon applies not only to sound waves but also to electromagnetic waves, including radio waves.

[0057] In intelligent transportation systems, accurate, real-time vehicle speed information is key to ensuring traffic safety and improving traffic efficiency. While traditional optical speed measurement methods work well on sunny days and during the day, their effectiveness is severely impacted at night, during inclement weather, or in low-light conditions. Furthermore, while radar speed measurement methods offer many advantages, including non-contact, real-time, and high accuracy, they also have drawbacks and limitations, such as high cost, weather conditions, and multipath interference. With the rapid development of wireless communication technology, base stations, as a crucial component of wireless communication networks, offer excellent advantages for speed measurement due to their wide coverage and dense deployment.

[0058] Speed ​​measurement technology using base stations as sensing devices primarily utilizes the characteristics of wireless signal propagation. By measuring the time difference or phase difference between signal transmission and reception, the relative speed between the target object (e.g., vehicle, pedestrian) and the base station is calculated. This speed measurement method can not only be performed without direct contact with the target object, but can also be combined with the location information of the base station to achieve three-dimensional positioning of the target object. Taking the target object as an example, vehicle sensing and speed measurement based on the base station network has the following advantages:

[0059] (1) Non-contact: Base station speed measurement technology does not require any equipment or sensors to be installed on the vehicle. Instead, it obtains speed information by measuring the interaction between the wireless signal and the target vehicle. This non-contact feature makes the speed measurement process simpler and more efficient, and does not interfere with the normal operation of the vehicle.

[0060] (2) Wide coverage: Base stations, as the infrastructure of wireless communication networks, are usually deployed to cover the entire city or a specific area. This means that base station speed measurement technology can provide continuous speed measurement services over a wide geographical range, providing comprehensive vehicle speed information for intelligent transportation systems.

[0061] (3) Real-time: Base station speed measurement technology can obtain vehicle speed data in real time and transmit this information to the traffic management system in a timely manner. This real-time feature enables traffic management departments to respond quickly to problems such as traffic congestion and speeding, thereby improving traffic management efficiency and safety.

[0062] (4) High Precision: Through advanced signal processing technology and algorithms, base station speed measurement technology can achieve high-precision speed measurement. Compared with some traditional speed measurement methods, base station speed measurement technology can provide more accurate and reliable speed data, providing more valuable reference information for traffic management and planning.

[0063] (5) Scalability: Base station speed measurement technology can be seamlessly integrated with existing wireless communication networks, and its performance and functionality can be further enhanced with the continuous development of wireless communication technology. In addition, combined with other traffic sensing technologies (such as cameras and radars), base station speed measurement technology can also achieve richer traffic information acquisition and analysis.

[0064] Currently, base station speed measurement technologies include those based on the Doppler effect. This type of base station speed measurement utilizes the Doppler effect. When a vehicle passes a base station, the frequency of the radio signal reflected by the vehicle changes. By measuring this frequency change, the vehicle's speed can be calculated. During speed measurement, the frequency of the electromagnetic waves reflected by the vehicle increases as the vehicle approaches the base station, while the frequency decreases as the vehicle moves away from the base station. This is because when the vehicle approaches the base station, the electromagnetic waves reflected by the vehicle are compressed during propagation, resulting in a shorter wavelength and a higher frequency. Conversely, when the vehicle moves away from the base station, the electromagnetic waves reflected by the vehicle are stretched during propagation, resulting in a longer wavelength and a lower frequency.

[0065] The base station can calculate the relative speed between the vehicle and the base station by measuring this frequency change (i.e., Doppler shift). Specifically, the Doppler shift is proportional to the relative speed between the wave source and the observer.

[0066] Figure 1A and Figure 1B This is a schematic diagram of Doppler shift.

[0067] See also Figure 1A As shown in the figure, assuming that the target object does not move, the initial distance between the transmitter and the target object is known to be R0, and the transmission signal of the transmitter is expressed as:

[0068] x s (t) = A t sin(2πf t t)

[0069] Among them, f t is the transmitting frequency, A t is the amplitude of the transmitted signal. Assuming that the flight delay from the emission of the transmitted signal to the reception of the reflected signal from the target is τ, the reflected signal can be expressed as:

[0070] x r (t) = A r sin(2πfr t+φ)=A r sin(2πf t (t-τ))

[0071] Furthermore, when the distance between the emission source and the target object is R0, the reflected signal can be expressed as:

[0072]

[0073] Among them, f r is the reflection frequency, A r is the amplitude of the reflected signal received by the transmitting source.

[0074] See also Figure 1B As shown in Figure 2, when the transmitting source and the target object move relative to each other, the propagation delay from the transmission signal to the reception of the reflected signal is Then the reflected signal can be expressed as:

[0075]

[0076] Among them, v r is the moving speed of the target. Based on the transmitted signal and the transmitted signal, the frequency difference, that is, the Doppler frequency shift Δf, can be obtained:

[0077]

[0078] In one current speed measurement method, a transmitting source can calculate the moving speed of a target object based on the phase difference between two transmitted signals.

[0079] Figure 2 It is a schematic diagram of the speed measurement principle, such as Figure 2 As shown, assuming that the transmitter sends the signal twice (such as Figure 2 The time interval between signal 1 and signal 2 is T c , the transmitter can measure the phase difference Δφ between the two signal peaks. The phase difference Δφ corresponds to the movement of the target. Assuming the moving speed of the object is v, the target is within the interval T c The distance traveled during this period is vT c , then the phase difference Δφ can be expressed as:

[0080]

[0081] Based on the phase difference Δφ, the moving speed of the target can be expressed as:

[0082]

[0083] Based on the above formula for calculating the target's moving speed, we can see that speed measurement depends on the measurement of phase difference, and accurate speed measurement can only be achieved when the phase difference is within the range of ±180 degrees. Figure 3 As shown in the schematic diagram of phase change, if the phase change in the clockwise or counterclockwise direction exceeds 180 degrees, phase ambiguity will occur, which will also cause velocity ambiguity.

[0084] Therefore, the phase change, or phase difference, needs to satisfy: |Δφ| < π. According to the above formula for calculating the phase difference, we can get:

[0085]

[0086] Then we can get:

[0087]

[0088] Therefore, the maximum unambiguous speed is When the actual speed of the target exceeds this value, that is, when the phase difference in the clockwise or counterclockwise direction exceeds the range of plus or minus 180 degrees, speed ambiguity will occur, affecting the performance of speed measurement.

[0089] In view of this, an embodiment of the present application provides a perception method, where a first perception device can report one or more fuzzy speeds among multiple fuzzy speeds and their corresponding probabilities to a second perception device, which is conducive to accurately determining the moving speed of the target object.

[0090] The first sensing device in this application is a device that receives a reflected signal. In one design, the first sensing device can also serve as a transmitter to transmit a sensing signal, and the first sensing device receives a reflected signal from a target object. In this design, the first sensing device transmits and receives the signal itself, thereby sensing the target object's movement speed. In another design, another sensing device serves as a transmitter to transmit a sensing signal, and the first sensing device receives the target object's reflected signal to sense the target object's movement speed. In this design, sending the sensing signal and receiving the reflected signal are implemented by different sensing devices.

[0091] In the embodiment of the present application, the first sensing device is, for example, an access network device or a terminal device. The second sensing device is, for example, a sensing management function network element, which is used to reconstruct an environment map based on sensing information.

[0092] The access network device provided in the embodiment of the present application can be a base station, a node B, an evolved node B (eNodeB or eNB), a transmission reception point (TRP), a next generation node B (gNB) in 5G or NR, an access network device in an open radio access network (O-RAN or open RAN), or a next generation base station in the sixth generation mobile communication technology (6G). Alternatively, the access network device can also be a satellite base station in a non-terrestrial network (NTN) communication network, or a base station in a future mobile communication system, or an access node in a wireless fidelity (Wi-Fi) system. Alternatively, the access network device can also be a module or unit that performs part of the functions of a base station, for example, a centralized unit (CU) or a distributed unit (DU), and the functions of the CU can be implemented by one entity or by different entities. For example, the functions of the CU can be further divided, for example, the control plane (CP) and the user plane (UP) can be separated, that is, the control plane of the CU (CU-CP) and the user plane of the CU (CU-UP). The access network device can be a satellite base station or a macro base station. The access network device can also be a micro base station or an indoor station, or a relay node or a host node. The specific technology and specific device form used by the access network device are not limited in this application.

[0093] The terminal device provided in the embodiments of the present application may also be referred to as a terminal, user equipment (UE), mobile station, or mobile terminal. The terminal device can be widely used in various scenarios for communication. Such scenarios include, but are not limited to, at least one of the following: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), massive machine-type communications (mMTC), device-to-device (D2D), vehicle-to-everything (V2X), machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearable, smart transportation, or smart city. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver function, wearable device, vehicle, helicopter, airplane, drone, ship, robot, robotic arm, or smart home device. This application does not limit the specific technology and specific device form used by the terminal device.

[0094] The terminal device and / or access network device can be fixed or movable. The terminal device and / or access network device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; or can be deployed on the water surface; or can be deployed on aircraft, balloons and artificial satellites in the air. This application does not limit the environment / scenario in which the terminal device and / or access network device are located. The terminal device and / or access network device can be deployed in the same or different environments / scenarios, for example, the terminal device and the access network device are deployed on land at the same time; or, the terminal device is deployed on land and the access network device is deployed on the water surface, etc., and examples are not given one by one here. This application does not limit the communication method between the terminal device and the access network device.

[0095] In the embodiments of the present application, the terminal device and the access network device can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (for example, a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. The present application does not limit the specific forms of the terminal device and the access network device.

[0096] Figure 4This is a schematic diagram of a perception architecture based on the next generation radio access network (NG-RAN) applicable to the embodiment of the present application. Figure 4 As shown in the figure, the functional entities of the 5G core network include LMF, SMF and access and mobility function (AMF). Optionally, the 5G core network also includes an evolved serving mobile location center (E-SMLC) and a service location protocol (SLP).

[0097] LMF is a network element, module or component that provides positioning functions in the 5G core network.

[0098] SMF is a network element, module or component in the 5G core network that provides positioning functions for terminal devices.

[0099] The AMF is a network element, module, or component in the 5G core network that provides access management functions. The AMF communicates with base stations via the NG-C interface. The AMF acts as a router between base stations and the LMF in the NG-RAN. The AMF and LMF communicate via the NLs interface and receive awareness service requests from other network elements regarding specific terminal devices. The AMF sends these awareness service requests to the LMF network element. The LMF is responsible for processing the received positioning service requests and initiating the relevant positioning procedures.

[0100] NG-RAN is responsible for sending and receiving positioning reference signals and obtaining related measurement information. The RAN nodes deployed in NG-RAN include the next-generation evolved Node B (ng-eNB) and gNB. The ng-eNB is connected to the upgraded 4G base station of the 5G core network. The ng-eNB serves as the primary RAN node, and the gNB serves as the secondary RAN node. The interface between the ng-eNB and gNB is based on the 5G protocol, referred to as Xn. The UE (5G device) receives data from both the ng-eNB and gNB.

[0101] It should be understood that Figure 4 This is only a schematic diagram of a possible positioning architecture. The positioning method of the embodiment of the present application can also be applied to other positioning architectures based on the 5G dual connectivity (DC) mode. For example, a positioning architecture based on the NR-DC mode, in which the RAN nodes deployed by the NG-RAN are all gNBs.

[0102] In another possible design, see Figure 5 The perception architecture diagram shown in Figure 4 The difference is that LMF and SMF are the same network element, which can serve as both a positioning management function network element and a perception management function network element.

[0103] The following will be combined Figures 6 to 9 Introducing the perception method of the embodiment of the present application. In the following embodiments, the first perception device may be a communication device having communication and perception functions, such as an access network device (such as the above Figure 4 or Figure 5 The ng-eNB or gNB in ​​the perception architecture shown in FIG, the communication device is, for example, a terminal device (such as the above Figure 4 or Figure 5 The second sensing device may be a sensing management function network element (e.g., Figure 4 or Figure 5 SMF in the perception architecture shown).

[0104] Figure 6 6 is a schematic flow chart of a sensing method 600 provided in an embodiment of the present application. The method 600 includes S601 to S603, and the specific steps are as follows:

[0105] S601: A first sensing device receives a reflection signal from a target object and obtains a plurality of fuzzy velocities of the target object.

[0106] In the embodiments of the present application, the target object is, for example, a vehicle or pedestrian in the environment. The reflected signal from the target object refers to a signal reflected by the target object, and the signal reflected by the target object is, for example, a perception signal. The transmitting source can emit the perception signal, and the first perception device can receive the perception signal reflected by the target object.

[0107] The transmission source of the perception signal is, for example, the first perception device or other devices, which is not limited in the embodiments of the present application.

[0108] It should be noted that the target's reflection signal in response to a particular sensing signal at a particular moment can be received by at least one sensing device, which can then sense the target's movement speed. The first sensing device is one of the at least one sensing device. Upon receiving the reflection signal from the target, the other sensing devices can perform similar steps and / or processes as the first sensing device.

[0109] When the phase difference between the two observed signals exceeds the range of ±180 degrees, velocity ambiguity occurs, resulting in multiple ambiguous velocities of the target object. The multiple ambiguous velocities of the target object are multiple possible movement speeds of the target object.

[0110] Assume that the frequency is 26 GHz, the wavelength is 0.01 m, and the subcarrier spacing is 60 kHz. The time interval T between two signal transmissions from a given transmitter is c In this case, referring to the above formula for calculating the maximum unambiguous speed, the maximum unambiguous speed is 45 km / h. Based on the above formula for calculating the moving speed of the target object, multiple fuzzy speeds of the target object will be obtained. For example, the set of fuzzy speeds is: {-30 km / h, 15 km / h, 60 km / h, 105 km / h, ...}, and the difference between two adjacent fuzzy speeds in the set is the value of the maximum unambiguous speed.

[0111] 7A to 7C Schematic diagram of the relationship between phase change and blur speed shown in the embodiment of the present application. Figure 7A When the counterclockwise phase change is greater than 180 degrees, the target's blur speed is -30 km / h. Figure 7B and Figure 7C ,When the phase change in the clockwise direction is greater than 180 degrees, the ,obtained blur speeds of the target include 15km / h and 60km / h.

[0112] In one possible implementation, the maximum fuzzy speed in the fuzzy speed set is less than or equal to a speed threshold. For example, if the speed threshold is 180 km / h, the fuzzy speed set is: {-30 km / h, 15 km / h, 60 km / h, 105 km / h, 150 km / h}. In another example, if the speed threshold is 120 km / h, the fuzzy speed set is: {-30 km / h, 15 km / h, 60 km / h, 105 km / h}.

[0113] S602: The first sensing device transmits a measurement result to the second sensing device. The measurement result indicates one or more fuzzy speeds among the plurality of fuzzy speeds and their corresponding probabilities. The probability corresponding to each of the one or more fuzzy speeds indicates the likelihood that each fuzzy speed is the moving speed of the target object. In response, the second sensing device receives the measurement result.

[0114] When the first sensing device transmits the target object's speed to the second sensing device, if the phase difference between the two observed signals exceeds a range of plus or minus 180 degrees, the first sensing device cannot accurately measure the target object's speed and instead measures multiple possible speeds, i.e., multiple ambiguous speeds. Therefore, the first sensing device can report the probability corresponding to each of the one or more ambiguous speeds to the second sensing device, using the probability corresponding to each ambiguous speed to indicate the likelihood that each ambiguous speed is the target object's speed.

[0115] In the embodiment of the present application, the first sensing device measures multiple fuzzy speeds. However, when sending the measurement result to the second sensing device, the first sensing device can pre-screen one or more fuzzy speeds that meet the conditions from the multiple fuzzy speeds, then determine the probability corresponding to each of the one or more fuzzy speeds, and send each fuzzy speed and its corresponding probability to the second sensing device. This can reduce signaling overhead.

[0116] S603: The second perception device determines the fuzzy speed with the highest probability among the one or more fuzzy speeds as the moving speed of the target object.

[0117] After receiving the measurement result, the second sensing device can determine the moving speed of the target object based on the probability corresponding to each fuzzy speed. For example, the fuzzy speed with the highest probability is determined as the moving speed of the target object.

[0118] For example, if the probability corresponding to the blur speed V1 is 0.9, the probability corresponding to the blur speed V2 is 0.3, and the probability corresponding to the blur speed V3 is 0.2, the second sensing device determines that the blur speed V1 is the moving speed of the target object.

[0119] If multiple sensing devices receive the reflected signal from the target, each of these sensing devices will obtain the multiple fuzzy velocities and transmit the measurement results to the second sensing device. In other words, the second sensing device can obtain multiple measurement results, each of which indicates the probability corresponding to one or more fuzzy velocities. The second sensing device can then combine these multiple measurement results to determine the target's moving speed.

[0120] It should be understood that the set of blurred velocities of the target object obtained by at least one sensing device is the same. For example, the set of blurred velocities obtained by sensing device 1 includes V1, V2, and V3, and the set of blurred velocities obtained by sensing device 2 also includes V1, V2, and V3. However, the probabilities corresponding to V1, V2, and V3 obtained by sensing devices 1 and 2 may differ. In this case, the probabilities corresponding to the blurred velocities reported by at least one sensing device must be combined to determine the target's moving speed.

[0121] For example, if Perception Device 1 indicates that the probability corresponding to blurred velocity V1 is 0.9, the probability corresponding to blurred velocity V2 is 0.3, and the probability corresponding to blurred velocity V3 is 0.2, and Perception Device 2 indicates that the probability corresponding to blurred velocity V1 is 0.8, the probability corresponding to blurred velocity V2 is 0.4, and the probability corresponding to blurred velocity V3 is 0.2. As can be seen, the probabilities of blurred velocity V1 indicated by both Perception Devices 1 and 2 are relatively high, so the second Perception Device can determine blurred velocity V1 as the moving velocity of the target object.

[0122] For another example, if sensing device 1 indicates that the probability corresponding to blurred velocity V1 is 0.9, the probability corresponding to blurred velocity V2 is 0.6, and the probability corresponding to blurred velocity V3 is 0.2, and sensing device 2 indicates that the probability corresponding to blurred velocity V1 is 0.5, the probability corresponding to blurred velocity V2 is 0.7, and the probability corresponding to blurred velocity V3 is 0.4. As can be seen, the probabilities of the same blurred velocity indicated by different sensing devices may vary. Therefore, the second sensing device can calculate the average of the different probabilities for the same blurred velocity. For example, if the average of the two probabilities of blurred velocity V1 indicated by sensing devices 1 and 2 is 0.7, the probability corresponding to blurred velocity V1 can be re-determined as 0.7. Similarly, the second sensing device can re-determine the probability corresponding to blurred velocity V2 as 0.65 and the probability corresponding to blurred velocity V3 as 0.3. Furthermore, the second sensing device can determine the blurred velocity corresponding to the maximum of the three re-determined probabilities as the moving velocity of the target object.

[0123] Based on the technical solution described in the above method 600, the first perception device can send one or more fuzzy speeds and the probability corresponding to each of the one or more fuzzy speeds to the second perception device. In this way, the second perception device can accurately determine the moving speed of the target object based on the probability corresponding to each fuzzy speed, thereby improving the perception performance.

[0124] As an optional embodiment, S602 includes: the measurement result being used to indicate a fuzzy speed with the highest probability among the multiple fuzzy speeds and its corresponding probability. In this embodiment of the present application, the first sensing device reports the fuzzy speed with the highest probability among the multiple fuzzy speeds as the target object's moving speed to the second sensing device based on the probabilities corresponding to the multiple fuzzy speeds, and indicates the probability corresponding to the reported target object's moving speed.

[0125] Optionally, the measurement result is used to indicate a fuzzy speed with the highest probability among the multiple fuzzy speeds, rather than indicating the probability corresponding to the fuzzy speed.

[0126] As an optional embodiment, before S602, method 600 further includes: the first perception device determining at least one fuzzy speed consistent with the target object's moving direction from the multiple fuzzy speeds, and then the first perception device determining a probability corresponding to each of the at least one fuzzy speed. The at least one fuzzy speed is the one or more fuzzy speeds indicated by the first perception device in S602.

[0127] When the number of the at least one blur speed determined from the plurality of blur speeds is one, the first perception device indicates the one blur speed and its corresponding probability to the second perception device.

[0128] In one possible implementation, the first sensing device determines at least one fuzzy speed consistent with the target's moving direction from the multiple fuzzy speeds, including: the first sensing device determines the target's moving direction based on the sign of the difference between the first distance and the second distance. The first distance is the distance between the target and the sensing device (i.e., the first sensing device) measured in the current measurement, the second distance is the distance between the target and the sensing device (i.e., the first sensing device) measured in the previous measurement, and the moving direction of the target is a direction away from or toward the sensing device (i.e., the first sensing device).

[0129] For example, if the first distance is R2 and the second distance is R1, the set of fuzzy speeds is: {-30km / h, 15km / h, 60km / h, 105km / h}. A negative fuzzy speed value indicates that the fuzzy speed is moving away from the first sensing device, while a positive fuzzy speed value indicates that the fuzzy speed is moving toward the first sensing device.

[0130] See also Figure 7A If the value of R2-R1 is positive, it means that the moving direction of the target object is deviating from the direction of the first sensing device, and at least one fuzzy speed selected includes -30km / h.

[0131] See also Figure 7B or Figure 7C If the value of R2-R1 is negative, it means that the target object is moving in a direction close to the first sensing device, and the at least one fuzzy speed selected includes 15km / h, 60km / h, and 105km / h.

[0132] In the embodiment of the present application, the first sensing device determines the probability corresponding to each fuzzy speed of the at least one fuzzy speed in the following three implementations:

[0133] Implementation Method 1: The first sensing device determines a first movement speed of the target object, where the first movement speed is the predicted movement speed of the target object. The first sensing device then determines a probability corresponding to each of the at least one fuzzy speeds based on a match between each of the at least one fuzzy speeds and the first movement speed.

[0134] In one possible implementation, the first sensing device determines the first moving speed of the target object, including: the first sensing device can determine the first moving speed corresponding to the first distance based on the first information, the first information is used to indicate the correspondence between at least one pair of distances and moving speeds, the distance is the distance between the target object and the sensing device (i.e., the first sensing device), and the moving speed is the moving speed of the target object.

[0135] The first information may be, for example, a Range-Doppler (RD) spectrum. The first sensing device may perform two FFT transforms on the channel estimated after transmitting the sensing signal at the historical moment to obtain the Range-Doppler spectrum. The first sensing device may determine a first moving distance corresponding to the first distance based on the Range-Doppler spectrum, that is, a first moving speed of the target at the first distance predicted based on the Range-Doppler spectrum.

[0136] The first information is, for example, a table indicating a correspondence between at least one pair of distances and moving speeds, and the first sensing device can determine the first moving speed corresponding to the first distance based on the table.

[0137] After determining the first moving speed of the target object, the first sensing device matches each fuzzy speed of the at least one fuzzy speed with the first moving speed to obtain a matching result.

[0138] In one possible implementation, the matching result between each blur speed and the first movement speed can be represented by a ratio of each blur speed to the first movement speed. The closer the ratio is to 1, the closer the blur speed is to the first movement speed. Furthermore, the first sensing device quantifies the ratio of each blur speed to the first movement speed to obtain a probability corresponding to each blur speed.

[0139] For example, at least one fuzzy speed includes V1=15km / h, V2=60km / h, V3=105km / h, the first moving speed V=58km / h, V1 / V≈0.26, V2 / V≈1.03, and V3 / V≈1.81. The difference between the value of V1 / V (0.25) and 1 is 0.75, the difference between the value of V2 / V (1.03) and 1 is 0.03, and the difference between the value of V3 / V (1.81) and 1 is 0.81. The first perception device can determine that the probability corresponding to V1 is 1-0.75=0.25, the first perception device can determine that the probability corresponding to V2 is 1-0.03=0.97, and the first perception device can determine that the probability corresponding to V3 is 1-0.81=0.19.

[0140] In another possible implementation, the matching result between each fuzzy speed and the first moving speed can be represented by the difference between each fuzzy speed and the first moving speed. The smaller the difference, the closer the fuzzy speed is to the first moving speed. Furthermore, the first sensing device determines the probability corresponding to each fuzzy speed based on the probability corresponding to the difference range between each fuzzy speed and the first moving speed.

[0141] For example, at least one fuzzy speed includes V1=15km / h, V2=60km / h, V3=105km / h, and the first moving speed V=58km / h. Assume that the probability corresponding to the difference range [0, 5] is 0.95, the probability corresponding to the difference range [6, 10] is 0.9, the probability corresponding to the difference range [11, 15] is 0.8, the probability corresponding to the difference range [16, 20] is 0.7, and the probability corresponding to the difference range [21, 25] is 0.6. The probability corresponding to the difference range [26, 30] is 0.5, the probability corresponding to the difference range [31, 35] is 0.4, the probability corresponding to the difference range [36, 40] is 0.3, the probability corresponding to the difference range [41, 45] is 0.25, the probability corresponding to the difference range [46, 50] is 0.2, the probability corresponding to the difference range [51, 55] is 0.15, the probability corresponding to the difference range [56, 60] is 0.1, and the probability corresponding to the difference range [60, +∞] is 0.05.

[0142] In this example, the difference between V1 and V is 43, which is in the difference range [41, 45] and has a corresponding probability of 0.25. Therefore, the first sensing device can determine that the probability corresponding to V1 is 0.25. The difference between V2 and V is 2, which is in the difference range [0, 5] and has a corresponding probability of 0.95. Therefore, the first sensing device can determine that the probability corresponding to V2 is 0.95. The difference between V3 and V is 47, which is in the difference range [46, 50] and has a corresponding probability of 0.2. Therefore, the first sensing device can determine that the probability corresponding to V3 is 0.2.

[0143] Implementation Method 2: The first perception device predicts at least one position of the target object based on a point cloud obtained through historical measurements, where the at least one position corresponds to the at least one fuzzy velocity. The first perception device predicts the first position of the target object using a Kalman filter. Furthermore, based on the matching result between the at least one position and the first position, the first perception device determines the probability of the fuzzy velocity corresponding to each of the at least one position.

[0144] The first sensing device can obtain a point cloud of the target object based on the distance information of the target object measured at a historical moment. The point cloud is composed of a large number of points, and each point contains a three-dimensional coordinate and an attribute value. The attribute value is, for example, the reflection intensity corresponding to the point. The reflection intensity is related to the material, roughness, incident angle direction, etc. of the target object. The first sensing device can use the point cloud to monitor the motion trajectory of the target object, and use the at least one fuzzy speed as the input data of the point cloud to predict the position of the target object when the first sensing device receives the reflected signal of the target object, and predict at least one position corresponding to the at least one fuzzy speed, and each fuzzy speed corresponds to a predicted position. At the same time, the first sensing device can also predict the position of the target object through a prediction algorithm to obtain the first position of the target object. The prediction algorithm is, for example, a Kalman filter algorithm, a machine learning algorithm, etc.

[0145] After predicting at least one position corresponding to the at least one blur speed and the first position, the first sensing device may match each of the at least one position with the first position, and determine a probability of the blur speed corresponding to each position based on the matching result of each position with the first position. When matching each position with the first position, the x, y, and z dimensions of each position may be matched with the x, y, and z dimensions of the first position, and the probability of the blur speed corresponding to each position may be determined based on the matching result.

[0146] For example, the at least one blur speed includes V1 and V2, and the at least one position includes P1 and P2. The blur speed V1 corresponds to position P1, and position P1 is expressed as P1 = (x1, y1, z1). The blur speed V2 corresponds to position P2, and position P2 is expressed as P2 = (x2, y2, z2). The first position P is expressed as P = (x, y, z). When matching position P1 with position P, the position offset between position P1 and position P can be calculated, that is, the offset between position P1 and position P in the three dimensions of x, y, and z can be calculated, and the position offset is recorded as △P = (△x1, △y1, △z1), where △x1 represents the difference between x1 and x, △y1 represents the difference between y1 and y, and △z1 represents the difference between z1 and z. Further, the first perception device calculates the average value of △x1, △y1, and △z1, recorded as E1. Similarly, the first perception device can calculate E2 corresponding to position P2.

[0147] Similar to the method of determining the probability corresponding to each fuzzy speed described in the above implementation method 1, the first perception device can determine the probability corresponding to each fuzzy speed based on the probability corresponding to the mean range of E1 and E2.

[0148] Assuming that the probability corresponding to the mean range [e1, e2] is 0.95, E1 is within the mean range [e1, e2]. Therefore, the first perception device can determine that the probability of the blurred velocity V1 corresponding to position P1 is 0.95. Assuming that the probability corresponding to the mean range [e3, e4] is 0.6, E2 is within the mean range [e3, e4]. Therefore, the first perception device can determine that the probability of the blurred velocity V2 corresponding to position P2 is 0.6.

[0149] Implementation method three: In a scenario where the first sensing device transmits and receives signals independently, different antennas (different arrays) of the first sensing device transmit signals in a time-sharing manner, that is, different transmitting antennas (arrays) transmit the same signal at different times. Then, due to the combined effect of the interval Tc between two signal transmissions and the target's moving speed vr, the time-sharing transmitted signals will produce a phase difference when they reach the first sensing device's receiving antenna, namely the Doppler phase shift. The Doppler phase shift is calculated as follows:

[0150]

[0151] Substituting the at least one fuzzy velocity as the moving velocity of the target into the above formula for calculating the Doppler shift, at least one estimated value of the Doppler shift can be obtained. The at least one estimated value of the Doppler shift corresponds one-to-one to the at least one fuzzy velocity.

[0152] Afterwards, the first sensing device can compensate for the measured incident angle of the sensing signal on the receiving antenna of the first sensing device based on at least one estimated value of the Doppler phase shift, and obtain at least one estimated value of the incident angle of the sensing signal on the receiving antenna of the first sensing device.

[0153] Furthermore, the first sensing device determines the probability of the blur velocity corresponding to each of the at least one estimated incident angle values ​​based on a match between each of the at least one estimated incident angle values ​​and the incident angle at the receiving antenna of the first sensing device obtained by historical measurements. For ease of description, the incident angle at the receiving antenna of the first sensing device obtained by historical measurements will be referred to as the historical incident angle. The historical incident angle can be represented by an angle range, denoted as [θ1, θ2].

[0154] The matching result of each estimated value of the incident angle and the historical incident angle is: each estimated value of the incident angle is within the angle range, or each estimated value of the incident angle is not within the angle range.

[0155] If an estimated value of the incident angle is not within the angle range, it can be determined that the estimated value of the incident angle has a low probability of being the true value of the incident angle on the receiving antenna. For example, the probability of the blurred speed corresponding to the estimated value of the incident angle can be determined as p1, and p1 is, for example, 0.

[0156] If an estimated value of the incident angle is within the angle range, it can be determined that the estimated value of the incident angle is likely to be the true value of the incident angle on the receiving antenna. For example, the probability of the blurred velocity corresponding to the estimated value of the incident angle can be determined as p2.

[0157] In one possible scenario, assuming that the at least one blur velocity includes blur velocity V1 and blur velocity V2, and the estimated value of the incident angle corresponding to blur velocity V1 (denoted as θ′1) and the estimated value of the incident angle corresponding to blur velocity V2 (denoted as θ′2) are both within an angular range, the first sensing device may determine the probability corresponding to blur velocity V1 as p2, and the probability corresponding to blur velocity V2 as p2. In this way, the probabilities of blur velocity V1 and blur velocity V2 are the same. After the second sensing device receives blur velocity V1 and its corresponding probability p1, and blur velocity V2 and its corresponding probability p1, sent by the first sensing device, if other sensing devices besides the first sensing device send measurement results for the target object to the second sensing device, the second sensing device may further determine the target object's moving speed based on the blur velocity V1 and its corresponding probability, and blur velocity V2 and its corresponding probability, sent by the first sensing device and the other sensing devices.

[0158] In another embodiment, in order to further distinguish the probabilities of blurred velocity V1 and blurred velocity V2, the first perception device can determine the difference between the estimated value θ′1 and the middle value of the angle range [θ1, θ2], and determine the probability of blurred velocity V1 based on the probability corresponding to the difference range in which the difference lies. The middle value of the angle range [θ1, θ2] is recorded as θ0, and θ0 = (θ2-θ1) / 2. Similarly, the first perception device can determine the probability of blurred velocity V2 based on the probability corresponding to the difference range in which the difference between the estimated value θ′2 and θ0 lies. The specific method is similar to the method of determining the probability corresponding to each blurred velocity based on the difference range described in the first implementation method above, and will not be repeated here.

[0159] The following combination Figure 8 Taking the scenario where the first sensing device sends and receives data by itself as an example, the specific process of the first sensing device sensing the target object and sending the measurement results to the second sensing device is introduced.

[0160] Figure 8 800 is a schematic flow chart of another sensing method 800 provided in an embodiment of the present application. The method 800 includes S801 to S807, and the specific steps are as follows:

[0161] S801: The first sensing device and the second sensing device exchange sensing capability information.

[0162] For example, the second sensing device requests the sensing capability of the first sensing device. Accordingly, the first sensing device requests the sensing capability of the first sensing device from the second sensing device. After receiving the first indication information, the second sensing device can determine the sensing capability of the first sensing device.

[0163] The perception capability includes one or more of the following: perception position accuracy, perception speed accuracy, perception resolution, perception distance, perception area, or duration required for perception.

[0164] S802: The second sensing device sends a configuration information request message to the first sensing device, wherein the configuration information request message is used to request the first sensing device to send system configuration information related to sensing. Accordingly, the first sensing device receives the configuration information request message.

[0165] The system configuration information may include one or more of the following: type information of the perception signal (for example, a positioning reference signal), beam information corresponding to the perception signal, or location information of the first perception device.

[0166] S803: The first sensing device sends system configuration information to the second sensing device. Correspondingly, the second sensing device receives the system configuration information.

[0167] S804: The second sensing device sends a sensing measurement request message to the first sensing device. The sensing measurement request message is used to request the first sensing device to measure the moving speed of the target object. The sensing measurement request message may include information such as the identification information of the target object, the sensing requirement, and the type of the sensing signal. Accordingly, the first sensing device receives the sensing measurement request message.

[0168] S805: The first sensing device senses the target object.

[0169] This step may include: a first sensing device transmitting a sensing signal and receiving a reflected signal from the target object. The first sensing device measures the phase difference between the current and previously received reflected signals. If the phase difference exceeds a range of ±180 degrees, the first sensing device can obtain multiple fuzzy velocities for the target object. The description of the multiple fuzzy velocities of the target object can be found in the description of S601 above and will not be repeated here.

[0170] S806: The first sensing device sends a measurement result to the second sensing device, where the measurement result indicates one or more fuzzy speeds among the plurality of fuzzy speeds and their corresponding probabilities. Correspondingly, the second sensing device receives the measurement result.

[0171] The manner in which the first perception device determines the probabilities corresponding to the one or more fuzzy speeds can refer to the three implementation methods of determining probabilities introduced above, which will not be repeated here.

[0172] S807: The second perception device determines the fuzzy speed with the highest probability among the one or more fuzzy speeds as the moving speed of the target object.

[0173] In an embodiment of the present application, a first sensing device transmits a sensing signal and receives a reflected signal from a target object. The first sensing device determines a phase difference based on the two received reflected signals. If the phase difference exceeds a range of plus or minus 180 degrees, the first sensing device may report to the second sensing device a probability indicating that each of the one or more fuzzy velocities is the target object's movement speed. This facilitates the second sensing device to accurately determine the target object's movement speed, thereby improving sensing performance.

[0174] The following combination Figure 9 Taking a scenario where a sensing device sends a sensing signal and at least one sensing device (including a first sensing device) receives a reflected signal as an example, the specific process of at least one sensing device sensing a target object and sending a measurement result to a second sensing device is introduced.

[0175] For ease of description, the sensing device for sending the sensing signal is referred to as a transmitting device, and the at least one sensing device for receiving the reflected signal of the target object is referred to as at least one receiving device.

[0176] Figure 9 900 is a schematic flow chart of another sensing method 900 provided in an embodiment of the present application. The method 900 includes S901 to S908, and the specific steps are as follows:

[0177] S901: A second sensing device exchanges sensing capability information with a sending device and at least one receiving device.

[0178] For an introduction to the perception capability information, please refer to the description of S801 above, which will not be repeated here.

[0179] S902: The second sensing device sends a configuration information request message to the sending device, wherein the configuration information request message is used to request the first sensing device to send system configuration information related to sensing. Accordingly, the sending device receives the configuration information request message.

[0180] For an introduction to the system configuration information, please refer to the description of S802 above, which will not be repeated here.

[0181] S903: The sending device sends the system configuration information to the second sensing device. Correspondingly, the second sensing device receives the system configuration information.

[0182] S904: The sending device sends the system configuration information to at least one receiving device. Correspondingly, the at least one receiving device receives the system configuration information.

[0183] S905: The second sensing device sends a sensing measurement request message to at least one receiving device. The sensing measurement request message is used to request the at least one receiving device to measure the moving speed of the target object. The sensing measurement request message may include information such as identification information of the target object, sensing requirements, and the type of sensing signal. Accordingly, the at least one receiving device receives the sensing measurement request message.

[0184] S906: The sending device and at least one receiving device jointly perform a sensing process.

[0185] This step, for example, includes: a transmitting device transmitting a sensing signal; each of at least one receiving device receiving a reflected signal from the target; and each receiving device measuring the target's velocity based on the received reflected signal. If the phase difference between the current and previously received reflected signals measured by each receiving device exceeds ±180 degrees, each receiving device can obtain multiple fuzzy velocities for the target. For an introduction to the multiple fuzzy velocities of the target, refer to the description of S601 above and will not be repeated here.

[0186] S907: At least one receiving device sends at least one measurement result to the second sensing device. Correspondingly, the second sensing device receives the at least one measurement result.

[0187] Each of the at least one receiving device sends a measurement result to the second sensing device, where each measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities.

[0188] The manner in which each receiving device determines the probabilities corresponding to the one or more fuzzy speeds may refer to the three implementation methods of determining probabilities introduced above, which will not be described in detail here.

[0189] S908: The second sensing device determines the moving speed of the target object based on at least one measurement result.

[0190] In a possible scenario, the number of at least one receiving device is one, and the second sensing device receives a measurement result. The way in which the second sensing device determines the moving speed of the target object can be found in the description above and will not be repeated here.

[0191] In another possible scenario, the at least one receiving device is multiple, and the second sensing device may combine multiple measurement results sent by the multiple receiving devices to determine the target object's movement speed. The method for the second sensing device to determine the target object's movement speed in the scenario where multiple receiving devices send multiple measurement results has been described above and will not be repeated here.

[0192] In an embodiment of the present application, a receiving device transmits a perception signal, and at least one receiving device receives a reflected signal from a target object. Each of the at least one receiving device determines a phase difference between the currently received and previously received reflected signals. When the phase difference exceeds a range of plus or minus 180 degrees, each receiving device can determine multiple fuzzy velocities of the target object. To accurately indicate the target object's movement speed, each receiving device transmits a probability of each of one or more fuzzy velocities being the target object's movement speed to a second perception device. This facilitates the second perception device's accurate determination of the target object's movement speed, improving perception performance.

[0193] It should be understood that the size of the serial numbers of the above processes 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.

[0194] Combined with the above Figures 6 to 9 , describes in detail the perception method according to the embodiment of the present application, and will be combined with Figure 10 and Figure 11 , describes in detail the sensing device according to an embodiment of the present application.

[0195] Figure 10 This is a schematic block diagram of a sensing device 1000 provided in an embodiment of the present application. The device 1000 includes: a processing module 1010 and a transceiver module 1020.

[0196] The processing module 1010 is used to process data. The transceiver module 1020 can implement corresponding communication functions. The transceiver module 1020 can also be called a communication interface or a communication module.

[0197] Optionally, the device 1000 may further include a storage module, which may be used to store data and / or to store computer programs or instructions. The processing module 1010 may read the computer programs / instructions and / or data in the storage module so that the device 1000 implements the above-mentioned method embodiment.

[0198] Device 1000 can be used to perform the actions performed by the first sensing device or the second sensing device in the above-described method embodiments. Alternatively, device 1000 is a component (e.g., a chip) configured in the first sensing device or the second sensing device. Processing module 1010 is used to perform operations related to processing by the first sensing device or the second sensing device in the above-described method embodiments. Transceiver module 1020 is used to perform operations related to receiving and sending by the first sensing device or the second sensing device in the above-described method embodiments.

[0199] Optionally, the transceiver module 1020 may include a sending module and a receiving module. The sending module is used to perform the sending operation in the above method embodiment. The receiving module is used to perform the receiving operation in the above method embodiment.

[0200] It should be noted that the apparatus 1000 may include a sending module but not a receiving module. Alternatively, the apparatus 1000 may include a receiving module but not a sending module. The specific implementation depends on whether the above solution executed by the apparatus 1000 includes a sending action and a receiving action.

[0201] Optionally, the apparatus 1000 is used to perform the above Figures 6 to 9 In the embodiment shown, the actions performed by the first sensing device or the second sensing device are as follows. Figures 6 to 9 The relevant introduction in the illustrated embodiment will not be repeated here.

[0202] In one embodiment, the processing module 1010 is used to measure the reflected signal from the target object to obtain multiple fuzzy speeds of the target object; the transceiver module 1020 is used to send the measurement results, which are used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, and the probability of each fuzzy speed among the one or more fuzzy speeds indicates the possibility that each fuzzy speed is the moving speed of the target object.

[0203] Optionally, the measurement result is used to indicate a fuzzy speed with the highest probability among the multiple fuzzy speeds and its corresponding probability.

[0204] Optionally, the processing module 1010 is configured to: determine at least one fuzzy speed consistent with the moving direction of the target object from the multiple fuzzy speeds; and determine a probability corresponding to each fuzzy speed in the at least one fuzzy speed.

[0205] Optionally, the processing module 1010 is used to determine the moving direction of the target object based on the positive or negative value of the difference between the first distance and the second distance, the first distance being the distance between the target object and the sensing device obtained in this measurement, the second distance being the distance between the target object and the sensing device obtained in the last measurement, and the moving direction being the direction away from the sensing device or the direction approaching the sensing device.

[0206] Optionally, the processing module 1010 is used to: determine a first moving speed of the target object, where the first moving speed is the predicted moving speed of the target object; and determine the probability corresponding to each of the at least one fuzzy speeds based on the matching result between each of the at least one fuzzy speeds and the first moving speed.

[0207] Optionally, the processing module 1010 is used to: determine a first moving speed corresponding to a first distance based on first information, the first information is used to indicate a correspondence between at least one pair of distances and moving speeds, the distance is the distance between the target object and the sensing device, and the moving speed is the moving speed of the target object.

[0208] Optionally, the first information is a range Doppler spectrum.

[0209] Optionally, the processing module 1010 is configured to perform two Fourier transforms on the channel estimated after the sensing signal is sent at the historical moment to obtain a range Doppler spectrum.

[0210] Optionally, the processing module 1010 is used to: predict at least one position of the target object corresponding to the at least one fuzzy speed based on the point cloud obtained from historical measurements; predict the first position of the target object through Kalman filtering; and determine the probability of the fuzzy speed corresponding to each position in the at least one position based on the matching result of each position in the at least one position with the first position.

[0211] Optionally, the processing module 1010 is used to: determine at least one estimated value of the Doppler phase deviation of the perception signal on the receiving antenna of the perception device based on the at least one fuzzy speed, the at least one estimated value of the Doppler phase deviation corresponding one-to-one to the at least one fuzzy speed; determine at least one estimated value of the incident angle of the perception signal on the receiving antenna of the perception device based on the at least one estimated value of the Doppler phase deviation, the at least one estimated value of the incident angle corresponding one-to-one to the at least one estimated value of the Doppler phase deviation; determine the probability of the fuzzy speed corresponding to each of the at least one estimated value of the incident angle based on a matching result between each estimated value of the at least one estimated value of the incident angle and the incident angle on the receiving antenna obtained from historical measurements.

[0212] In this embodiment, those skilled in the art will appreciate that the device 1000 may be specifically the above-mentioned Figures 6 to 9 The first sensing device in the embodiment shown, or the above Figures 6 to 9 In the illustrated embodiment, the functions of the first sensing device can be integrated into device 1000. These functions can be implemented via hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions described above. Device 1000 can be used to execute the various processes and / or steps corresponding to the first sensing device in the aforementioned method embodiments.

[0213] In another embodiment, the transceiver module 1020 is used to: receive measurement results, where the measurement results are used to indicate one or more fuzzy speeds among a plurality of fuzzy speeds and their corresponding probabilities, and the probability corresponding to each of the one or more fuzzy speeds indicates the likelihood that each fuzzy speed is the moving speed of the target object; and the processing module 1010 is used to: determine the fuzzy speed with the highest probability among the one or more fuzzy speeds as the moving speed of the target object.

[0214] Optionally, the measurement result is used to indicate a fuzzy speed with the highest probability among the multiple fuzzy speeds and its corresponding probability.

[0215] It should be understood that the apparatus 1000 herein is embodied in the form of functional modules. The term "module" herein may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group processor, etc.) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0216] In the embodiment of the present application, the device 1000 may also be a chip or a chip system, such as a system on chip (SoC). Correspondingly, the transceiver module may be a transceiver circuit of the chip, which is not limited here.

[0217] Figure 10 1 is a schematic block diagram of another communication device 1100 provided in an embodiment of the present application. The communication device 1100 includes a processor 1110, a transceiver 1120, and a memory 1130. The processor 1110, the transceiver 1120, and the memory 1130 communicate with each other via an internal connection path. The memory 1130 is used to store instructions, and the processor 1110 is used to execute the instructions stored in the memory 1130 to control the transceiver 1120 to send and / or receive signals.

[0218] Optionally, the communication device 1100 further includes a power supply circuit 1140 , which can be used to supply power to the communication device 1100 .

[0219] It should be understood that the device 1100 can be specifically the first sensing device or the second sensing device in the above-mentioned embodiment, or the functions of the first sensing device or the second sensing device in the above-mentioned embodiment can be integrated in the device 1100, and the device 1100 can be used to execute the various steps and / or processes corresponding to the first sensing device or the second sensing device in the above-mentioned method embodiment. Optionally, the memory 1130 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type. The processor 1110 can be used to execute instructions stored in the memory, and when the processor executes the instruction, the processor 1110 can execute the various steps and / or processes corresponding to the first sensing device or the second sensing device in the above-mentioned method embodiment.

[0220] An embodiment of the present application further provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer, the method described in the above embodiment is executed.

[0221] An embodiment of the present application further provides a computer program product, which includes: a computer program or instructions, and when the computer program or instructions are executed, the method described in the above embodiment is executed.

[0222] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), ASICs, field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0223] During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor executes the instructions in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0224] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0225] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0226] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0227] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0228] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0229] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0230] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A perception method, characterized in that: include: measuring a reflected signal from a target object to obtain a plurality of fuzzy velocities of the target object; Sending a measurement result, where the measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, wherein the probability corresponding to each of the one or more fuzzy speeds indicates the likelihood that each fuzzy speed is the moving speed of the target object.

2. The method according to claim 1, characterized in that The measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, including: The measurement result is used to indicate a blur speed with the highest probability among the multiple blur speeds and its corresponding probability.

3. The method according to claim 1 or 2, characterized in that Before sending the measurement result, the method further includes: determining at least one blur speed consistent with the moving direction of the target object from the plurality of blur speeds; A probability corresponding to each blur speed of the at least one blur speed is determined.

4. The method according to claim 3, characterized in that Before determining at least one blur speed consistent with the moving direction of the target object from the multiple blur speeds, the method further includes: The moving direction of the target object is determined based on the positive or negative value of the difference between the first distance and the second distance, where the first distance is the distance between the target object and the sensing device obtained in this measurement, and the second distance is the distance between the target object and the sensing device obtained in the last measurement. The moving direction is the direction away from the sensing device or the direction close to the sensing device.

5. The method according to claim 4, characterized in that Determining the probability corresponding to each fuzzy speed of the at least one fuzzy speed includes: Determining a first moving speed of the target object, where the first moving speed is a predicted moving speed of the target object; According to a matching result between each blur speed of the at least one blur speed and the first moving speed, a probability corresponding to each blur speed of the at least one blur speed is determined.

6. The method according to claim 5, characterized in that The determining the first moving speed of the target object includes: According to the first information, the first moving speed corresponding to the first distance is determined, and the first information is used to indicate the correspondence between at least one pair of distances and moving speeds, the distance being the distance between the target object and the sensing device, and the moving speed being the moving speed of the target object.

7. The method according to claim 6, characterized in that The first information is a range Doppler spectrum.

8. The method according to claim 7, characterized in that Before determining the first moving speed corresponding to the first distance according to the first information, the method further includes: The channel estimated after the sensing signal is sent at the historical moment is subjected to two Fourier transforms to obtain the range Doppler spectrum.

9. The method according to claim 3 or 4, characterized in that Determining the probability corresponding to each fuzzy speed of the at least one fuzzy speed includes: predicting at least one position of the target object corresponding to the at least one fuzzy velocity based on the point cloud obtained by historical measurements; Predicting a first position of the target object by using a Kalman filter; According to the matching result between each of the at least one position and the first position, a probability of a blur speed corresponding to each of the at least one position is determined.

10. The method according to claim 3 or 4, characterized in that Determining the probability corresponding to each fuzzy speed of the at least one fuzzy speed includes: determining, based on the at least one ambiguous velocity, at least one estimated value of a Doppler shift of the sensing signal on a receiving antenna of the sensing device, wherein the at least one estimated value of the Doppler shift corresponds one-to-one to the at least one ambiguous velocity; determining, based on the at least one estimated value of the Doppler phase shift, at least one estimated value of an angle of incidence of the sensing signal on a receiving antenna of the sensing device, wherein the at least one estimated value of the angle of incidence corresponds one-to-one to the at least one estimated value of the Doppler phase shift; The probability of an ambiguous velocity corresponding to each of the at least one estimated value of the incident angle is determined based on a matching result of each of the at least one estimated value of the incident angle with the incident angle on the receiving antenna obtained by historical measurements.

11. A sensing method, characterized in that: include: receiving a measurement result, the measurement result being used to indicate one or more fuzzy speeds among a plurality of fuzzy speeds and their corresponding probabilities, wherein the probability corresponding to each of the one or more fuzzy speeds indicates a likelihood that each fuzzy speed is a moving speed of the target object; The blur speed with the highest probability among the one or more blur speeds is determined as the moving speed of the target object.

12. The method according to claim 11, characterized in that The measurement result is used to indicate one or more fuzzy speeds among the multiple fuzzy speeds and their corresponding probabilities, including: The measurement result is used to indicate a blur speed with the highest probability among the multiple blur speeds and its corresponding probability.

13. A sensing device, characterized in that: The method comprises a module for implementing the method according to any one of claims 1 to 10, or a module for implementing the method according to claim 11 or 12.

14. A sensing device, characterized in that: The method comprises one or more processors coupled to a memory, wherein the memory is used to store programs or instructions. When the programs or instructions are executed by the one or more processors, the method according to any one of claims 1 to 10 is executed, or the method according to claim 11 or 12 is executed.

15. A computer-readable storage medium, characterized in that Used for storing a computer program, which, when running on a computer, causes the method according to any one of claims 1 to 10 to be performed, or causes the method according to claim 11 or 12 to be performed.

16. A computer program product, characterized in that include: A computer program or instructions, which, when executed, causes the method according to any one of claims 1 to 10 to be performed, or causes the method according to claim 11 or 12 to be performed.

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