A speed perception method, apparatus, system and storage medium

Abstract

The application provides a speed sensing method, device, system and storage medium. The method can be applied to a sensing integration scene. In the method, a first device transmits a sensing signal and receives a return signal through a two-dimensional array antenna. The return signal refers to a signal reflected by a target after the sensing signal. The first device performs channel estimation based on the return signal to obtain an estimation result, which includes a time delay estimation and a phase estimation. The first device determines a frequency offset based on the time delay estimation and the phase estimation. The frequency offset is determined based on frequency offsets corresponding to return signals received by different elements in the two-dimensional array antenna. The first device determines a lateral speed of the target based on the frequency offset. It can be seen that the first device can directly extract the lateral speed information of the target according to the difference between the Doppler frequency offsets of different elements in the two-dimensional array antenna, improve the real-time and accuracy of the estimation, and reduce the cost.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a speed sensing method, apparatus, system and storage medium. Background Technology

[0002] With the continued and in-depth advancement of research into sixth-generation mobile communication technology, mobile communication networks are evolving towards "integrated air-space-ground" and "integrated communication, sensing, and computing" directions. Coverage and service support capabilities in high-speed mobile scenarios have become key technological bottlenecks. Currently, the speed distribution of future mobile terminals will exhibit multi-gradient characteristics: in high-speed rail scenarios, terminal speeds can reach 350 km / h; in vehicle-to-everything (V2X) scenarios, terminal speeds are 60-120 km / h; in low-altitude aircraft (such as drone swarms) scenarios, terminal speeds are 80-200 km / h; while in traditional urban roads, mobile terminal speeds are 30-60 km / h. Different speed levels of mobile terminals place differentiated demands on communication network latency, Doppler shift compensation, beam tracking accuracy, and resource scheduling flexibility.

[0003] To ensure the stability of communication links in mobile scenarios, future mobile communication networks need to possess millisecond-level, high-precision target velocity estimation capabilities (e.g., velocity estimation error ≤5%, estimation delay ≤10ms). Currently, target velocity is mainly sensed through single-site sensing technology. Single-site sensing technology refers to a system where the node transmitting the sensing signal and receiving the echo signal are the same node, such as a base station transmitting and receiving signals independently, or a terminal device transmitting and receiving signals independently. Specifically, the single-site sensing system infers distance parameters by analyzing the delay changes in the received target reflected signal, calculates azimuth information using the phase difference of the array antenna, and derives radial velocity (i.e., the velocity component of the target along the line connecting the base station and the target) using Doppler frequency offset. However, lateral velocity (i.e., the velocity component of the target perpendicular to the radial direction) cannot be directly decoupled using a single observation dimension.

[0004] Currently, most methods for estimating the lateral velocity of targets employ indirect sensing, such as multi-base station synthesis. However, indirect sensing methods often suffer from high overhead and low accuracy, making them difficult to scale to diverse scenarios. Summary of the Invention

[0005] This application provides a speed sensing method, apparatus, system, and storage medium that can directly estimate the speed of a target, reduce overhead and estimation latency, and improve estimation accuracy.

[0006] Firstly, a speed-sensing method is provided. This method can be executed by a terminal device, or by a component (such as a circuit, chip, or chip system) configured in the terminal device, or by a logic module or software capable of implementing all or part of the terminal device's functions. This application does not limit this. Alternatively, the method can be executed by a network device, or by a component (such as a circuit, chip, or chip system) configured in the network device, or by a logic module or software capable of implementing all or part of the network device's functions.

[0007] The method includes: a first device transmitting a sensing signal and receiving an echo signal via a two-dimensional array antenna. The echo signal refers to the signal reflected by the target after the sensing signal has been received. The first device performs channel estimation based on the echo signal to obtain an estimation result, which includes a time delay estimate and a phase estimate. Based on the time delay estimate and the phase estimate, the first device determines a frequency offset, which is determined by the frequency offset corresponding to the echo signals received by different elements in the two-dimensional array antenna. Based on the frequency offset, the first device determines the lateral velocity corresponding to the target. Therefore, the first device extracts the target's lateral velocity information based on the difference in Doppler frequency offset between different elements of the two-dimensional array antenna, achieving direct estimation of the lateral velocity, improving the real-time performance and accuracy of the estimation, and reducing overhead.

[0008] Secondly, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is used to transmit sensing signals via a two-dimensional array antenna and to receive echo signals via the two-dimensional array antenna. The processing module is used to perform channel estimation based on the echo signals to obtain estimation results, the estimation results including time delay estimates and phase estimates. The processing module is also used to determine a frequency offset based on the time delay estimates and phase estimates, the frequency offset referring to the frequency offset between echo signals received by different array elements in the two-dimensional array antenna. The processing module is also used to determine the horizontal velocity corresponding to the target based on the frequency offset.

[0009] Thirdly, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0010] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0011] In another implementation, the communication device is a chip configured in a terminal device. When the communication device is a chip configured in a terminal device, the communication interface can be an input / output interface.

[0012] In another implementation, the communication device is a chip configured in a network device. When the communication device is a chip configured in a network device, the communication interface can be an input / output interface.

[0013] Fourthly, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0014] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0015] Fifthly, a communication device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory, receive signals via a receiver, and transmit signals via a transmitter to execute the method in any possible implementation of any of the above aspects.

[0016] Optionally, the processor may be one or more, and the memory may be one or more.

[0017] In a sixth aspect, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0018] In a seventh aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the methods in any possible implementation of any of the above aspects.

[0019] Eighthly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0020] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0021] In a ninth aspect, a communication system is provided, including the aforementioned terminal device and network device. Optionally, the communication system may further include other devices that communicate with the terminal device and / or network device. Attached Figure Description

[0022] Figure 1 A communication system structure diagram provided in this application embodiment;

[0023] Figure 2 This is a scene diagram for lateral velocity sensing;

[0024] Figures 3a-3b This is a schematic diagram of an indirect method of sensing lateral velocity.

[0025] Figure 4 This is a schematic diagram of a planar array antenna structure provided in an embodiment of this application;

[0026] Figure 5 A flowchart of a speed sensing method provided in an embodiment of this application;

[0027] Figure 6 A speed sensing framework diagram provided for an embodiment of this application;

[0028] Figures 7a to 7d This application provides a schematic diagram illustrating different parameter estimations and resource allocation in an embodiment.

[0029] Figure 8 This is a schematic diagram of adjusting the perception window provided in an embodiment of this application;

[0030] Figure 9 A structural diagram of a communication device provided in an embodiment of this application;

[0031] Figure 10 This is a structural diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0032] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0033] The technical solutions provided in this application can be applied to various communication systems, such as: Global System for Mobile Communications (GSM) systems, General Packet Radio Service (GPRS), Wireless Local Area Network (WLAN), Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE Time Division Duplex (TDD) systems, sidelink communication systems, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication systems, non-terrestrial network (NTN) communication systems, 5th generation (5G) mobile communication systems, or new radio access technology (NR). Among these, 5G mobile communication systems can include non-standalone (NSA) and / or standalone (SA) networking. The technical solutions provided in this application can also be applied to future communication systems. This application does not limit the scope of these applications.

[0034] Figure 1 This is a schematic diagram of a communication system 100 used in an embodiment of this application. The communication system 100 may include network devices, such as... Figure 1 The network device 110 is shown. The communication system 100 may also include terminal devices, such as... Figure 1 The terminal device 120 shown. The network device 110 and the terminal device 120 can communicate via a wireless link.

[0035] In this application, the first device can be network device 110, and the target can be terminal device 120. Alternatively, the first device can be terminal device 120, and the target can be other devices to be sensed.

[0036] Figure 1 An exemplary network device 110 and a terminal device 120 are shown. Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0037] The network equipment in this application can be network-side equipment such as access network equipment and core network equipment. Access network equipment is sometimes also called access node. Access network equipment has wireless transceiver capabilities and is used to communicate with terminals. Access network equipment includes, but is not limited to, base stations, evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the aforementioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, access network equipment or modules of access network equipment in open RAN (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in WiFi systems. Access network equipment can also be modules or units capable of implementing some of the functions of a base station. Access network equipment can be a macro base station (such as...). Figure 1 The access network device can be a micro base station or indoor station, a relay node or donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. Optionally, the access network device can also be a server, wearable device, or vehicle-mounted device. For example, in vehicle-to-everything (V2X) technology, the access network device can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate with terminals directly or via relay stations. Terminals can communicate with multiple base stations using different access technologies. The embodiments of this application do not limit the specific technology or device form used in the access network device. In this application, the access network device is referred to as a network device.

[0038] In this application, the means for implementing the functions of a network device can be a network device itself, or a means capable of supporting the network device in implementing those functions, such as a processor, circuit, chip, or chip system. This means can be installed in or connected to the network device. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0039] The terminal device in this application can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, or satellite communication, etc. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of this application do not limit the form of the terminal device.

[0040] In this application, the apparatus for implementing the functions of a terminal device can be the terminal device itself, or any apparatus capable of supporting the terminal device in implementing those functions, such as a processor, circuit, chip, or chip system. This apparatus can be installed in or connected to the terminal device. In the technical solutions provided in this application, the example of a terminal device being used to implement the functions of a terminal device is used to describe the technical solutions provided in this application.

[0041] Access network devices and / or terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network devices and terminals. Access network devices and terminal devices can be deployed in the same or different scenarios; for example, both can be deployed on land; or the access network device can be deployed on land, and the terminal device on water, etc., and so on.

[0042] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing a portion of the base station's functions. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0043] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0044] To facilitate understanding of the embodiments of this application, the terminology used in this application will be briefly explained first. Optionally, the explanation of some terms may also refer to the explanations in the 3rd Generation Partnership Project (3GPP) standard protocol.

[0045] 1. Two-dimensional array antenna

[0046] A two-dimensional array antenna refers to an antenna array in which antenna elements are arranged on a two-dimensional plane. Specifically, it can include matrix array antennas, circular array antennas, etc.

[0047] Among them, a planar antenna array is an antenna array composed of several antenna elements (also called "array elements") with independent electromagnetic wave radiation / reception capabilities, arranged on the same two-dimensional plane according to a preset rule (such as rectangle, square, circle, etc.). It is a two-dimensional extension of the antenna array relative to the one-dimensional linear array antenna, and it is also the core antenna structure for realizing two-dimensional spatial angle resolution, three-dimensional beamforming and spatial filtering under azimuth and elevation angles.

[0048] 2. Pointing angle

[0049] The pointing angle is an angular parameter that describes the specific pointing direction of an electromagnetic wave beam, antenna array, detection signal, or target in three-dimensional space. It is a collective term for the azimuth angle and elevation angle. By combining these two angles in spherical coordinates, the three-dimensional pointing direction in a certain direction in space is uniquely determined. It is a core parameter for beamforming of area array antennas, angle of arrival (DOA) estimation, radar target localization, and communication beam tracking.

[0050] It should be understood that the technical terms used in this application are for illustrative purposes only and not as limiting. For example, as technology evolves, technical terms may also change, and other technical terms that have the same technical meaning should also apply to this application.

[0051] Currently, single-station sensing nodes estimate distance by receiving echo signal delays, calculate pointing angles by the phase difference of array antennas, and derive radial velocity by Doppler frequency shift. However, lateral velocity cannot be directly decoupled from radial velocity using a single observation dimension. The reason for this inability to calculate lateral velocity using a single observation dimension is that single-station sensing can only acquire the target's motion characteristics in the one-dimensional radial direction. Lateral motion does not produce Doppler frequency shifts and cannot be directly characterized by time delay or phase information, making it difficult to separate the coupling relationship between lateral and radial velocities using algorithms.

[0052] The lack of lateral velocity will lead to application limitations in multiple scenarios. For example, in high-speed movement scenarios (such as vehicle-to-everything (V2X) and low-altitude aircraft), the lateral movement of the target (such as vehicle lane changes and aircraft flight path deviations) is a key factor affecting collision warnings and path prediction. Relying solely on radial velocity cannot accurately predict the target's trajectory, potentially leading to decision delays or misjudgments in integrated sensing services. Furthermore, in traffic speed measurement and warning scenarios, when the target is traveling at a small angle (≤30°) to the line connecting it to the base station, the proportion of lateral velocity increases significantly, and the speed measurement error of traditional technologies will exceed 15 km / h, far exceeding the ±2 km / h accuracy standard required by traffic management. Additionally, in target tracking scenarios, the unknown lateral velocity will result in missing parameters in the prediction model of the tracking algorithm, causing tracking errors to accumulate over time. When the target is continuously moving laterally, the tracking offset can reach more than 50 meters within 10 seconds, failing to meet the high-precision tracking requirements of security monitoring and emergency rescue scenarios. Meanwhile, the lack of lateral velocity measurement capability limits the adaptability of single-station sensing systems to complex motion states: for non-uniform motion scenarios such as curvilinear motion and variable acceleration motion, the lateral velocity of the target changes dynamically over time. Current technology lacks an independent measurement dimension and cannot update lateral motion parameters in real time, resulting in insufficient dynamic response performance of full-dimensional velocity estimation. The estimation latency has increased from 10ms to more than 50ms, making it difficult to adapt to the measurement needs of high-speed dynamic targets.

[0053] In three-dimensional spatial scenes, the above problems will become even more pronounced. For example... Figure 2 As shown, due to the omnidirectional nature of the target's motion, its lateral velocity V is not a single-dimensional parameter, but can be divided into two mutually perpendicular independent components: the horizontal velocity Vx (the velocity component parallel to the horizontal array elements) and the vertical velocity Vy (the velocity component parallel to the vertical array elements). The completeness of the measurement of these two dimensions has a more critical impact on the accuracy of velocity vector estimation. The core reason is that the increased degrees of freedom in three-dimensional space lead to more complex coupling relationships among the velocity components, and the absence or measurement deviation of a single dimension will cause systematic vector reconstruction errors.

[0054] The completeness of horizontal and vertical velocity measurements directly determines the adaptability of integrated sensing services in three-dimensional space. In low-altitude surveillance scenarios, the lack of vertical velocity will lead to the inability to identify the ascent and descent of aircraft, easily causing low-altitude flight conflicts. In high-rise building fire rescue scenarios, the joint estimation deviation of horizontal and vertical velocities will affect the accurate deployment of rescue equipment (such as fire-fighting drones). In integrated air-space-ground communication scenarios, measurement errors of the vertical velocity components of satellites and ground terminals (such as satellite orbital ascent and descent, and terminal altitude changes) will lead to insufficient Doppler frequency shift compensation, increasing the communication bit error rate to 10%. -3 The magnitude far exceeds the 10 required by 6G systems. -6 Magnitude.

[0055] Furthermore, when the target is in the near-field region (distance ≤ 2D² / λ, where D is the array aperture and λ is the signal wavelength), the signal incident angle exhibits a non-linear change with the target position, further complicating the coupling relationship between horizontal, vertical, and radial velocities. The angle-velocity decoupling model under the traditional far-field assumption fails, not only causing measurement deviations in horizontal / vertical velocities (the deviation increases to over 15% as the target distance decreases), but also leading to distortions in azimuth and pitch angle estimations. This, in turn, compromises the accuracy of 3D velocity vector reconstruction, significantly reducing the adaptability of integrated sensing services in near-field scenarios (such as indoor robots and near-field low-altitude aircraft). Therefore, constructing a universal single-station velocity sensing method applicable to both near and far fields, achieving accurate measurement of both horizontal and vertical velocities in 3D space, is a core prerequisite for overcoming existing velocity vector estimation bottlenecks and supporting the implementation of integrated 3D sensing services.

[0056] For estimating the lateral velocity of a target, traditional methods mostly employ indirect sensing approaches, such as multi-base station synthesis, Kalman filtering, and interferometric radar. Indirect sensing methods often suffer from high overhead and poor robustness. While they can meet the requirements of low-precision, low-real-time lateral velocity sensing in specific scenarios, they are difficult to extend to high-precision, high-real-time sensing scenarios across diverse environments.

[0057] The core of the multi-base station-based lateral velocity sensing scheme lies in collecting distance, distance difference, angle, and Doppler data from multiple transmitting / receiving stations, jointly calculating the target position and velocity vector, and decomposing the lateral component. For example, Figure 3aAs shown, both base station 1 and base station 2 sense the radial velocity of user equipment. V1 is the radial velocity of user equipment relative to base station 1 measured by base station 1, and V2 is the radial velocity of user equipment relative to base station 2 measured by base station 2. Va is the lateral velocity corresponding to V1, and Vb is the lateral velocity corresponding to V2. The vector sum of V1 and Va is equal to the vector sum of V2 and Vb, which equals Vnet. Given Vnet, V1, and V2, Va and Vb can be calculated. Dual base stations can achieve partial constraints, while three or more base stations can complete the full spatial solution. This technical solution possesses the natural advantage of complementary perspectives, making it outstanding in terms of resistance to obstruction and electronic interference, and particularly suitable for parameter estimation in multi-target and complex environments. However, it is precisely this characteristic of multi-source data joint dependence that leads to significant limitations of the scheme: (1) As an indirect estimation mode, its data processing flow is extremely complex, and it imposes stringent requirements on time synchronization, phase synchronization and baseline calibration between multiple stations. Any slight deviation may cause calculation errors; (2) The hardware deployment, network construction and subsequent maintenance of multiple base stations require high costs, making it difficult to popularize in low-cost scenarios; (3) During the multi-source data fusion process, it is easy to generate false targets due to the mixing of redundant information and interference signals. In complex environments, the data loss or abnormality of some base stations will directly destroy the reliability of complete spatial calculation, restricting its adaptability in resource-constrained or dynamically changing scenarios.

[0058] For example, Figure 3b The illustrated lateral velocity sensing scheme based on Kalman filtering utilizes Kalman filtering (including extended forms such as EKF and UKF) to construct state-space models for uniform velocity and uniform acceleration. It collects azimuth angles at different time points (t1, t2, etc.) and then outputs an optimal state estimate containing lateral velocity based on the azimuth angle observations using a predictive-update iterative approach. This scheme exhibits strong noise suppression capabilities and supports multi-sensor data fusion, demonstrating significant advantages in maneuvering target tracking scenarios. However, the advantages and limitations of this scheme both stem from its core logic of indirect estimation: (1) Although the iterative estimation process can smooth out noise interference, it requires a lot of time domain resources and is not suitable for scenarios with high real-time requirements; (2) Its performance is highly dependent on the preset state space model and accurate noise statistics. If the target motion pattern deviates significantly from the model (such as sudden change of direction or non-uniform motion), or if there is non-Gaussian noise, it will directly lead to a decrease in estimation accuracy or even filter divergence; (3) Reliable observation source data is the premise of its optimal estimation. If the observation signal is interfered with, data is missing, or the noise statistics do not match the actual situation, it will seriously affect the accuracy of the lateral velocity estimation. At the same time, the method of synthesizing a single measurement quantity in multiple time slots and multiple directions may lose some key dynamic information, further limiting its application effect in high dynamic and complex noise environments.

[0059] For example, in interferometric radar-based lateral velocity sensing schemes, interferometric radar utilizes a baseline formed by dual antennas or repeating orbits to directly invert the target's lateral velocity through interferometric phase or frequency. This direct measurement principle eliminates the need for radial projection, giving it a unique advantage in close-range, high-precision velocity measurement scenarios. Millimeter-wave interferometric radar can achieve single / multi-target velocity measurement with a wide field of view, while SAR interferometry is suitable for monitoring slow-moving ground targets. However, the technical nature of coherent measurement also determines its inherent limitations: (1) The length and configuration of the baseline directly constrain the speed measurement range and accuracy. Once it does not match the target speed range, a speed measurement blind zone or insufficient accuracy will occur; (2) Signal coherence is the core premise of measurement. However, in actual scenarios, occlusion, multipath effect, and environmental clutter will destroy coherence, resulting in distorted speed measurement results; (3) Phase ambiguity and unwrapping are always technical bottlenecks. Especially when the target speed exceeds the adaptation range or the signal is interfered with, the unwrapping error will be significantly amplified; In addition, the judgment of bright and dark stripes depends on a stable signal environment and accurate phase analysis. In complex scenarios, stripe features are easily blurred, making the scheme less adaptable to complex environments and difficult to play a role in multi-target, strong interference, or large-scale speed measurement scenarios.

[0060] In summary, current methods for sensing lateral velocity cannot meet the needs of different scenarios, and finding a way to directly measure the lateral velocity of a target is an urgent technical solution.

[0061] In view of this, this application provides a velocity sensing method that directly extracts the lateral velocity of the target based on the difference in Doppler frequency offset between different array elements in a two-dimensional array antenna, thereby reducing overhead while ensuring estimation accuracy and thus meeting the performance requirements of single-station velocity sensing.

[0062] Specifically, this application decomposes the Doppler frequency shift into a fixed frequency offset, a horizontal frequency difference, and a vertical frequency difference by changing the Doppler frequency shift model representation in a three-dimensional scene. The radial velocity is estimated based on the fixed frequency difference, the horizontal velocity is estimated based on the horizontal frequency difference, and the vertical velocity is estimated based on the vertical frequency difference.

[0063] To facilitate understanding of this application, a rectangular array antenna is used as an example for illustration in this explanation. See [link to documentation]. Figure 4 The first device is equipped with M A uniform square array antenna of size N, with horizontal and vertical spacing of array elements, respectively d. M and d N For any moving target in three-dimensional space, the target relative to any array element E mn The three-dimensional state (position and velocity) of (m=1,2,…,M;n=1,2,…,N) can be represented as: .in, Indicates distance, For spatial azimuth, For pitch angle, Radial velocity, For horizontal speed, Let be the vertical velocity. The target's velocity vector can be represented as... For ease of subsequent description, the default state of the target is defined relative to the state of E11, that is: .

[0064] According to the second-order Taylor expansion, the distance can be obtained. This can be expressed as:

[0065]

[0066] Among them, in the far field, it satisfies Therefore, it can be ignored. Differentiating both sides of the above equation with respect to time yields the radial velocity, which can be specifically expressed as:

[0067]

[0068] For safety reasons, the distance should be much larger than the array aperture, thus This holds true consistently in both the far and near fields, and therefore can be ignored. Furthermore, by relating both sides of the equation to wavelengths, the Doppler frequency shift can be obtained. for:

[0069]

[0070] in, For fixed frequency offset, For horizontal frequency difference, This represents the vertical frequency difference.

[0071] The above formula shows that there is a one-to-one linear relationship between the fixed frequency offset - horizontal frequency difference - vertical frequency difference and the radial velocity - horizontal velocity - vertical velocity. Linear separation can be achieved based on the changes of Doppler frequency offset with m and n.

[0072] The solution provided in this application will be described in detail below with reference to the corresponding flowcharts. It is understood that the illustrative flowcharts provided in this application primarily use different devices (e.g., terminal devices, network devices) as examples of the execution subjects of this interactive illustration to illustrate the method, but this application does not limit the execution subjects of the interactive illustrations. For example, the devices (e.g., terminal devices, network devices) in the illustrative flowcharts can also be chips, chip systems, or processors that support the implementation of this method on the device, or logic modules or software that can implement all or part of the functions of the device.

[0073] As a general statement, the message or signaling interactions involved in the interaction process of this application embodiment can be standard messages or signaling or newly introduced messages or signaling. This application embodiment does not make specific limitations on this.

[0074] Figure 5 This is a schematic diagram of a speed sensing method according to an embodiment of this application. It can be understood that... Figure 5 The first device in the middle can be Figure 1 The term "first device" can refer to any terminal device, or it can refer to a component within a terminal device (such as a processor, chip, or chip system). Alternatively, the first device can be... Figure 1 Any access network device, or a component within an access network device (such as a processor, chip, or chip system). Figure 5 As shown, the method includes the following steps:

[0075] S501: The first device transmits sensing signals through a two-dimensional array antenna.

[0076] In this embodiment, the first device is for performing a sensing task, and can send sensing signals to the sensing area through a two-dimensional array antenna according to the sensing requirements corresponding to the sensing task.

[0077] The sensing signals can be pilot signals, control signals, or data signals, improving flexibility. Specifically, pilot signals can include channel state information reference signals (CSI-RS) and sounding reference signals (SRS). Control signals can include synchronization signal blocks (SSBs) or physical random access channels (PRACHs). Data signals can include orthogonal frequency division multiplexing (OFDM) signals, linear frequency modulation (LFM) signals, etc.

[0078] In some implementations, to ensure measurement accuracy, the first device can adjust the two-dimensional array antenna before transmitting the sensing signal. Specifically, the first device acquires initial configuration information corresponding to itself and initial state information corresponding to the target; the first device adjusts the steering matrix corresponding to the two-dimensional array antenna based on the initial configuration information and the initial state information.

[0079] The initial configuration information mainly includes parameters corresponding to the two-dimensional array antenna and sensing-related parameters. Specifically, the parameters corresponding to the two-dimensional array antenna may include the horizontal element spacing d. M Vertical interval d N Number of horizontal array elements M, number of vertical array elements N, array azimuth angle Array pitch angle Parameters related to sensing may include wavelength λ, sensing interval T, and time slot length T. s The sampling interval T0, the number of time slots K, and the number of samples per time slot Q are specified. The sampling interval indicates the interval at which the echo signal is sampled, and the number of time slots indicates the number of time slots occupied by the sensing signal transmitted by the first device within the current sensing period.

[0080] Initial state information includes initial position information and initial velocity information. Specifically, initial position information includes distance d0 and spatial azimuth angle. Pitch angle Initial velocity information includes radial velocity. Horizontal speed Vertical velocity .

[0081] Specifically, the steering matrix of the array antenna can be determined using the following formula based on time t, initial configuration information, and initial state information. Adjustments will be made:

[0082]

[0083] Among them, by input , and The three functions can respectively correct the horizontal direction, vertical direction, and focal distance, thereby achieving initial alignment and real-time tracking of the target based on its initial position and velocity.

[0084] As can be seen, in this embodiment, beam alignment for the initial sensing period in a high-maneuverability scenario can be ensured by the initial horizontal velocity and the initial vertical velocity, and beam focusing for the initial sensing period in a far-field or near-field scenario can be ensured by the initial distance and radial velocity.

[0085] After determining the steering matrix of the area array antenna, the sensing signal... It can be represented as:

[0086]

[0087] in,

[0088] S502: The first device receives the echo signal through a two-dimensional array antenna.

[0089] In this embodiment, the first device receives the echo signal reflected from the target via a two-dimensional array antenna. The echo signal can be represented as:

[0090]

[0091] Where, hadamard represents the hadamard product. For additive white Gaussian noise, the Doppler frequency shift can be expressed as:

[0092]

[0093] S503: The first device performs channel estimation based on the echo signal and obtains the estimation result, which includes a time delay estimate and a phase estimate.

[0094] Upon receiving the echo signal, the first device performs channel estimation based on the echo signal and obtains estimation results including time delay estimates and phase estimates.

[0095] In some implementations, the first device may further sample the received signal using an analog-to-digital converter before performing channel estimation based on the received signal, so as to use the sampled echo signal for channel estimation. Specifically, the sampled echo signal can be represented as:

[0096]

[0097] in, For delay information, a fourth-order tensor, This is a fourth-order tensor for phase information. Specifically, the tensor dimension... The horizontal spatial dimension; Vertical spatial dimension; This represents the time-domain dimension of the time slot, used for parameter extraction. This represents the sampling time domain dimension, used for signal processing.

[0098] The first device can perform channel estimation by using frequency domain processing or by using time domain matched filtering; this embodiment does not limit the method.

[0099] In some implementations, channel estimation can be performed using the following frequency domain processing method. Specifically, the first device can use Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) methods to perform channel estimation and obtain a third-order tensor of time delay estimation, phase-signal-noise ratio estimation information. Specifically, this step can be divided into the following sub-steps:

[0100] (1) The first device converts the sampled sequences of the transmitted signal and the echo signal in the time slot to the frequency domain using FFT, wherein the transmitted signal is represented as:

[0101]

[0102] Where i is the frequency domain sampling number.

[0103] The echo signal can be represented as:

[0104]

[0105] (2) The first device determines the channel frequency shift response based on the transmitted signal and the echo signal, and then converts it to the time domain using IFFT to obtain the channel impulse response. The channel frequency response can be expressed as:

[0106]

[0107] The channel impulse response can be expressed as:

[0108]

[0109] (3) The first device obtains the time delay estimate and phase estimate of different array elements at different times by identifying the peak position.

[0110] The time delay estimation information tensor can be expressed as:

[0111]

[0112] The phase estimation information tensor can be represented as:

[0113]

[0114] As can be seen, this embodiment divides the time slot into time domain dimensions and performs parameter estimation by sampling within the time slot, thereby extracting time slot-level time domain information from the received signal, providing conditions for direct estimation of lateral velocity.

[0115] S504: The first device determines the frequency offset based on the time delay estimate and the phase estimate. The frequency offset is determined based on the frequency offset of the echo signals received by different array elements in the two-dimensional array antenna.

[0116] The frequency offset includes horizontal and vertical frequency differences, and may also include a fixed offset. Specifically, the horizontal frequency difference is used to determine the horizontal velocity in the lateral velocity, the vertical frequency difference is used to determine the vertical velocity in the lateral velocity, and the fixed offset is used to determine the radial velocity. That is, this embodiment can determine not only the lateral velocity but also the longitudinal velocity, thereby determining the three-dimensional velocity of the target.

[0117] The horizontal frequency difference refers to the difference between the Doppler frequency shift of a horizontal element in a two-dimensional array antenna and the Doppler frequency shift of the reference element, while the vertical frequency difference refers to the difference between the Doppler frequency shift of a vertical element in a two-dimensional array antenna and the Doppler frequency shift of the reference element. For example, for Figure 4 The area array antenna shown, for the array element E mn The horizontal frequency difference of this array refers to the difference between the frequency offset of the m-th array and the frequency offset of the 1st array; the vertical frequency difference of this array refers to the difference between the frequency offset of the n-th row and the frequency offset of the 1st row.

[0118] Specifically, the first device can determine the frequency offset in the following way:

[0119] (1) The first device determines the position parameters corresponding to the target based on the time delay estimate and the phase estimate. The position parameters include distance, spatial azimuth and pitch angle.

[0120] In this embodiment, the first device will use time delay estimates and phase estimates to estimate the target's position parameters. Specifically, the position parameters include the distance between the target and the first device and the target's pointing angle.

[0121] In some implementations, the first device can determine the position parameters corresponding to the target in the following ways:

[0122] The first device obtains a vector set based on the phase estimate; the first device determines the distance between the target and the first device based on the time delay estimate; the first device determines the spatial azimuth angle corresponding to the target based on the vector combination and the first coefficient in the coefficient set; the first device determines the pitch angle corresponding to the target based on the vector set and the second coefficient in the coefficient set.

[0123] The vector set includes reference vectors for calculating position parameters, specifically, reference vectors for calculating the spatial azimuth and elevation angles. It further includes reference vectors for calculating velocity, specifically, reference vectors for radial velocity, vertical velocity, and horizontal velocity. The coefficient set includes coefficients used to calculate position parameters, such as spatial azimuth sampling coefficients and elevation sampling coefficients. Furthermore, the coefficient set may also include coefficients used to calculate velocity, such as radial velocity sampling coefficients, horizontal velocity sampling coefficients, and vertical velocity sampling coefficients. The coefficient set is determined based on the number of horizontal and vertical array elements in the two-dimensional array antenna and the number of time slots occupied by the transmitted signal.

[0124] It should be noted that there is a one-to-one correspondence between the vectors included in the vector set and the coefficients in the coefficient set. That is, if the vector set includes a reference vector for vertical velocity and a reference vector for horizontal velocity, then the coefficient set includes sampling coefficients for vertical velocity and sampling coefficients for horizontal velocity.

[0125] The vector set can be represented as follows: from top to bottom, the vector set includes five reference vectors, corresponding to the reference vector for the spatial azimuth angle, the reference vector for the pitch angle, the reference vector for the radial velocity, the reference vector for the horizontal velocity, and the reference vector for the vertical velocity.

[0126]

[0127] When the first device determines the distance between the target and the first device based on the time delay estimate, it can be achieved using the following formula, where c represents the speed of light:

[0128]

[0129] When the first device determines the spatial azimuth angle of the target based on the vector set and the first coefficient in the coefficient set, it can be achieved using the following formula:

[0130]

[0131] Where λ is the wavelength of the sensed signal and is the first coefficient in the coefficient set.

[0132] The coefficient set can be represented as follows: from top to bottom, the coefficient set includes spatial azimuth sampling coefficients, pitch sampling coefficients, radial velocity sampling coefficients, horizontal velocity sampling coefficients, and vertical velocity sampling coefficients.

[0133]

[0134] When the first device determines the pitch angle corresponding to the target based on the vector set and the second coefficient in the coefficient set, it can be achieved using the following formula:

[0135]

[0136] Where is the second coefficient in the coefficient set.

[0137] As can be seen, this embodiment decouples distance and pointing angle, and can realize a closed-loop position-velocity sensing method based on the least squares method.

[0138] (2) The first device determines the frequency offset based on the position parameters and phase estimates.

[0139] After acquiring the position parameters corresponding to the target, the first device uses the position parameters and phase estimates to determine the frequency offset. Specifically, the first device can determine the horizontal frequency difference, the vertical frequency difference, and the fixed frequency shift.

[0140] The first device determines the horizontal frequency difference based on distance, vector set, third coefficient in coefficient set, and first parameter set. The first parameter set includes the number of horizontal array elements M, the number of time slots occupied by the sensed signal K, the spacing dm between horizontal array elements, and the sampling interval T0.

[0141] Specifically, the first device can determine the horizontal frequency difference using the following formula:

[0142]

[0143] The first device determines the vertical frequency difference based on distance, vector set, the fourth coefficient in the coefficient set, and the second parameter set. The second parameter set includes the number of vertical array elements N and K, and the spacing d between the vertical array elements. N And at least one of T0. In this implementation, the two-dimensional array antenna included in the first device includes M. N array elements.

[0144] Specifically, the first device can determine the vertical frequency difference using the following formula:

[0145]

[0146] The first device determines the fixed frequency shift based on the vector set, the fifth coefficient in the coefficient set, and the third parameter set. The third parameter set includes K and T0.

[0147] Specifically, the first device can determine the fixed frequency shift using the following formula:

[0148]

[0149] S505: The first device determines the lateral velocity corresponding to the target based on the frequency offset.

[0150] In this embodiment, after determining the frequency offset between the echo signals received by different array elements in the two-dimensional array antenna, the first device can determine the lateral velocity corresponding to the target based on the frequency offset.

[0151] Specifically, the frequency offset includes the horizontal frequency difference and the vertical frequency difference. The first device can determine the horizontal velocity corresponding to the target based on the horizontal frequency difference, and determine the vertical velocity corresponding to the target based on the vertical frequency difference.

[0152] In some implementations, the first device determines the horizontal velocity of the target based on the wavelength of the sensing signal, the trigonometric function value corresponding to the spatial azimuth angle, and the horizontal frequency difference.

[0153] In practical implementation, the first device can determine the horizontal velocity corresponding to the target using the following formula:

[0154]

[0155] In some implementations, the first device determines the vertical velocity of the target based on the wavelength corresponding to the sensing signal, the trigonometric function value corresponding to the pitch angle, and the vertical frequency difference.

[0156] In practical implementation, the first device can determine the vertical velocity corresponding to the target using the following formula.

[0157]

[0158] In some implementations, if the frequency offset includes a fixed frequency shift, the first device can also sense the wavelength of the signal and the fixed frequency shift to determine the radial velocity of the target.

[0159] In practical implementation, the first device can determine the radial velocity corresponding to the target using the following formula:

[0160]

[0161] As can be seen from the above scheme, the first device can make full use of the time and spatial resources in the echo signal, and directly estimate the lateral velocity (horizontal velocity and vertical velocity) through the Doppler frequency difference between different array elements, which has the advantages of high real-time performance, high precision and strong universality.

[0162] For a better understanding of the target velocity estimation process, see [link to relevant documentation]. Figure 6 The estimation process shown in the diagram obtains the time delay estimation tensor X, the phase estimation tensor X, and the signal-to-noise ratio (SNR) estimation tensor X through channel estimation. Then, the range Y is determined based on the time delay estimation tensor X; the spatial azimuth angle X and elevation angle X are determined based on the phase estimation tensor X; and the SNR estimation tensor X is used to determine the SNR estimate Y. Next, the radial velocity is determined based on the phase estimation tensor; the horizontal velocity is calculated based on the phase estimation tensor, range, and spatial phase angle; and the vertical velocity is calculated based on the phase estimate, range, and elevation angle.

[0163] To facilitate understanding of the feasibility of this scheme, the implementation process of parameter estimation will be derived below:

[0164] First, the least squares cost function, i.e., the sum of squared residuals, can be constructed as follows:

[0165]

[0166] The parameter estimates for finding the minimum cost function can be obtained by solving a system of homogeneous equations based on the condition that the partial derivatives are zero:

[0167]

[0168] By solving the above system of equations, the set of coefficients can be obtained as follows:

[0169]

[0170] Thus, the least squares estimates of the spatial azimuth, pitch, radial velocity, lateral velocity, and vertical velocity are obtained, which conform to the expressions given in this step.

[0171] Furthermore, to better describe the effectiveness of this solution, a performance evaluation method for speed estimation is given in the current embodiment:

[0172] The accuracy of the joint parameter estimation of range-azimuth-pitch-radial velocity-lateral velocity-vertical velocity can be assessed based on the Cramér-Rao Bound (CRB). First, the Fisher Information Matrix (FIM) is solved, which has the following form:

[0173]

[0174] The reason why the off-diagonal elements in the first row or first column are zero is that the time delay information and the phase information can be decoupled, so the diagonal elements of FIM can be solved as follows:

[0175]

[0176] in, For the sampled cumulative function, the non-zero off-diagonal elements can be solved as follows:

[0177]

[0178] By solving for the inverse matrix of the FIM, the parameter estimate CRB can be obtained as follows:

[0179]

[0180] Therefore, the specific expression for the lower bound of the variance of the parameter estimate is:

[0181]

[0182] Based on the above expression, the functional relationship between the estimation accuracy of different parameters and the resource proportion can be obtained as follows: Figures 7a to 7d As shown, that is:

[0183] 1) The variance of the distance estimation is inversely proportional to the square of the bandwidth;

[0184] 2) Variance of spatial azimuth estimation and Inversely proportional;

[0185] 3) Variance of pitch angle estimation and Inversely proportional;

[0186] 4) Radial velocity estimation variance and Inversely proportional;

[0187] 5) Variance of horizontal velocity estimation and and Inversely proportional;

[0188] 6) Variance of vertical velocity estimation and and Inversely proportional.

[0189] In some implementations, when the first device performs channel estimation based on the echo signal, the estimation result may also include a signal-to-noise ratio (SNR) estimate. Then, the SNR estimate is used to adjust the sensing window corresponding to the sensing signal in real time to meet the time-domain resource requirements of the next sensing cycle.

[0190] Specifically, if the signal-to-noise ratio (SNR) estimate is greater than or equal to the SNR threshold, the first device determines the sensing window corresponding to the sensing signal as the first sensing window; if the SNR estimate is less than the SNR threshold, the first device determines the sensing window corresponding to the sensing signal as the second sensing window. The number of time slots corresponding to the second sensing window is greater than the number of time slots corresponding to the first sensing window.

[0191] That is, when the signal-to-noise ratio (SNR) estimate is greater than or equal to the SNR threshold, it indicates a high SNR, and the real-time performance of sensing can be improved by reducing the window size; when the SNR estimate is less than the SNR threshold, it indicates a low SNR, and the sensing accuracy can be improved by expanding the window size. Therefore, the number of time slots occupied by the sensing signal can be adjusted in real time based on the SNR estimate of the echo signal within the current sensing period to make reasonable use of time-domain resources.

[0192] The number of time slots corresponding to the first sensing window is determined based on the sensing interval T, the sampling interval T0, and the signal-to-noise ratio (SNR) estimate. The number of time slots corresponding to the second sensing window is determined based on the number of time slots K corresponding to the sensing signal, the sensing interval T, the sampling interval T0, and the SNR estimate. The SNR threshold is based on the sensing interval T, the sampling interval T0, and the number of time slots K corresponding to the sensing signal transmitted in the current sensing period.

[0193] For ease of understanding, see [link to relevant documentation]. Figure 8 The time-domain resource update flowchart shown includes the following processes:

[0194] (1) The first device determines the signal-to-noise ratio threshold based on the initial configuration information.

[0195] Specifically, the first device can determine the signal-to-noise ratio threshold using the following formula:

[0196]

[0197] (2) The first device determines whether the signal-to-noise ratio estimate is greater than or equal to the signal-to-noise ratio threshold. If it is, proceed to step (3); otherwise, proceed to step (5).

[0198] Specifically, when the signal-to-noise ratio estimate is greater than or equal to the signal-to-noise ratio threshold, i.e. When using the first sensing window, which can also be called a single-cycle window, a first sensing window is adopted.

[0199] When the signal-to-noise ratio estimate is less than the signal-to-noise ratio threshold, i.e. When using a second sensing window, which can also be called a multi-period window mode, a second sensing window is employed.

[0200] (3) The first device determines the sensing window corresponding to the sensing signal as the first sensing window.

[0201] (4) The first device calculates the number of time slots corresponding to the first sensing window.

[0202] Specifically, the first device can calculate the number of time slots corresponding to the first sensing window using the following formula:

[0203]

[0204] in, This is for rounding up.

[0205] (5) The first device determines the sensing window corresponding to the sensing signal as the second sensing window.

[0206] (6) The first device calculates the number of time slots corresponding to the second sensing window.

[0207] Specifically, the first device can calculate the number of time slots corresponding to the second sensing window using the following formula:

[0208]

[0209] To better understand the rationale for determining the signal-to-noise ratio threshold based on the sensing interval T, the sampling interval T0, and the number of time slots K corresponding to the sensing signal transmitted in the current sensing period, the derivation process is given below:

[0210] To meet the requirements of single-site sensing beam alignment or beam focusing, a sufficiently large beam gain is needed. Therefore, the expected beam gain must satisfy the following:

[0211]

[0212] Substituting the expression for parameter estimation CRB into the above equation, we can obtain the effective conditions for beam alignment or beam focusing as follows:

[0213]

[0214] Considering that the lateral velocity estimation has a dominant influence on the expectation of beam gain, the above equation can be further simplified to:

[0215]

[0216] By utilizing the signal-to-noise ratio threshold, time slot resources can be dynamically planned, thereby achieving the optimal trade-off between sensing accuracy and real-time performance.

[0217] It should be noted that the first device can be a network device or a terminal device. Specifically, the first device may include an initialization module, a signal transceiver module, a signal processing module, a parameter estimation module, a coordinate transformation module, and a configuration update module. Among them, the initialization module is used to obtain initial configuration information and initial state information, and adjust the steering matrix corresponding to the two-dimensional array antenna according to these two types of information.

[0218] The signal transceiver module is used to send sensing signals and receive echo signals, and to sample the echo signals.

[0219] The signal processing module is used to extract time delay estimates, phase estimates, and signal-to-noise ratio estimates from the sampled echo signals.

[0220] The parameter estimation module is used to perform three-dimensional velocity estimation based on the Doppler information contained in the phase information.

[0221] The coordinate transformation module is used to transform three-dimensional velocity from the array coordinate system to the geographic coordinate system.

[0222] The configuration update module is used to adjust the sensing window in real time based on the signal-to-noise ratio estimate.

[0223] When the first device is a terminal device, since the terminal device's posture is not fixed and the array coordinate system is changing, the coordinate transformation module needs to use the corresponding posture information of the terminal device to transform the three-dimensional velocity from the array coordinate system to the geographic coordinate system.

[0224] Specifically, the technical solution provided in this application has the following advantages:

[0225] (1) Make full use of the time-space information in the echo signal to directly estimate the transverse velocity (horizontal velocity / vertical velocity) through the Doppler frequency difference between different phases.

[0226] Specifically, this embodiment fully leverages the coupling information between the time and spatial domains in the echo signal. Utilizing the horizontal and vertical element layout of the area array antenna, it directly calculates the horizontal and vertical velocity components by extracting the Doppler frequency difference of the echoes received by different elements. Compared to the indirect derivation or missing dimensions issues of traditional schemes, this embodiment does not rely on multi-time-point sampling differences or motion model assumptions. Instead, it utilizes the inherent spatial dimensional differences of the area array to achieve direct estimation of the lateral velocity, combining it with the radial velocity component to form a complete three-dimensional velocity vector. This not only adapts to complex motion patterns but also avoids the real-time performance loss caused by multi-time-point sampling. Simultaneously, it is compatible with multiple signal types such as pilot, control, and data signals, significantly improving the dimensional completeness and scenario versatility of single-station sensing.

[0227] (2) Based on the scenario assumption, the distance domain and angular domain parameters are decoupled, and a single-station position-velocity parameter sensing closed method based on least squares is proposed.

[0228] Specifically, this embodiment introduces the scenario assumption that "the distance d is much larger than the antenna aperture D," effectively decoupling the range domain (time delay information) and angular domain (azimuth and elevation information) parameters, simplifying the coupling complexity of parameter estimation. Based on this, this embodiment abandons the complex algorithms of traditional schemes such as inversion and singular value decomposition, proposing a closed-form solution method based on least squares: first, it extracts the third-order tensor information of time delay and phase from the fourth-order tensor of the channel state; then, it uses the least squares method to solve for the range-spatial azimuth-elevation estimates; finally, based on this position information and the Doppler information in the phase, it further calculates the radial-horizontal-vertical three-dimensional velocity estimates. The closed-form solution design not only ensures the simplicity of theoretical derivation and avoids the iterative overhead of Kalman filtering or the noise sensitivity of singular value decomposition, but also significantly reduces the hardware computational complexity, making it more suitable for the engineering implementation requirements of single-station sensing. Compared with the performance degradation problem of traditional schemes in low signal-to-noise ratio and long-distance scenarios, the decoupling design and least squares estimation in this embodiment improve the robustness of parameter estimation and maintain high accuracy even in long-distance single-station sensing scenarios.

[0229] (3) Real-time closed-loop estimation of signal-to-noise ratio information is used to correct the perception window, thereby improving resource utilization while ensuring the effectiveness of perception.

[0230] Specifically, this embodiment constructs a closed-loop optimization mechanism of "signal-to-noise ratio estimation - dynamic window adjustment": First, by jointly solving the fourth-order tensor of the transmitted signal, received signal, and channel state, a third-order tensor form of signal-to-noise ratio estimate is obtained in real time. This closed-loop estimation method ensures the real-time performance and accuracy of the signal-to-noise ratio assessment without requiring additional complex measurement procedures. Then, based on a preset signal-to-noise ratio threshold, the sensing window mode is dynamically selected. Specifically, when the signal-to-noise ratio is greater than or equal to the threshold, a single-cycle window mode is adopted to minimize time-domain resource overhead; when the signal-to-noise ratio is less than the threshold, it automatically switches to a multi-cycle window mode, increasing the number of sensing window slots to achieve sufficient signal accumulation. Compared with the fixed window design of traditional schemes, this dynamic adjustment mechanism achieves a balance between resource utilization and sensing effectiveness: in high signal-to-noise ratio scenarios, unnecessary resource occupation is avoided, reserving more time-domain resources for communication and other functions; in low signal-to-noise ratio scenarios, window expansion ensures the accuracy of parameter estimation, solving the problem of insufficient sensing stability in complex scenarios in traditional schemes. Meanwhile, the adjustment of the sensing window is based on the flexible correction of the number of time slots, which adapts to the system parameter configuration of single-station sensing, further improving the practicality of the method.

[0231] The technical solution provided in this application can be applied to the following scenarios:

[0232] 1. Low-Altitude UAV Supervision Scenarios

[0233] Scenario Description: This includes the management and control of consumer-grade drones and logistics delivery drones flying at low altitudes in urban areas, as well as drones in sensitive areas such as airport airspace and power corridors. In these scenarios, drones are small in size, fly at high speeds, and have flexible three-dimensional motion trajectories. They are also susceptible to obstruction by buildings or electromagnetic interference, requiring single-station equipment (such as roadside base stations or portable terminals) to achieve rapid and accurate three-dimensional speed monitoring.

[0234] Technology Applications and Advantages:

[0235] (1) Accurate three-dimensional velocity acquisition: This embodiment relies on the horizontal / vertical array layout of the area array antenna to directly estimate the horizontal and vertical velocities of the UAV through the Doppler frequency difference between different arrays. Combined with the radial velocity, a complete three-dimensional velocity vector is formed, which solves the limitation of traditional single-station sensing that can only measure two-dimensional velocity and accurately tracks the complex motion states of the UAV such as climbing, diving, and lateral movement.

[0236] (2) Dynamic anti-interference perception: In response to multipath interference and signal-to-noise ratio fluctuations in low-altitude environments, this embodiment uses closed-loop estimation of the signal-to-noise ratio and dynamically adjusts the perception window. For example, when the signal-to-noise ratio is high, a single-cycle window is used to achieve millisecond-level speed updates, and when the signal-to-noise ratio is low (such as in occluded scenarios), it automatically switches to a multi-cycle window to accumulate signals, ensuring that the UAV can still measure speed stably at long distances or with weak reflections, thus meeting the real-time and reliability requirements of the monitoring scenario;

[0237] (3) Flexible dual-end deployment: The base station side can serve as a fixed monitoring node to cover a large area of ​​low-altitude regions, while the terminal equipment side (such as portable control equipment) can serve as a mobile monitoring unit to achieve blind spot monitoring in sensitive areas. It can independently complete three-dimensional velocity estimation without the need for multi-station collaboration, thus reducing deployment costs.

[0238] 2. Vehicle-to-Everything (V2X) High-Precision Positioning Scenarios

[0239] Scenario Description: This includes autonomous driving fleets on highways, intelligent connected vehicles on urban roads, and collaborative driving of automated guided vehicles (AGVs) within industrial parks. In these scenarios, vehicles move at high speeds and their trajectories are highly variable, requiring single-station equipment (roadside base stations and vehicle-mounted terminals) to acquire real-time three-dimensional vehicle speeds (including horizontal travel speed and vertical bump speed) to support high-precision positioning and safe obstacle avoidance decisions.

[0240] Technology Applications and Advantages:

[0241] (1) Full-dimensional speed perception: This embodiment breaks through the limitation of existing single-station speed measurement that only focuses on horizontal speed. It directly calculates the horizontal speed and vertical bump speed of the vehicle through the Doppler frequency difference in the phase information, providing more comprehensive motion state data for the autonomous driving system and improving the positioning accuracy under complex road conditions (such as potholes and slopes).

[0242] (2) Low-latency closed-loop solution: The closed-loop solution method based on the least squares method is adopted, which does not require iterative calculation or multi-time point sampling. Compared with existing methods such as Kalman filtering and singular value decomposition, the computational complexity is lower, and microsecond-level speed updates can be achieved, which meets the high reliability and low latency requirements of vehicle networking and supports real-time obstacle avoidance and fleet collaborative control.

[0243] (3) Dynamic resource optimization: Based on the changes in signal-to-noise ratio during vehicle operation (such as the decrease in signal-to-noise ratio in tunnels and the increase in signal-to-noise ratio on open roads), the number of time slots in the sensing window is automatically adjusted. When the signal-to-noise ratio is high, time domain resources are saved for communication data transmission. When the signal-to-noise ratio is low, the sensing window is extended to ensure speed measurement accuracy, thereby achieving a dynamic balance between sensing and communication resources and adapting to the concurrent needs of multiple services in the Internet of Vehicles.

[0244] 3. Industrial Robot Collaboration Scenarios

[0245] Scenario Description: This scenario encompasses collaborative operations of multiple industrial robots within a factory workshop (such as robotic arm assembly and material handling robot interaction), as well as path planning and collision avoidance for unmanned vehicles in a warehouse environment. In these scenarios, high robot motion precision is required, and the workspace is dense. Individual station equipment (workshop base station, robot terminal) must perceive the robot's three-dimensional velocity in real time to avoid collisions and ensure operational synchronization.

[0246] Technology Applications and Advantages:

[0247] (1) Millimeter-level three-dimensional velocity measurement: This embodiment extracts high-precision phase and time delay information through the fourth-order tensor of the channel state, and combines the least squares to decouple the distance domain and angular domain parameters. The accuracy of three-dimensional velocity estimation can reach the millimeter level, which accurately matches the high-precision motion control requirements of industrial robots and solves the problem of collaborative lag caused by the large speed error of traditional single-station sensing.

[0248] (2) Resistance to interference from complex environments: There are many metal equipment and complex electromagnetic environments in the workshop, which can easily lead to fluctuations in signal-to-noise ratio. This embodiment dynamically adjusts the sensing window through real-time signal-to-noise ratio estimation. In scenarios with strong multipath reflection, the sensing window is automatically extended to accumulate signals, ensuring that the robot can still output three-dimensional velocity data stably in occluded or strong interference environments, thus ensuring the continuity of collaborative operations;

[0249] (3) Lightweight deployment of terminals: The robot terminal can be used as a single-station sensing node without relying on the collaboration of multiple base stations in the workshop. It can independently complete the three-dimensional velocity estimation of itself or the surrounding robots. The compact array antenna layout is adapted to the miniaturized design of the robot. At the same time, the closed-loop solution method reduces the terminal's computing power consumption, which meets the low power consumption requirements of industrial equipment.

[0250] 4. Human Motion Monitoring Scenarios

[0251] Scenario Description: These scenarios include fall detection for the elderly in smart elderly care, exercise posture analysis in gyms, and abnormal behavior recognition in security scenarios. In these scenarios, human movements are small in amplitude, slow in speed, and have highly variable three-dimensional postures, requiring single-station devices (home base stations, wearable terminals) to achieve non-contact, high-precision three-dimensional velocity sensing.

[0252] Technology Applications and Advantages:

[0253] (1) Micro-motion 3D capture: This embodiment uses an optimized sensing window adjustment mechanism to automatically extend the sensing window in micro-motion scenarios (such as when an elderly person gets up or when the range of motion of a fitness exercise is small), thereby improving the signal accumulation effect. Combined with the high-resolution Doppler frequency difference estimation of the array antenna, it accurately captures the horizontal movement speed and vertical rise and fall speed of the human body (such as jumping or falling), thus solving the problem that traditional single-station sensing is not sensitive to micro-motion speed estimation.

[0254] (2) Low power terminal adaptation: When the wearable terminal is used as a single-station sensing node, the closed-loop solution method in this embodiment reduces the computational complexity. At the same time, the sensing window is dynamically adjusted according to the signal-to-noise ratio. For example, when the signal-to-noise ratio is high, the sensing cycle is shortened to reduce energy consumption, and when the signal-to-noise ratio is low, the window is appropriately extended to ensure accuracy, balancing the monitoring reliability and terminal battery life, and adapting to long-term use scenarios such as smart elderly care and fitness monitoring.

[0255] (3) Non-contact stable monitoring: No need for the human body to carry additional sensors. Long-distance three-dimensional speed monitoring can be achieved through single-station sensing of base station or terminal, avoiding the restriction of human activities by contact devices; at the same time, the multi-cycle window mode can still measure speed stably when the person is blocked by furniture (low signal-to-noise ratio), ensuring the continuity of monitoring in complex environments such as home and gym.

[0256] It should be understood that Figures 1 to 8 The flowcharts or scene diagrams shown are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples illustrated. In fact, those skilled in the art can interpret the embodiments based on... Figures 1 to 8 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0257] The above text combined Figures 1 to 8 This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figures 9 to 10 The device embodiments of this application are described in detail below. It should be understood that the communication device of this application embodiment can execute the various communication methods of the foregoing embodiments of this application, that is, the specific working processes of the various products below can be referred to the corresponding processes in the foregoing method embodiments.

[0258] In the embodiments described above, the terminal device may execute some or all of the steps in each embodiment; the network device may execute some or all of the steps in each embodiment. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments, and it is not necessary to execute all the operations in the embodiments of this application. Moreover, the sequence number of each step does not imply 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 this application.

[0259] Figure 9 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 9 As shown, the communication device 900 may include a communication module 920. The communication module 920 can implement corresponding communication functions, which can be internal communication functions of the communication device 900 or communication functions between the communication device 900 and other devices. Optionally, the communication module 920 may also be referred to as a communication interface or transceiver module. Optionally, the communication device 900 further includes a processing module 910. The processing module 910 can implement corresponding processing functions.

[0260] Optionally, the communication device 900 further includes a storage module, which can be used to store instructions and / or data; the processing module 910 can read the instructions and / or data in the storage module so that the communication device 900 can implement the aforementioned method embodiments.

[0261] In one possible design, the communication device 900 may correspond to the first device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the first device. The communication device 900 may be used to perform the steps or processes performed by the first device in any of the above method embodiments.

[0262] For example, the communication module 920 is used to transmit sensing signals via a two-dimensional array antenna;

[0263] The communication module 920 is also used to receive echo signals through the two-dimensional array antenna, wherein the echo signal refers to the signal after the sensing signal is reflected by the target;

[0264] Processing module 910 is used to perform channel estimation based on echo signals and obtain estimation results, which include time delay estimates and phase estimates;

[0265] The processing module 910 is also used to determine the frequency offset based on the time delay estimate and the phase estimate. The frequency offset is determined based on the frequency offset of the echo signal received by different array elements in the two-dimensional array antenna.

[0266] The processing module 910 is also used to determine the lateral velocity corresponding to the target based on the frequency offset.

[0267] In some implementations, the processing module 910 is specifically used to determine the position parameters corresponding to the target based on the time delay estimate and the phase estimate, the position parameters including distance, azimuth angle and elevation angle; and to determine the frequency offset based on the position parameters and the phase estimate.

[0268] In some implementations, the processing module 910 is specifically configured to: obtain a vector set based on the phase estimate, the vector set including a reference vector for calculating the position parameter; determine the distance between the target and the first device based on the time delay estimate; determine the spatial azimuth angle corresponding to the target based on the vector set and a first coefficient in the coefficient set, wherein the vectors in the vector set correspond one-to-one with the coefficients in the coefficient set; and determine the pitch angle corresponding to the target based on the vector set and a second coefficient in the coefficient set.

[0269] In some embodiments, the processing module 910 is specifically configured to determine the horizontal frequency difference based on the distance, the vector set, a third coefficient in the coefficient set, and a first parameter set, wherein the first parameter set includes at least one of the following: the number of horizontal array elements M, the number of time slots K occupied by the sensed signal, the spacing dm between the horizontal array elements, and the sampling interval T0, wherein the sampling interval is used to indicate sampling of the echo signal, and the vector set is determined based on the phase estimation vector; and to determine the vertical frequency difference based on the distance, the vector set, a fourth coefficient in the coefficient set, and a second parameter set, wherein the second parameter set includes at least one of the following: the number of vertical array elements N, the K, the spacing dn between the vertical array elements, and T0, wherein the two-dimensional array antenna includes the M N array elements.

[0270] In some implementations, the frequency offset includes a horizontal frequency difference and a vertical frequency difference. The processing module 910 is specifically used to determine the horizontal velocity corresponding to the target based on the horizontal frequency difference and to determine the vertical velocity corresponding to the target based on the vertical frequency difference. The lateral velocity includes the horizontal velocity and the vertical velocity.

[0271] In some implementations, the processing module 910 is specifically used to determine the horizontal velocity corresponding to the target based on the wavelength corresponding to the sensing signal, the trigonometric function value corresponding to the spatial azimuth angle, and the horizontal frequency difference; and to determine the vertical velocity corresponding to the target based on the wavelength, the trigonometric function value corresponding to the pitch angle, and the vertical frequency difference.

[0272] In some implementations, the frequency offset also includes a fixed frequency shift. The processing module 910 is further configured to determine the fixed frequency shift based on the vector set, the fifth coefficient in the coefficient set, and the third parameter set, wherein the third parameter set includes the number of time slots K occupied by the sensing signal and the sampling interval T0; and to determine the radial velocity corresponding to the target based on the wavelength corresponding to the sensing signal and the fixed frequency shift.

[0273] In some implementations, the estimation result also includes a signal-to-noise ratio estimate, and the processing module 910 is further configured to determine the size of the sensing window corresponding to the sensing signal based on the signal-to-noise ratio estimate.

[0274] In some implementations, the processing module 910 is specifically configured to determine the sensing window corresponding to the sensing signal as a first sensing window if the signal-to-noise ratio estimate is greater than or equal to the signal-to-noise ratio threshold; and to determine the sensing window corresponding to the sensing signal as a second sensing window if the signal-to-noise ratio estimate is less than the signal-to-noise ratio threshold, wherein the number of time slots corresponding to the second sensing window is greater than the number of time slots corresponding to the first sensing window.

[0275] In some implementations, the number of time slots corresponding to the first sensing window is determined based on the sensing interval T, the sampling interval T0, and the signal-to-noise ratio estimate;

[0276] The number of time slots corresponding to the second sensing window is determined based on the number of time slots K corresponding to the sensing signal sent in the current sensing cycle, the sensing interval T, the sampling interval T0, and the signal-to-noise ratio estimate.

[0277] In some implementations, the signal-to-noise ratio threshold is based on the sensing interval T, the sampling interval T0, and the number of time slots K corresponding to the sensing signal transmitted in the current sensing period.

[0278] In some embodiments, the processing module 910 is further configured to acquire initial configuration information corresponding to the first device and initial state information corresponding to the target before the communication module 920 sends the sensing signal; and adjust the steering matrix corresponding to the two-dimensional array antenna according to the initial configuration information and the initial state information.

[0279] In some implementations, the initial configuration information includes one or more of the following: the wavelength of the sensing signal, the sensing interval T, the time slot length Ts, the sampling interval T0, the number of time slots occupied by the sensing signal K, the number of samples per time slot Q, the horizontal array element interval dm, the vertical array element interval dn, the number of horizontal array elements M, the number of vertical array elements N, and the pointing angle of the two-dimensional array antenna.

[0280] The initial state information includes one or more of the following: initial position information and initial velocity information.

[0281] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0282] Figure 10This is another schematic block diagram of the communication device 1000 provided in the embodiments of this application. The communication device 1000 may be a chip, chip system, or processor, etc., used by a terminal device or network device to implement the above-described methods. The communication device 1000 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0283] like Figure 10 As shown, the communication device 1000 may include one or more processors 1010, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 1010 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 1000 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0284] In an alternative design, the processor 1010 may also store instructions and / or data that can be executed by the processor 1010 to cause the communication device 1000 to perform the methods described in the above method embodiments.

[0285] In another alternative design, the communication device 1000 may include a communication interface 1020 for implementing receiving and transmitting functions. For example, the communication interface 1020 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0286] Optionally, the communication device 1000 may include one or more memories 1030, which may store instructions that can be executed on the processor 1010, causing the communication device 1000 to perform the methods described in the above method embodiments. Optionally, the memories 1030 may also store data. Optionally, the processor 1010 may also store instructions and / or data. The processor 1010 and the memories 1030 may be provided separately or integrated together.

[0287] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0288] In one implementation, the communication device 1000 may correspond to the first device in the above method embodiments and may be used to execute the various steps and / or processes executed by the first device in the above method embodiments. The processor 1010 may be used to execute instructions stored in the memory 1030, and when the processor 1010 executes the instructions stored in the memory, the processor 1010 is used to execute the various steps and / or processes of the above method embodiments corresponding to the first device.

[0289] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0290] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0291] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0292] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0293] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned first device.

[0294] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the first device in any of the foregoing method embodiments.

[0295] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code that, when run on a computer, causes the computer to execute the various steps or processes executed by the first device in any of the foregoing method embodiments.

[0296] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0297] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0298] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0299] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0300] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply 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 this application.

[0301] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A speed sensing method, characterized in that, The method includes: The first device transmits sensing signals via a two-dimensional array antenna; The first device receives the echo signal through the two-dimensional array antenna, where the echo signal refers to the signal after the sensing signal is reflected by the target; The first device performs channel estimation based on the echo signal and obtains estimation results, which include time delay estimates and phase estimates. The first device determines the position parameters corresponding to the target based on the time delay estimate and the phase estimate, wherein the position parameters include distance, spatial azimuth angle and pitch angle; The first device determines the frequency offset based on the position parameters and the phase estimate; the frequency offset is determined based on the frequency offset corresponding to the echo signals received by different array elements in the two-dimensional array antenna; the frequency offset includes horizontal frequency difference and vertical frequency difference. The first device determines the lateral velocity corresponding to the target based on the frequency offset, the lateral velocity including the horizontal velocity determined based on the horizontal frequency difference and the vertical velocity determined based on the vertical frequency difference.

2. The method according to claim 1, characterized in that, The first device determines the position parameters corresponding to the target based on the time delay estimate and the phase estimate, including: The first device obtains a vector set based on the phase estimate, the vector set including a reference vector for calculating the position parameter; The first device determines the distance between the target and the first device based on the time delay estimate; The first device determines the spatial azimuth angle corresponding to the target based on the vector set and the first coefficient in the coefficient set, wherein the vectors in the vector set correspond one-to-one with the coefficients in the coefficient set; The first device determines the pitch angle corresponding to the target based on the vector set and the second coefficient in the coefficient set.

3. The method according to claim 2, characterized in that, The first device determines the frequency offset based on the position parameters and the phase estimate, including: The first device determines the horizontal frequency difference based on the distance, the vector set, the third coefficient in the coefficient set, and the first parameter set. The first parameter set includes at least one of the following: the number of horizontal array elements M, the number of time slots K occupied by the sensed signal, the spacing dm between the horizontal array elements, and the sampling interval T0. The sampling interval is used to indicate sampling of the echo signal. The vector set is determined based on the phase estimate. The first device determines the vertical frequency difference based on the distance, the vector set, the fourth coefficient in the coefficient set, and the second parameter set. The second parameter set includes at least one of the following: the number of vertical array elements N, the K, the spacing dn between the vertical array elements, and T0. The two-dimensional array antenna includes the M*N array elements.

4. The method according to claim 1, characterized in that, The first device determines the horizontal velocity corresponding to the target based on the horizontal frequency difference, including: The first device determines the horizontal velocity corresponding to the target based on the wavelength corresponding to the sensing signal, the trigonometric function value corresponding to the spatial azimuth angle, and the horizontal frequency difference; The first device determines the vertical velocity corresponding to the target based on the vertical frequency difference, including: The first device determines the vertical velocity corresponding to the target based on the wavelength, the trigonometric function value corresponding to the pitch angle, and the vertical frequency difference.

5. The method according to claim 1, characterized in that, The frequency offset also includes a fixed frequency shift, and the method further includes: The first device determines a fixed frequency shift based on a vector set, a fifth coefficient in a coefficient set, and a third parameter set. The third parameter set includes the number of time slots K occupied by the sensing signal and the sampling interval T0. The first device determines the radial velocity corresponding to the target based on the wavelength corresponding to the sensing signal and the fixed frequency shift.

6. The method according to claim 1, characterized in that, The estimation result also includes a signal-to-noise ratio estimate, and the method further includes: The first device determines the size of the sensing window corresponding to the sensing signal based on the signal-to-noise ratio estimate.

7. The method according to claim 6, characterized in that, The first device determines the number of time slots corresponding to the sensed signal based on the signal-to-noise ratio estimate, including: If the signal-to-noise ratio estimate is greater than or equal to the signal-to-noise ratio threshold, the first device determines the sensing window corresponding to the sensing signal as the first sensing window. If the signal-to-noise ratio estimate is less than the signal-to-noise ratio threshold, the first device determines the sensing window corresponding to the sensing signal as the second sensing window, and the number of time slots corresponding to the second sensing window is greater than the number of time slots corresponding to the first sensing window.

8. The method according to claim 7, characterized in that, The number of time slots corresponding to the first sensing window is determined based on the sensing interval T, the sampling interval T0, and the signal-to-noise ratio estimate; The number of time slots corresponding to the second sensing window is determined based on the number of time slots K corresponding to the sensing signal sent in the current sensing cycle, the sensing interval T, the sampling interval T0, and the signal-to-noise ratio estimate.

9. The method according to claim 7 or 8, characterized in that, The signal-to-noise ratio threshold is based on the sensing interval T, the sampling interval T0, and the number of time slots K corresponding to the sensing signal sent in the current sensing cycle.

10. The method according to claim 1, characterized in that, Before the first device sends the sensing signal, the method further includes: The first device acquires the initial configuration information corresponding to the first device and the initial state information corresponding to the target; The first device adjusts the steering matrix corresponding to the two-dimensional array antenna according to the initial configuration information and the initial state information.

11. The method according to claim 10, characterized in that, The initial configuration information includes one or more of the following: the wavelength of the sensing signal, the sensing interval T, the time slot length Ts, the sampling interval T0, the number of time slots occupied by the sensing signal K, the number of samples per time slot Q, the horizontal array element interval dm, the vertical array element interval dn, the number of horizontal array elements M, the number of vertical array elements N, and the pointing angle of the two-dimensional array antenna. The initial state information includes one or more of the following: initial position information and initial velocity information.

12. A communication device, characterized in that, The device includes at least one processor coupled to a memory storing a program or instructions, the processor executing the program or instructions to cause the device to perform the method as described in any one of claims 1 to 11.

13. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they cause the computer to perform the method as described in any one of claims 1 to 11.

14. A communication system, characterized in that, Includes the communication device as described in claim 12.

15. A chip system, characterized in that, The chip system includes one or more processors, which are configured to retrieve and execute instructions stored in memory, such that the method as described in any one of claims 1 to 11 is performed.

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