A Joint Beamforming Method Based on Space-Ground Cooperative Communication and Multi-Target Sensing

Abstract

The present invention provides a joint beamforming method based on satellite-ground collaborative communication and multi-target perception, which relates to the technical field of integrated communication and sensing. This method is applied to a satellite-ground collaborative communication and sensing integrated system with multiple satellites, multiple ground stations, and one satellite application center. All satellites and all ground stations are equipped with ISAC devices. After obtaining the status data of the ISAC devices, with the goal of maximizing the cumulative reward, a distributed reinforcement learning model is used to process the status data of all ISAC devices, and the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device are obtained. The reward after all agents in the model execute actions is positively correlated with the total throughput of the system and negatively correlated with the Cramer-Rao bound of all ISAC devices for sensing all UAVs. Therefore, it can ensure the timeliness and accuracy of UAV positioning and provide highly reliable services for UAV target perception and data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated sensing and communication, and in particular to a joint beamforming method based on satellite-ground collaborative communication and multi-target perception. Background Art

[0002] In recent years, with the development of unmanned aerial vehicle (UAV) technology, UAVs have been increasingly widely used in multiple fields such as commerce and security. However, in sensitive areas such as airports, illegal intrusion by unauthorized aircraft poses a serious security threat, which may cause infrastructure damage and other security hazards. Therefore, the demand for accurate and flexible perception of airborne intrusion aircraft is becoming more urgent. Moreover, in the upcoming era of high-rate and high-reliability communication, the shortage of spectrum resources is becoming more severe.

[0003] To address the growing demand for communication and sensing capabilities in diverse services, integrated sensing and communication (ISAC) technology has gradually received extensive attention and become a potential solution. However, in the complex airspace environment of multiple UAV targets, traditional single-sensing systems face significant limitations. On the one hand, ground stations are limited by terrain and line of sight, making it difficult to comprehensively cover wide airspaces, with limited sensing ranges and blind spots in the field of vision. On the other hand, although low Earth orbit (LEO) satellites have global coverage and continuous monitoring capabilities, it is difficult to achieve high-precision detection under low-resolution conditions. These limitations lead to sensing blind spots and response delays. In addition, the balanced optimization of communication and sensing performance is the focus of integrated sensing and communication beamforming design, which affects the overall performance of the system. Summary of the Invention

[0004] The purpose of the present invention is to provide a joint beamforming method based on satellite-ground collaborative communication and multi-target perception to ensure the timeliness, continuity, and accuracy of UAV positioning, and to provide high-reliability services for UAV target perception and data transmission.

[0005] In a first aspect, the present invention provides a joint beamforming method based on satellite-ground collaborative communication and multi-target perception, which is applied to a satellite-ground collaborative communication and perception integrated system. The satellite-ground collaborative communication and perception integrated system includes: multiple satellites, multiple ground stations, and one satellite application center. The method includes: obtaining the state data of each integrated sensing and communication (ISAC) device in the satellite-ground collaborative communication and perception integrated system; wherein, all satellites and all ground stations in the satellite-ground collaborative communication and perception integrated system are equipped with ISAC devices; the state data includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects; aiming at maximizing the cumulative reward, using a distributed reinforcement learning model to process the state data of all ISAC devices to obtain the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device; wherein, the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices, the state of the agent is the state data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, and the reward after all agents execute actions is positively correlated with the total throughput of the satellite-ground collaborative communication and perception integrated system and negatively correlated with the Cramer-Rao lower bound of the perception of all drones by all ISAC devices; based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, determine the joint beamforming strategy of the satellite-ground collaborative communication and perception integrated system to perform high-precision positioning on the drones within the system coverage range.

[0006] Optionally, high-precision positioning of UAVs within the system coverage is performed based on a joint beamforming strategy, including: Step 201, determining the transmission signals of each ISAC device based on the joint beamforming strategy, and obtaining the position coordinates of each ISAC device and the echo signals received by it; Step 202, determining the DOA parameters of the target UAV closest to it within its sensing range based on the echo signal of the target ISAC device; and, determining the signal propagation delay between the target ISAC device and the target UAV based on the echo signal and transmission signal of the target ISAC device; wherein, the target ISAC device represents any integrated sensing and communication ISAC device in the space-ground collaborative communication and sensing integrated system; the DOA parameters of the target UAV include: the azimuth angle and elevation angle of the target UAV relative to the antenna array of the target ISAC device; Step 203, performing three-dimensional positioning on the target UAV based on the position coordinates of the target ISAC device, the signal propagation delay between the target ISAC device and the target UAV, and the DOA parameters of the target UAV, to obtain the position coordinates of the target UAV sensed by the target ISAC device; Step 204, suppressing the echo signal of the target UAV, and returning to execute Step 202 until each ISAC device determines the position coordinates of each UAV within its sensing range; Step 205, determining the high-precision positioning result of each UAV based on the position coordinates of each UAV sensed by all ISAC devices and the preset sensing accuracy coefficient corresponding to each ISAC device.

[0007] Optionally, determining the DOA parameters of the target UAV closest to it within its sensing range based on the echo signal of the target ISAC device includes: constructing the covariance matrix of the echo signal, performing eigenvalue decomposition on the covariance matrix to obtain the eigenvector matrix and eigenvalues of the target subspace; wherein, the target subspace includes: signal subspace, interference subspace, and noise subspace; determining the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace; within the preset angle search range, calculating the MUSIC spectrum based on the eigenvector matrix of the noise subspace and the receiving antenna steering vector function; determining the position corresponding to the peak value of the MUSIC spectrum within the preset angle search range as the DOA parameters of the target UAV.

[0008] Optionally, based on the echo signal and the transmitted signal of the target ISAC device, determining the signal propagation delay between the target ISAC device and the target UAV includes: performing matched filtering on the transmitted signal and the echo signal to obtain a matched filtering result; determining a multi-channel generalized cross-correlation function based on the matched filtering result and the transmitted signal; determining a noise detection threshold of the multi-channel generalized cross-correlation function, and performing peak detection on the multi-channel generalized cross-correlation function within a preset peak search range to obtain an initial peak position, where the initial peak position represents the sampling delay index of the preliminary delay estimation of the target UAV; optimizing the initial peak position by using a weighted quadratic fitting method to obtain a target peak position; and calculating the signal propagation delay between the target ISAC device and the target UAV based on the sampling rate of the transmitted signal and the target peak position.

[0009] Optionally, it further includes: constructing a joint steering vector based on the signal propagation delay between the target ISAC device and the target UAV and the DOA parameter of the target UAV; where the joint steering vector includes a spatial phase response and a delay effect; constructing a Fisher information matrix based on the joint steering vector; and determining the Cramer-Rao bound of the target ISAC device's perception of the target UAV based on the Fisher information matrix.

[0010] Optionally, if the ISAC device is configured on a satellite, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the satellite and each ground station matched with it, and the channel matrix between the satellite and each UAV within its coverage; if the ISAC device is configured on a ground station, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the ground station and the satellite application center, and the channel matrix between the ground station and each UAV within its coverage.

[0011] Optionally, it further includes: obtaining the channel matrix between each satellite and each ground station it matches, the channel matrix between each satellite and each unmanned aerial vehicle within its coverage, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each satellite, to calculate the first signal-to-interference-plus-noise ratio (SINR) of the downlink between the target satellite and the target ground station; where the target satellite represents any satellite in the satellite-ground collaborative communication and sensing integrated system; the target ground station represents any ground station in the satellite-ground collaborative communication and sensing integrated system; calculating the throughput of the downlink communication link between the target satellite and the target ground station based on the first SINR and the authorized bandwidth occupied by the transmitted signal of the target satellite; obtaining the channel matrix between each ground station and the satellite application center, the channel matrix between each ground station and each unmanned aerial vehicle within its coverage, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each ground station, to calculate the second SINR of the downlink between the target ground station and the satellite application center; calculating the throughput of the downlink communication link between the target ground station and the satellite application center based on the second SINR and the authorized bandwidth occupied by the transmitted signal of the target ground station; determining the total throughput of the satellite-ground collaborative communication and sensing integrated system based on the throughput of the downlink communication links between all satellites and each ground station they match and the throughput of the downlink communication links between all ground stations and the satellite application center.

[0012] In a second aspect, the present invention provides a joint beamforming device based on satellite-ground collaborative communication and multi-target perception, which is applied to a satellite-ground collaborative communication and sensing integrated system. The satellite-ground collaborative communication and sensing integrated system includes: multiple satellites, multiple ground stations, and one satellite application center, and includes: an acquisition module, configured to acquire the state data of each integrated sensing and communication (ISAC) device in the satellite-ground collaborative communication and sensing integrated system; wherein, all satellites and all ground stations in the satellite-ground collaborative communication and sensing integrated system are equipped with ISAC devices; the state data includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects; a processing module, configured to process the state data of all ISAC devices by using a distributed reinforcement learning model with the goal of maximizing the cumulative reward, so as to obtain a target communication beamforming matrix and a target sensing beamforming matrix for each ISAC device; wherein, the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices, the state of the agent is the state data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, and the reward after all agents execute actions is positively correlated with the total throughput of the satellite-ground collaborative communication and sensing integrated system, and is negatively correlated with the Cramer-Rao lower bound of all ISAC devices' perception of all unmanned aerial vehicles; a determination module, configured to determine a joint beamforming strategy for the satellite-ground collaborative communication and sensing integrated system based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, so as to perform high-precision positioning on the unmanned aerial vehicles within the coverage range of the system based on the joint beamforming strategy.

[0013] In a third aspect, the present invention provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored on the memory. When the processor executes the computer program, it implements the joint beamforming method based on satellite-ground collaborative communication and multi-target perception according to any one of the foregoing embodiments.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions. When the computer instructions are executed by a processor, they implement the joint beamforming method based on satellite-ground collaborative communication and multi-target perception according to any one of the foregoing embodiments.

[0015] The present invention provides a joint beamforming method based on satellite-ground collaborative communication and multi-target perception, which is applied to a satellite-ground collaborative communication and perception integrated system with multiple satellites, multiple ground stations, and one satellite application center. In this system, all satellites and all ground stations are equipped with ISAC devices. After obtaining the status data of the ISAC devices, with the goal of maximizing the cumulative reward, a distributed reinforcement learning model is used to process the status data of all ISAC devices to obtain the target communication beamforming matrix and the target perception beamforming matrix of each ISAC device. Among them, the reward after all agents in the model execute actions is positively correlated with the total throughput of the system and negatively correlated with the Cramer-Rao lower bound of all ISAC devices' perception of all UAVs. Therefore, the present invention can ensure the timeliness, continuity, and accuracy of UAV positioning based on the collaborative perception of low-altitude high-precision perception of ground stations and high-altitude large-range observation of satellites, and provide highly reliable services for UAV target perception and data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is an architecture diagram of a satellite-ground collaborative communication and perception integrated system provided by an embodiment of the present invention;

[0018] Figure 2 It is a flowchart of a joint beamforming method based on satellite-ground collaborative communication and multi-target perception provided by an embodiment of the present invention;

[0019] Figure 3 It is a schematic diagram showing the relationship between the DOA estimation accuracy MSE and the device transmission power provided by an embodiment of the present invention;

[0020] Figure 4 It is a functional module diagram of a joint beamforming device based on satellite-ground collaborative communication and multi-target perception provided by an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a variety of different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0024] The following will describe in detail some embodiments of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0025] Embodiment 1

[0026] In the existing wireless communication systems, the research on communication-sensing integration technology is still in its infancy. Especially in the scenario of satellite-ground heterogeneous networks, it requires a large amount of power consumption and hardware implementation. How to control the main lobe of the array pattern to align with the desired direction to achieve adaptive beamforming while effectively suppressing interference; how to design the satellite-ground collaborative communication-sensing integration network architecture; how to achieve the trade-off between the data transmission and sensing requirements of each device, etc., are all key issues to be solved in the development of satellite-ground collaborative communication-sensing integration technology.

[0027] The objective of the embodiments of the present invention is to propose a satellite-ground collaborative communication and sensing integration architecture, so as to provide a high-speed transmission and high-precision sensing method with full-domain coverage and spectrum sharing; and to propose a communication-sensing integration beamforming method suitable for this architecture, so as to improve the target sensing accuracy of unmanned aerial vehicles while meeting communication services.

[0028] Specifically, the embodiments of the present invention provide a joint beamforming method based on satellite-ground collaborative communication and multi-target sensing. This method is applied to a satellite-ground collaborative communication and sensing integration system, such as Figure 1As shown in the figure, the space-ground collaborative communication and sensing integrated system includes: multiple satellites, multiple ground stations, and one satellite application center. Among them, the satellite layer composed of multiple satellites supports joint communication and sensing, can cover a wide airspace, provides preliminary detection with low resolution and communication relay, supports regional sensing enhancement, and generates preliminary target direction and distance estimation; at the same time, it supports high-speed communication with ground stations; the ground station layer composed of multiple ground stations also supports joint communication and sensing, provides high-resolution precise sensing of close-range UAV targets, obtains accurate DOA parameters (azimuth and elevation angles) and time delay estimation; at the same time, it serves as the communication terminal of the satellite; the collaborative data fusion layer is deployed in the satellite application center, which improves the overall sensing accuracy and communication reliability through multi-source data (from satellites and ground stations) fusion, and performs global scheduling and optimization.

[0029] Figure 2 The flowchart of a joint beamforming method based on space-ground collaborative communication and multi-target sensing provided by an embodiment of the present invention is as follows Figure 2 As shown in the figure, the method includes the following steps:

[0030] Step 102, obtain the status data of each integrated sensing and communication (ISAC) device in the space-ground collaborative communication and sensing integrated system.

[0031] Among them, all satellites and all ground stations in the space-ground collaborative communication and sensing integrated system are equipped with ISAC devices; they are respectively represented as , , represents the number of satellites, represents the number of ground stations. Therefore, the set of ISAC devices can be represented as , . The communication and sensing functions of the ISAC device share spectrum resources and the transmitting antenna array. Each satellite has transmitting antennas and receiving antennas. Each ground station has transmitting antennas and receiving antennas. In the embodiment of the present invention, it is default that the transmitting antenna arrays and receiving antenna arrays of all satellites are the same, and the transmitting antenna arrays and receiving antenna arrays of all ground stations are also the same.

[0032] The set of aerial UAV targets co-sensed by satellites and ground stations is represented as , and the total number of UAV targets It is unknown before the UAV target perception and is specifically determined by the satellite application center: In the UAV perception stage, each satellite and ground station send their respective perception results (i.e., the position coordinates of the UAV targets within their coverage areas) to the satellite application center. The satellite application center takes the union of all the perception results as the set of airborne UAV targets and then determines the total number of airborne UAV targets. In the embodiments of the present invention, it is assumed that the distances between individual UAV targets are far enough apart. Therefore, the satellite application center regards multiple UAVs within a certain small spatial distribution range as the same UAV.

[0033] The status data of the ISAC device includes: position coordinates, transmitted signal, received echo signal, and channel matrix for communicating with external objects.

[0034] Specifically, if the ISAC device is configured on a satellite, the external objects communicating with the ISAC device include: each ground station (communication object) matching the satellite, and each UAV (perception object) within the satellite coverage area. Considering that the number of ground station devices within the coverage area of each satellite is limited, and the ground station devices have disabled time periods and can only communicate with one satellite in a single time period, appropriate ground station devices should be matched to each satellite according to constraints such as satellite orbit prediction, ground station disabled time, and limited ground station resources. The embodiments of the present invention do not specifically limit the method of matching ground stations for each satellite, and users can choose any method in the prior art according to the actual situation.

[0035] Therefore, if the ISAC device is configured on a satellite, the transmitted signal (integrated signal for data transmission and perception target) of the ISAC device m (i.e., satellite m) , is expressed as: , where represents the communication beamforming matrix of the ISAC device m, represents the communication beamforming vector designed for the ISAC device m to transmit communication signals to the ground ISAC device n (i.e., ground station n). By controlling the beam direction, shape, and intensity distribution, the communication quality between the two can be adjusted; represents the communication data symbol matrix of the ISAC device m, represents the communication symbol vector transmitted by the ISAC device m to the ground ISAC device n; represents the perception beamforming matrix of the ISAC device m, represents the perception beamforming vector designed for the ISAC device m to perceive the UAV target k, which can control the directivity and intensity of the signal and determines the detection ability of the ISAC device m for the UAV target k; represents the perception signal vector of the ISAC device m, Denote the sensing symbol vector sent by the ISAC device m to the UAV target k for target sensing.

[0036] If the transmitting and receiving arrays of the ISAC device m are respectively 、 uniform planar arrays (UPAs, uniform planar array), and both are half-wavelength antenna spacings, that is . Considering that the signals received by the radar receiver of the ISAC device include the echo signals reflected from the UAV target, the interference signals of non-desired clutter, as well as self-interference and noise, therefore, the echo signal model received by the ISAC device m is expressed as: ; where represents the reflection coefficient of the UAV k, represents the reflection coefficient of the clutter j. The above two reflection coefficients mainly depend on the radar cross-section, transmission distance, and carrier frequency; represents the angle of the UAV k relative to the horizontal direction of the ISAC device m, that is, the azimuth angle; represents the angle of the UAV k relative to the vertical direction of the ISAC device m, that is, the elevation angle. Similarly, represents the azimuth angle and elevation angle of the clutter j relative to the ISAC device m. represents the receiving antenna steering vector in the direction, H represents the conjugate transpose, represents the transmitting antenna steering vector in the represents the noise plus self-interference signal with variance (known parameter), , .

[0037] If the ISAC device is configured on a ground station, the external objects communicating with the ISAC device include: the satellite application center (communication object), and each UAV within the coverage of the ground station (sensing object). Referring to the construction principle of the transmitted signal and the received echo signal of the ISAC device m configured on the satellite in the above text, the transmitted signal and the received echo signal of the ISAC device n (that is, the ground station n) can be determined.

[0038] In the embodiments of the present invention, if the ISAC device is configured on a satellite, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the satellite and each ground station that matches it, the channel matrix between the satellite and each UAV within its coverage; if the ISAC device is configured on a ground station, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the ground station and the satellite application center, the channel matrix between the ground station and each UAV within its coverage.

[0039] From the introduction of the satellite-ground collaborative communication and sensing integrated system in the above text, it can be seen that the ground station not only serves as an ISAC device to sense the UAV target, but also serves as a communication user of the satellite. That is, the satellite needs to transmit communication data to the satellite application center through the ground station. Therefore, the received signal at ground station n can be expressed as: ; where represents the Gaussian white noise with variance (known parameter) at ground station n, represents the channel matrix between satellite m and ground station n. Based on the free space model and considering the Doppler effect, , represents the path gain, represents the transmit gain of satellite m, represents the receive gain of ground station n, represents the propagation distance between satellite m and ground station n at time t, represents the wavelength of the transmitted signal, represents the carrier frequency of the transmitted signal, represents the propagation delay between satellite m and ground station n at time t, represents the speed of light. The position of the ground station remains unchanged, but the position of the satellite changes with time. Therefore, the position coordinates of the ISAC device should also be used as its key state data.

[0040] Step 104: With the goal of maximizing the cumulative reward, use the distributed reinforcement learning model to process the state data of all ISAC devices to obtain the target communication beamforming matrix and the target sensing beamforming matrix for each ISAC device.

[0041] To provide high-reliability services for UAV target sensing and data transmission, it is necessary to determine the optimal communication beamforming matrix and sensing beamforming matrix for each ISAC device in the system, that is, the target communication beamforming matrix and the target sensing beamforming matrix in the above text. Therefore, the embodiment of the present invention adopts a distributed reinforcement learning model, where the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices. The state of the agent is the state data of the corresponding ISAC device, the action of the agent is the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, and the reward after all agents execute actions is positively correlated with the total throughput of the satellite-ground collaborative communication and sensing integrated system and negatively correlated with the Cramér-Rao bound of all ISAC devices' sensing of all UAVs.

[0042] Based on the states, actions, and rewards of the agents in the distributed reinforcement learning model in the above text, after obtaining the state data of each ISAC device, the states of each agent can be determined, and then actions can be output and rewards can be calculated. Optionally, the expression of the reward function is: ; where and both represent weight parameters, represents the total throughput of the satellite-terrestrial integrated communication and sensing system, represents the Cramér-Rao lower bound for the ISAC device z to sense the UAV target k.

[0043] The embodiment of the present invention aims to maximize the cumulative reward, and continuously updates the policy parameters of each agent in the distributed reinforcement learning model until the policies of all agents converge to a stable and well-performing state, that is, the optimal solution is found: the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device.

[0044] Step 106, based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, determine the joint beamforming strategy of the satellite-terrestrial integrated communication and sensing system, so as to perform high-precision positioning on the UAVs within the coverage of the system based on the joint beamforming strategy.

[0045] The joint beamforming strategy of the satellite-terrestrial integrated communication and sensing system includes the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices. This joint beamforming strategy can achieve the trade-off between the data transmission and sensing requirements of each ISAC device. When the communication beam direction and sensing beam direction of each ISAC device are optimal, the ground station performs low-altitude high-precision sensing on the UAV, the satellite performs high-altitude low-precision sensing on the UAV, the satellite transmits the sensing data to the satellite application center through the ground station, and the ground station also transmits the sensing data to the satellite application center. The satellite application center performs satellite-terrestrial data fusion, so as to achieve high-precision positioning of the UAVs within the coverage of the system.

[0046] An embodiment of the present invention provides a joint beamforming method based on satellite-ground collaborative communication and multi-target perception, which is applied to a satellite-ground collaborative communication and perception integrated system with multiple satellites, multiple ground stations, and one satellite application center. All satellites and all ground stations in this system are equipped with ISAC devices. After obtaining the status data of the ISAC devices, aiming at maximizing the cumulative reward, a distributed reinforcement learning model is used to process the status data of all ISAC devices, and the target communication beamforming matrix and the target perception beamforming matrix of each ISAC device are obtained. Among them, the reward after all agents in the model execute actions is positively correlated with the total throughput of the system and negatively correlated with the Cramer-Rao lower bound of the perception of all UAVs by all ISAC devices. Therefore, the present invention can ensure the timeliness, continuity, and accuracy of UAV positioning based on the collaborative perception of high-precision low-altitude perception of ground stations and large-scale high-altitude observations of satellites, and provide highly reliable services for UAV target perception and data transmission.

[0047] In an alternative embodiment, in step 106 above, high-precision positioning of UAVs within the system coverage based on the joint beamforming strategy specifically includes the following steps:

[0048] Step 201, determine the transmitted signal of each ISAC device based on the joint beamforming strategy, and obtain the position coordinates of each ISAC device and the echo signal received by it.

[0049] Specifically, the joint beamforming strategy includes the target communication beamforming matrix and the target perception beamforming matrix of all ISAC devices. Substituting the expressions of the transmitted signal and the echo signal of the ISAC device, the transmitted signal and the received echo signal of each ISAC device can be obtained. The position coordinates of the ISAC device arranged on the ground station are fixed, while the position coordinates of the ISAC device arranged on the satellite are not fixed. Therefore, to perform high-precision positioning of UAVs, the position coordinates of the ISAC device should also be updated periodically.

[0050] Step 202, determine the DOA parameters of the target UAV closest to it within its perception range based on the echo signal of the target ISAC device; and, based on the echo signal and the transmitted signal of the target ISAC device, determine the signal propagation delay between the target ISAC device and the target UAV; where the target ISAC device represents any integrated sensing and communication ISAC device in the satellite-ground collaborative communication and perception integrated system; the DOA parameters of the target UAV include: the azimuth angle and the elevation angle of the target UAV relative to the antenna array of the target ISAC device.

[0051] In an embodiment of the present invention, based on the echo signal of the target ISAC device, the DOA parameter of the target UAV closest to it within its sensing range is determined, which specifically includes the following steps:

[0052] Step 202a, construct the covariance matrix of the echo signal, perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector matrix and eigenvalues of the target subspace; where the target subspace includes: signal subspace, interference subspace, and noise subspace.

[0053] Taking the ISAC device m set on the satellite as an example, the method for determining the DOA parameter of the target UAV will be introduced below. The method for the ISAC device set on the ground station to determine the DOA parameter of the target UAV within its sensing range is the same.

[0054] In the embodiment of the present invention, in order to estimate the DOA parameter of the UAV, first use the formula to construct the covariance matrix of the echo signal; where represents the echo signal received by the ISAC device m at time t, represents the covariance matrix of represents the power distribution of the echo signal reflected by the UAV target, which contains the DOA information of all UAV targets, represents the propagation matrix of the echo signal reflected by the UAV target, represents the transmitted signal the covariance matrix of represents the power distribution of the non-expected clutter interference signal, represents the propagation matrix of the clutter interference signal, which describes the propagation characteristics of the clutter, including the power and coherence of the clutter, represents the covariance matrix of the noise, represents the identity matrix.

[0055] The eigenvalue decomposition of the covariance matrix can be expressed as: , where respectively represent the eigenvector matrices of the signal subspace, interference subspace, and noise subspace, respectively represent the eigenvalues of the signal subspace, interference subspace, and noise subspace.

[0056] Step 202b, determine the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace.

[0057] Considering that there is usually a large eigenvalue difference between the echo signal reflected by the UAV target and the noise or clutter, tracking the change rate of adjacent eigenvalues, and the eigenvalues of the signal subspace can be intercepted according to the position with the largest eigenvalue change rate. The number of eigenvalues of the signal subspace is the number of UAVs within the sensing range of the ISAC device.

[0058] Step 202c, within the preset angle search range, calculate the MUSIC spectrum based on the eigenvector matrix of the noise subspace and the receiving antenna steering vector function.

[0059] Step 202d, determine the position corresponding to the peak of the MUSIC spectrum within the preset angle search range as the DOA parameter of the target UAV.

[0060] The signal subspace is spanned by the receiving antenna steering vectors , the noise subspace is orthogonal to the receiving antenna steering vector, and calculate the MUSIC spectrum within the preset angle search range of azimuth and elevation angles, as shown in the following formula: . Search for the peak of within the preset angle search range. The position corresponding to the peak of the MUSIC spectrum is the DOA parameter of the target UAV , which can be expressed as: .

[0061] To determine the accuracy of the above method for DOA estimation, if the actual DOA parameter of UAV k is known , and the DOA parameter of this UAV is estimated using the above method as , then the mean square error (MSE) of multiple UAV DOA parameters can be used as the DOA estimation accuracy index, and the formula is as follows: .

[0062] In the embodiments of the present invention, based on the echo signal and the transmitted signal of the target ISAC device, determine the signal propagation delay between the target ISAC device and the target UAV, which specifically includes the following steps:

[0063] Step 2021, perform matched filtering on the transmitted signal and the echo signal to obtain the matched filtering result.

[0064] Specifically, in the matched filtering, the conjugate flip of the transmitted signal is used as the matched filter, and convolved with the received echo signal to strengthen the amplitude of the UAV target signal and simultaneously suppress the influence of noise and interference, thereby providing more accurate signal information for subsequent delay estimation. The matched filtering for each transmit-receive channel is expressed as: , where represents the j-th column of The \(i\)-th column of , represents the transmitted signal after conjugate flipping; represents the convolution operation, and represents the matched filtering result.

[0065] Step 2022: Based on the matched filtering result and the transmitted signal, determine the multi-channel generalized cross-correlation function.

[0066] To accurately estimate the propagation delay between the echo signal and the transmitted signal, the embodiments of the present invention use generalized cross-correlation (GCC-PHAT) to process the signals, thereby enhancing the matching robustness between the signals. The multi-channel generalized cross-correlation function reveals the similarity of the signals at different delays by calculating the normalized cross-correlation for each receiving channel, and is then used for delay estimation. The mathematical expression of the generalized cross-correlation matrix between the echo signal after matched filtering (i.e., the matched filtering result) and the transmitted signal (i.e., the multi-channel generalized cross-correlation function) is as follows: ; The \(j\)-th column of , represents the complex conjugate of The \(i\)-th column of , represents the inverse Fourier transform, and represents the numerically stable term.

[0067] Step 2023: Determine the noise detection threshold of the multi-channel generalized cross-correlation function, and perform peak detection on the multi-channel generalized cross-correlation function within a preset peak search range to obtain the initial peak position, where the initial peak position represents the sampling delay index of the preliminary delay estimation of the target UAV.

[0068] Specifically, to further suppress noise, the embodiments of the present invention use robust statistics to construct a dynamic detection threshold: , where , represents the noise floor estimation, represents the dynamic detection threshold, that is, the above-mentioned noise detection threshold; , and respectively represent the median, median absolute deviation, and standard deviation of

[0069] Assume that the delays corresponding to each UAV target are sufficiently separated, use the multi-channel method to decouple the reflected signals of each UAV target, and search for the peak position of the generalized cross-correlation function within the preset peak search range , the sampling delay index of the preliminary delay estimation of the target UAV k is: ; represents the preset peak search range, represents the sampling points within the search range.

[0070] Step 2024, optimize the initial peak position by using the weighted quadratic fitting method to obtain the target peak position.

[0071] In the above delay estimation, the initially detected sampling delay index may be restricted by noise and sampling intervals. Therefore, the weighted quadratic fitting method is used to optimize it to obtain a more accurate delay estimation. Specifically, for the sampling delay index of the preliminary delay estimation, set the fitting window , represents the window size. Select the within the window range and fit the quadratic polynomial , where a, b, and c all represent fitting parameters. To enhance the fitting robustness, introduce the weight matrix , and the weight is based on the sampling point to the sampling delay index of the preliminary delay estimation is defined as: .

[0072] Next, construct the weighted least squares equation: , , , and the least squares solution is . The extreme point (peak) of the quadratic curve is the sampling delay index (that is, the target peak position in the above text) , which can be obtained by solving where the second derivative is zero .

[0073] Step 2025, based on the sampling rate of the transmitted signal and the target peak position, calculate the signal propagation delay between the target ISAC device and the target UAV.

[0074] In the embodiment of the present invention, the signal propagation delay between the target ISAC device m and the target UAV k is calculated by the following formula: , where represents the sampling rate of the transmitted signal.

[0075] Step 203, based on the position coordinates of the target ISAC device, the signal propagation delay between the target ISAC device and the target UAV, and the DOA parameters of the target UAV, perform three-dimensional positioning on the target UAV to obtain the position coordinates of the target UAV sensed by the target ISAC device.

[0076] Specifically, based on the DOA parameters and time delay estimation results of the target UAV, three-dimensional positioning of the target UAV is performed using geometric relationships: ; where represents the position coordinates of the target ISAC device, respectively represent the latitude, longitude, and altitude of the satellite to which the target ISAC device belongs, represents the unit direction vector, represents the speed of light.

[0077] Step 204, suppress the echo signal of the target UAV, and return to execute Step 202 until each ISAC device determines the position coordinates of each UAV within its sensing range.

[0078] When there are multiple UAV targets in the sensing range of the ISAC device, after identifying the first UAV target, it is necessary to suppress the adjacent peaks in the MUSIC spectrum to avoid repeated detection until the position coordinates of each UAV within its sensing range are determined.

[0079] The process of the ISAC device deployed on the satellite sensing the UAV target is described in detail above. In the embodiments of the present invention, the communication and sensing fusion mechanism of the satellite can be extended to the ground station subsystem. While providing low-resolution sensing of the airspace UAV target at the satellite layer, joint communication and close-range target sensing are carried out at the ground station layer. The DOA and time delay estimation algorithms are similar to those of the satellite, realizing precise sensing of the UAV target and transmitting the sensing data from the satellite to the satellite application center. The positioning of the UAV target k by the ISAC device n deployed on the ground station can be expressed as: , where represents the position coordinates of the ground station n (i.e., the ISAC device n), respectively represent the latitude, longitude, and altitude of the ground station to which the ISAC device n belongs, represents the signal propagation time delay between the ISAC device n and the UAV target k, represents the DOA parameter estimation result of the ISAC device n for the UAV target k.

[0080] In summary, the positioning estimation of any ISAC device z in the satellite-ground collaborative communication and sensing integrated system for the UAV target k can be uniformly expressed as: .

[0081] Step 205, based on the position coordinates of each UAV sensed by all ISAC devices and the preset sensing accuracy coefficient corresponding to each ISAC device, determine the high-precision positioning result of each UAV.

[0082] Specifically, by fusing the sensing structures from the satellite layer and the ground station layer, if the weighted squared error of all devices is minimized, the objective function is: ; where represents the preset sensing accuracy coefficient of the ISAC device, which is proportional to the sensing accuracy of the ISAC device, represents the true position coordinates of the UAV target k.

[0083] Using the weighted least squares method to solve the above objective function, the high-precision positioning result of the UAV target k is obtained: .

[0084] In an alternative embodiment, the embodiment of the present invention further includes the step of calculating the Cramer-Rao bound for the target ISAC device to sense the target UAV:

[0085] Step 301, based on the signal propagation delay between the target ISAC device and the target UAV and the DOA parameters of the target UAV, construct a joint steering vector; where the joint steering vector includes the spatial phase response and the time delay effect.

[0086] Specifically, in the satellite and ground station collaborative sensing scenario, the embodiment of the present invention uses the Cramer-Rao bound (CRB) to measure the accuracy of DOA and time delay estimation. Given that the signal propagation delay between the target ISAC device z and the target UAV k is , and the DOA parameter estimation result of the ISAC device z for the UAV target k is , then first construct the parameter vector , and then construct the joint steering vector: , the joint steering vector includes the spatial phase response and the time delay effect, where represents the receiving antenna steering vector in the direction of, represents the time delay phase shift, and the operation symbol represents matrix dot multiplication.

[0087] Step 302, construct the Fisher information matrix based on the joint steering vector.

[0088] If the i-th parameter in the parameter vector is represented as , and the j-th parameter is represented as , then the embodiment of the present invention defines the element in the i-th row and j-th column of the Fisher information matrix (FIM) as: , where represents the noise power perturbation. Based on this, the Fisher information matrix is expanded as: , where Sym represents the symmetric elements, that is, the matrix The elements in the lower triangular part are equal to their corresponding elements in the upper triangular part.

[0089] Step 303: Determine the Cramér-Rao bound for the target ISAC device to sense the target UAV based on the Fisher information matrix.

[0090] Parameter vector The Cramér-Rao bound for the i-th parameter in is the diagonal element of the inverse matrix of the Fisher information matrix , that is, . Based on this formula, the CRBs for azimuth angle, elevation angle, and time delay estimation can be expressed as: , , . Therefore, the combined Cramér-Rao bound for the target ISAC device z to sense the target UAV k is: . The CRB is used to characterize the minimum variance bound of the DOA and time delay estimation values. Referring to the above method, the Cramér-Rao bounds for all ISAC devices to sense all UAVs can be calculated.

[0091] In the embodiments of the present invention, if the ISAC device is configured on a satellite, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the satellite and each ground station that matches it, and the channel matrix between the satellite and each UAV within its coverage; if the ISAC device is configured on a ground station, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the ground station and the satellite application center, and the channel matrix between the ground station and each UAV within its coverage.

[0092] The embodiments of the present invention further include the following steps for calculating the total throughput of the satellite-ground collaborative communication and sensing integrated system:

[0093] Step 401: Obtain the channel matrix between each satellite and each ground station that matches it, the channel matrix between each satellite and each UAV within its coverage, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each satellite, so as to calculate the first signal-to-interference-plus-noise ratio of the downlink between the target satellite and the target ground station; where, the target satellite represents any satellite in the satellite-ground collaborative communication and sensing integrated system; the target ground station represents any ground station in the satellite-ground collaborative communication and sensing integrated system.

[0094] In the embodiments of the present invention, the first signal-to-interference-plus-noise ratio of the downlink between the target satellite m and the target ground station n is calculated through the following formula: . In the formula, represents the channel matrix between the satellite i and the ground station j that matches it, denotes the channel matrix between satellite i and UAV target k within its coverage area. The interference mainly includes co-frequency signal interference from other satellites, downlink receiving signal interference from other ground stations, and radar sensing waveform interference.

[0095] Step 402: Calculate the throughput of the downlink communication link between the target satellite and the target ground station based on the first signal-to-interference-plus-noise ratio (SINR) and the authorized bandwidth occupied by the transmitted signal of the target satellite.

[0096] The throughput can reflect the actual communication capacity of the system. The throughput of the downlink communication link between target satellite m and target ground station n can be expressed as: . Wherein, denotes the authorized bandwidth occupied by the transmitted signal of target satellite m.

[0097] Step 403: Obtain the channel matrix between each ground station and the satellite application center, the channel matrix between each ground station and each UAV within its coverage area, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each ground station, so as to calculate the second SINR of the downlink between the target ground station and the satellite application center.

[0098] In the embodiment of the present invention, the second SINR of the downlink between target ground station n and satellite application center center is calculated by the following formula: ; Wherein, denotes the channel matrix from target ground station n to satellite application center center, and respectively denote the number of transmitting antennas of the ground station and the number of receiving antennas of the satellite application center, denotes the channel matrix between ground station e and UAV target k within its coverage area, and respectively denote the target communication beamforming matrix and the target sensing beamforming matrix in the integrated communication and sensing transmitted signal of ground station e.

[0099] Step 404: Calculate the throughput of the downlink communication link between the target ground station and the satellite application center based on the second SINR and the authorized bandwidth occupied by the transmitted signal of the target ground station.

[0100] The throughput of the downlink communication link between target ground station n and the satellite application center is: , wherein, denotes the authorized bandwidth occupied by the transmitted signal of target ground station n.

[0101] Step 405: Determine the total throughput of the satellite-ground collaborative communication and sensing integrated system based on the throughput of the downlink communication links between all satellites and each of their matching ground stations and the throughput of the downlink communication links between all ground stations and the satellite application center. The formula for the total throughput is as follows: .

[0102] The objective of the embodiments of the present invention is to maximize the total throughput of the system while ensuring the multi-target sensing and satellite-ground collaborative sensing accuracy. Therefore, the objective function for jointly optimizing beamforming can be expressed as: . Among them, and respectively represent the weight parameters of the total throughput and sensing accuracy of the system; represents the minimum threshold of the signal-to-interference-plus-noise ratio (SINR) of the downlink of the ISAC device z and the communication device , represents the satellite application center; the symbol represents the Frobenius norm of the matrix. This constraint requires that the total power of the transmitted signal is limited by the hardware and cannot exceed the power upper limit; represents the minimum gain of the radar full-space scanning, represents the set of angles in the full space. This constraint ensures that the sensing signal beam can cover all spatial directions; represents the lower limit of beam uniformity, requiring that the beam gain difference in all spatial directions does not exceed the ratio , that is, the gain of the weakest direction is not less than times the gain of the strongest direction, to avoid insufficient sensing in some directions.

[0103] To verify the performance of the embodiments of the present invention, assume that the satellite-ground collaborative communication and sensing integrated system includes 1 satellite and 2 ground stations, and both are equipped with communication and sensing integrated transmitters and radar receivers, , , , , the starting positions of the 2 unmanned aerial vehicles are (51.500°N, 26.000°E, 10000m) and (63.100°N, 19.00°E, 15000m) respectively, and the final arrival positions are (59.90°N, 23.000°E, 12000m) and (45.200°N, 29.00°E, 27000m) respectively. They all fly at a constant speed of 200 m / s, and the system noise power is set to -110 dBm. Figure 3 It is a schematic diagram of the relationship between the DOA estimation accuracy MSE and the device transmit power after positioning using the method provided by the embodiments of the present invention. As can be seen from Figure 3 , as the transmit power increases, the DOA estimation accuracy MSE gradually decreases.

[0104] In summary, the embodiment of the present invention provides a space-ground collaborative communication and sensing integrated architecture, which makes full use of existing space-air-ground integrated communication devices and communication and sensing integrated technologies. Aiming at jointly optimizing throughput and sensing accuracy, it combines DOA, time delay estimation and beamforming optimization to achieve UAV target positioning and sensing. Through space-ground collaborative sensing, multi-source data is fused, the UAV target positioning accuracy is improved, the fairness of communication and sensing is ensured, and the collaborative and efficient sensing and communication optimization between satellites and ground stations in complex multi-target scenarios are realized.

[0105] Embodiment 2

[0106] The embodiment of the present invention also provides a joint beamforming device based on space-ground collaborative communication and multi-target sensing. This device is applied to a space-ground collaborative communication and sensing integrated system, which includes: multiple satellites, multiple ground stations and one satellite application center. This device is mainly used to execute the joint beamforming method based on space-ground collaborative communication and multi-target sensing provided in the above Embodiment 1. The following is a specific introduction to the joint beamforming device based on space-ground collaborative communication and multi-target sensing provided in the embodiment of the present invention.

[0107] Figure 4 It is a functional module diagram of a joint beamforming device based on space-ground collaborative communication and multi-target sensing provided in the embodiment of the present invention. As Figure 4 shown, this device mainly includes: an acquisition module 10, a processing module 20, and a determination module 30, where:

[0108] The acquisition module 10 is used to acquire the status data of each integrated sensing and communication (ISAC) device in the space-ground collaborative communication and sensing integrated system; among them, all satellites and all ground stations in the space-ground collaborative communication and sensing integrated system are equipped with ISAC devices; the status data includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects.

[0109] The processing module 20 is used to process the status data of all ISAC devices with the goal of maximizing the cumulative reward by using a distributed reinforcement learning model, and obtain the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device; among them, the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices, the state of the agent is the status data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, and the reward after all agents execute actions is positively correlated with the total throughput of the space-ground collaborative communication and sensing integrated system, and negatively correlated with the Cramer-Rao bound of all ISAC devices for sensing all UAVs.

[0110] A determination module 30, configured to determine a joint beamforming strategy for the space-ground collaborative communication and sensing integrated system based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, so as to perform high-precision positioning on the unmanned aerial vehicles within the coverage range of the system based on the joint beamforming strategy.

[0111] An embodiment of the present invention provides a joint beamforming device based on space-ground collaborative communication and multi-target sensing, which is applied to a space-ground collaborative communication and sensing integrated system having multiple satellites, multiple ground stations, and one satellite application center. All satellites and all ground stations in the system are equipped with ISAC devices. After obtaining the status data of the ISAC devices, with the goal of maximizing the cumulative reward, a distributed reinforcement learning model is used to process the status data of all ISAC devices to obtain the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device. Among them, the reward after all agents in the model execute actions is positively correlated with the total throughput of the system and negatively correlated with the Cramér-Rao lower bound of the sensing of all drones by all ISAC devices. Therefore, the embodiment of the present invention can ensure the timeliness, continuity, and accuracy of drone positioning based on the collaborative sensing of low-altitude high-precision sensing of ground stations and high-altitude large-range observation of satellites, and provides a highly reliable service for drone target sensing and data transmission.

[0112] Optionally, the determination module 30 includes:

[0113] A first determination unit, configured to determine the transmitted signal of each ISAC device based on the joint beamforming strategy, and obtain the position coordinates of each ISAC device and the echo signal received by it.

[0114] A second determination unit, configured to determine the DOA parameters of the target unmanned aerial vehicle closest to it within its sensing range based on the echo signal of the target ISAC device; and, based on the echo signal and the transmitted signal of the target ISAC device, determine the signal propagation delay between the target ISAC device and the target unmanned aerial vehicle; where the target ISAC device represents any integrated sensing and communication ISAC device in the space-ground collaborative communication and sensing integrated system; the DOA parameters of the target unmanned aerial vehicle include: the azimuth angle and the elevation angle of the target unmanned aerial vehicle relative to the antenna array of the target ISAC device.

[0115] A positioning unit, configured to perform three-dimensional positioning on the target unmanned aerial vehicle based on the position coordinates of the target ISAC device, the signal propagation delay between the target ISAC device and the target unmanned aerial vehicle, and the DOA parameters of the target unmanned aerial vehicle, so as to obtain the position coordinates of the target unmanned aerial vehicle sensed by the target ISAC device.

[0116] A repeated execution unit, configured to suppress the echo signal of the target UAV, and return to call the second determination unit and the positioning unit until each ISAC device determines the position coordinates of each UAV within its sensing range.

[0117] A third determination unit, configured to determine the high-precision positioning result of each UAV based on the position coordinates of each UAV sensed by all ISAC devices and the preset sensing accuracy coefficient corresponding to each ISAC device.

[0118] Optionally, the second determination unit is specifically configured to:

[0119] Construct a covariance matrix of the echo signal, perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector matrix and eigenvalues of the target subspace; wherein, the target subspace includes: a signal subspace, an interference subspace, and a noise subspace.

[0120] Determine the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace.

[0121] Within a preset angle search range, calculate the MUSIC spectrum based on the eigenvector matrix of the noise subspace and the receiving antenna steering vector function.

[0122] Determine the position corresponding to the peak value of the MUSIC spectrum within the preset angle search range as the DOA parameter of the target UAV.

[0123] Optionally, the second determination unit is further configured to:

[0124] Perform matched filtering on the transmitted signal and the echo signal to obtain a matched filtering result.

[0125] Determine the multi-channel generalized cross-correlation function based on the matched filtering result and the transmitted signal.

[0126] Determine the noise detection threshold of the multi-channel generalized cross-correlation function, and perform peak detection on the multi-channel generalized cross-correlation function within a preset peak search range to obtain an initial peak position, where the initial peak position represents the sampling delay index of the preliminary delay estimation of the target UAV.

[0127] Optimize the initial peak position by using a weighted quadratic fitting method to obtain a target peak position.

[0128] Calculate the signal propagation delay between the target ISAC device and the target UAV based on the sampling rate of the transmitted signal and the target peak position.

[0129] Optionally, the apparatus is further configured to:

[0130] Construct a joint steering vector based on the signal propagation delay between the target ISAC device and the target UAV and the DOA parameters of the target UAV; wherein, the joint steering vector includes the spatial phase response and the delay effect.

[0131] Construct a Fisher information matrix based on the joint steering vector.

[0132] Determine the Cramer-Rao bound for the target ISAC device to sense the target UAV based on the Fisher information matrix.

[0133] Optionally, if the ISAC device is configured on a satellite, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the satellite and each of its matching ground stations, and the channel matrix between the satellite and each UAV within its coverage area.

[0134] If the ISAC device is configured on a ground station, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the ground station and the satellite application center, and the channel matrix between the ground station and each UAV within its coverage area.

[0135] Optionally, the apparatus is further configured to:

[0136] Obtain the channel matrix between each satellite and each of its matching ground stations, the channel matrix between each satellite and each UAV within its coverage area, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each satellite, so as to calculate the first signal-to-interference-plus-noise ratio (SINR) of the downlink between the target satellite and the target ground station; wherein, the target satellite represents any satellite in the satellite-ground collaborative communication and sensing integrated system; the target ground station represents any ground station in the satellite-ground collaborative communication and sensing integrated system.

[0137] Calculate the throughput of the downlink communication link between the target satellite and the target ground station based on the first SINR and the authorized bandwidth occupied by the transmitted signal of the target satellite.

[0138] Obtain the channel matrix between each ground station and the satellite application center, the channel matrix between each ground station and each UAV within its coverage area, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each ground station, so as to calculate the second SINR of the downlink between the target ground station and the satellite application center.

[0139] Calculate the throughput of the downlink communication link between the target ground station and the satellite application center based on the second SINR and the authorized bandwidth occupied by the transmitted signal of the target ground station.

[0140] Determine the total throughput of the satellite-ground collaborative communication and sensing integrated system based on the throughput of the downlink communication link between all satellites and each ground station they are matched with, and the throughput of the downlink communication link between all ground stations and the satellite application center.

[0141] Embodiment III

[0142] Refer to Figure 5 , an embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected through the bus 62; the processor 60 is configured to execute an executable module stored in the memory 61, such as a computer program.

[0143] Among them, the memory 61 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0144] The bus 62 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a bidirectional arrow is used in

[0145] to represent, but it does not mean that there is only one bus or one type of bus. Among them, the memory 61 is used to store a program. After receiving an execution instruction, the processor 60 executes the program, and the method executed by the device defined by the process disclosed in any embodiment of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0146] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 60 or the instructions in the form of software. The above-mentioned processor 60 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and combines its hardware to complete the steps of the above method.

[0147] A computer program product of a joint beamforming method based on satellite-ground collaborative communication and multi-target perception provided by an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments and will not be elaborated here.

[0148] In addition, in each embodiment of the present invention, each functional unit may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0149] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0150] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0151] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0152] In addition, the terms "horizontal", "vertical", "hanging", etc. do not mean that the components are required to be absolutely horizontal or hanging, but can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0153] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect", "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A joint beamforming method based on satellite-ground collaborative communication and multi-target perception, characterized in that Applied to the space-ground collaborative communication and sensing integrated system, the space-ground collaborative communication and sensing integrated system includes: multiple satellites, multiple ground stations, and one satellite application center. The method includes: Obtain the state data of each integrated sensing and communication (ISAC) device in the space-ground collaborative communication and sensing integrated system; wherein, all satellites and all ground stations in the space-ground collaborative communication and sensing integrated system are equipped with ISAC devices; the state data includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects; With the goal of maximizing the cumulative reward, use a distributed reinforcement learning model to process the state data of all ISAC devices to obtain the target communication beamforming matrix and the target sensing beamforming matrix for each ISAC device; wherein, the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices, the state of the agent is the state data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, and the reward after all agents execute actions is positively correlated with the total throughput of the space-ground collaborative communication and sensing integrated system and negatively correlated with the Cramér-Rao bound for the sensing of all drones by all ISAC devices; Based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, determine the joint beamforming strategy of the space-ground collaborative communication and sensing integrated system to perform high-precision positioning on the drones within the system coverage based on the joint beamforming strategy; Among them, performing high-precision positioning on the drones within the system coverage based on the joint beamforming strategy includes: Step 201, determine the transmitted signal of each ISAC device based on the joint beamforming strategy, and obtain the position coordinates of each ISAC device and the echo signals received by it; Step 202, determine the DOA parameters of the target drone closest to it within its sensing range based on the echo signal of the target ISAC device; and, based on the echo signal and the transmitted signal of the target ISAC device, determine the signal propagation delay between the target ISAC device and the target drone; wherein, the target ISAC device represents any integrated sensing and communication (ISAC) device in the space-ground collaborative communication and sensing integrated system; the DOA parameters of the target drone include: the azimuth angle and the elevation angle of the target drone relative to the antenna array of the target ISAC device; Step 203, perform three-dimensional positioning on the target drone based on the position coordinates of the target ISAC device, the signal propagation delay between the target ISAC device and the target drone, and the DOA parameters of the target drone to obtain the position coordinates of the target drone sensed by the target ISAC device; Step 204, suppress the echo signal of the target drone, and return to execute Step 202 until each ISAC device determines the position coordinates of each drone within its sensing range; Step 205: Determine the high-precision positioning results of each UAV based on the position coordinates of each UAV sensed by all the ISAC devices and the preset sensing accuracy coefficients corresponding to each ISAC device.

2. The joint beamforming method based on satellite-ground collaborative communication and multi-target perception according to claim 1, wherein Determine the DOA parameters of the target UAV closest to the target ISAC device within its sensing range based on the echo signal of the target ISAC device, including: Construct the covariance matrix of the echo signal, and perform eigenvalue decomposition on the covariance matrix to obtain the eigenvector matrix and eigenvalues of the target subspace; wherein, the target subspace includes: signal subspace, interference subspace, and noise subspace; Determine the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace; Within the preset angle search range, calculate the MUSIC spectrum based on the eigenvector matrix of the noise subspace and the receiving antenna steering vector function; Determine the position corresponding to the peak of the MUSIC spectrum within the preset angle search range as the DOA parameters of the target UAV.

3. The joint beamforming method based on space-ground cooperative communication and multi-target perception according to claim 1, wherein Determine the signal propagation delay between the target ISAC device and the target UAV based on the echo signal and the transmitted signal of the target ISAC device, including: Perform matched filtering on the transmitted signal and the echo signal to obtain the matched filtering result; Determine the multi-channel generalized cross-correlation function based on the matched filtering result and the transmitted signal; Determine the noise detection threshold of the multi-channel generalized cross-correlation function, and perform peak detection on the multi-channel generalized cross-correlation function within the preset peak search range to obtain the initial peak position, and the initial peak position represents the sampling delay index of the preliminary delay estimation of the target UAV; Optimize the initial peak position by using the weighted quadratic fitting method to obtain the target peak position; Calculate the signal propagation delay between the target ISAC device and the target UAV based on the sampling rate of the transmitted signal and the target peak position.

4. The joint beamforming method based on satellite-ground cooperative communication and multi-target perception according to claim 1, wherein It further includes: Construct a joint steering vector based on the signal propagation delay between the target ISAC device and the target UAV and the DOA parameters of the target UAV; wherein, the joint steering vector includes spatial phase response and delay effect; Construct a Fisher information matrix based on the joint steering vector; Determine the Cramer-Rao bound of the target ISAC device's sensing of the target UAV based on the Fisher information matrix.

5. The joint beamforming method based on satellite-ground cooperative communication and multi-target sensing according to claim 1, characterized in that If the ISAC device is configured on the satellite, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the satellite and each of its matching ground stations, and the channel matrix between the satellite and each UAV within its coverage; If the ISAC device is configured on the ground station, the channel matrix for the ISAC device to communicate with external objects includes: the channel matrix between the ground station and the satellite application center, and the channel matrix between the ground station and each UAV within its coverage.

6. The joint beamforming method based on satellite-ground cooperative communication and multi-target perception according to claim 5, characterized in that It further includes: Obtain the channel matrix between each satellite and each ground station it matches, the channel matrix between each satellite and each unmanned aerial vehicle within its coverage, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each satellite, to calculate the first signal-to-interference-plus-noise ratio (SINR) of the downlink between the target satellite and the target ground station; wherein, the target satellite represents any satellite in the satellite-ground collaborative communication and sensing integrated system; the target ground station represents any ground station in the satellite-ground collaborative communication and sensing integrated system; Based on the first SINR and the authorized bandwidth occupied by the transmitted signal of the target satellite, calculate the throughput of the downlink communication link between the target satellite and the target ground station; Obtain the channel matrix between each ground station and the satellite application center, the channel matrix between each ground station and each unmanned aerial vehicle within its coverage, the communication beamforming matrix and the sensing beamforming matrix of the ISAC device on each ground station, to calculate the second SINR of the downlink between the target ground station and the satellite application center; Based on the second SINR and the authorized bandwidth occupied by the transmitted signal of the target ground station, calculate the throughput of the downlink communication link between the target ground station and the satellite application center; Based on the throughput of the downlink communication links between all satellites and each ground station they match and the throughput of the downlink communication links between all ground stations and the satellite application center, determine the total throughput of the satellite-ground collaborative communication and sensing integrated system.

7. A joint beamforming device based on satellite-ground collaborative communication and multi-target perception, characterized in that, Applied to a satellite-ground collaborative communication and sensing integrated system, the satellite-ground collaborative communication and sensing integrated system includes: multiple satellites, multiple ground stations and one satellite application center, including: An acquisition module, configured to acquire the status data of each integrated sensing and communication (ISAC) device in the satellite-ground collaborative communication and sensing integrated system; wherein, all satellites and all ground stations in the satellite-ground collaborative communication and sensing integrated system are equipped with ISAC devices; the status data includes: position coordinates, transmitted signals, received echo signals, and the channel matrix for communicating with external objects; A processing module, configured to aim at maximizing the cumulative reward, and use a distributed reinforcement learning model to process the status data of all ISAC devices, to obtain the target communication beamforming matrix and the target sensing beamforming matrix of each ISAC device; wherein, the agents in the distributed reinforcement learning model correspond one-to-one with the ISAC devices, the state of the agent is the status data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the sensing beamforming matrix of the corresponding ISAC device, the reward after all agents execute actions is positively correlated with the total throughput of the satellite-ground collaborative communication and sensing integrated system, and negatively correlated with the Cramér-Rao bound of the sensing of all drones by all ISAC devices; A determination module, configured to determine a joint beamforming strategy for the space-ground collaborative communication and sensing integrated system based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, so as to perform high-precision positioning on the unmanned aerial vehicles within the coverage of the system based on the joint beamforming strategy; Wherein, the determination module includes: A first determination unit, configured to determine the transmission signal of each ISAC device based on the joint beamforming strategy, and obtain the position coordinates of each ISAC device and the echo signal received by it; A second determination unit, configured to determine the DOA parameters of the target unmanned aerial vehicle closest to it within its sensing range based on the echo signal of the target ISAC device; and, based on the echo signal and the transmission signal of the target ISAC device, determine the signal propagation delay between the target ISAC device and the target unmanned aerial vehicle; wherein, the target ISAC device represents any integrated sensing and communication ISAC device in the space-ground collaborative communication and sensing integrated system; the DOA parameters of the target unmanned aerial vehicle include: the azimuth angle and the elevation angle of the target unmanned aerial vehicle relative to the antenna array of the target ISAC device; A positioning unit, configured to perform three-dimensional positioning on the target unmanned aerial vehicle based on the position coordinates of the target ISAC device, the signal propagation delay between the target ISAC device and the target unmanned aerial vehicle, and the DOA parameters of the target unmanned aerial vehicle, to obtain the position coordinates of the target unmanned aerial vehicle sensed by the target ISAC device; A repeated execution unit, configured to suppress the echo signal of the target unmanned aerial vehicle, and return to call the second determination unit and the positioning unit until each ISAC device determines the position coordinates of each unmanned aerial vehicle within its sensing range; A third determination unit, configured to determine the high-precision positioning result of each unmanned aerial vehicle based on the position coordinates of each unmanned aerial vehicle sensed by all the ISAC devices and the preset sensing accuracy coefficient corresponding to each ISAC device.

8. An electronic device, comprising a memory and a processor, wherein a computer program capable of running on the processor is stored on the memory, characterized in that When the processor executes the computer program, it implements the joint beamforming method based on space-ground collaborative communication and multi-target sensing according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by the processor, it implements the joint beamforming method based on space-ground collaborative communication and multi-target sensing according to any one of claims 1 to 6.

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