Joint beamforming method based on satellite-ground cooperative communication and multi-target perception

Abstract

The invention provides a joint beamforming method based on satellite-ground cooperative communication and multi-target sensing, and relates to the technical field of communication and sensing integration, the method is applied to a satellite-ground cooperative communication and sensing integrated system with a plurality of satellites, a plurality of ground stations and a satellite application center, and all the satellites and all the ground stations are equipped with ISAC equipment. After the state data of the ISAC devices are obtained, the state data of all the ISAC devices are processed by using a distributed reinforcement learning model with the purpose of maximizing accumulated rewards, and a target communication beam forming matrix and a target sensing beam forming matrix of each ISAC device are obtained; and awards after all agents execute actions in the model are in positive correlation with the total throughput of the system and are in negative correlation with the Cramer-Rao bound sensed by all the ISAC equipment to all the unmanned aerial vehicles. Therefore, timeliness and accuracy of unmanned aerial vehicle positioning can be ensured, and high-reliability service is provided for target perception and data transmission of the unmanned aerial vehicle.

Description

Technical Field

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

[0002] In recent years, with the development of unmanned aerial vehicle (UAV) technology, UAVs have been increasingly widely used in various fields such as commerce and security. However, in sensitive areas such as airports, the illegal intrusion of unauthorized aircraft poses a serious security threat, which may cause infrastructure damage and other security hazards. Therefore, the demand for accurate and flexible sensing 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 and more severe.

[0003] To meet the growing demand for communication and sensing capabilities of 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 airspace, with a limited sensing range and blind spots in the field of view. 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 the integrated communication and sensing 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 cooperative communication and multi-target sensing to ensure the timeliness, continuity, and accuracy of UAV positioning, and to provide high-reliability services for UAV target sensing and data transmission.

[0005] In a first aspect, the present invention provides a joint beamforming method based on satellite-ground cooperative communication and multi-target perception, which is applied to a satellite-ground cooperative communication and perception integrated system. The satellite-ground cooperative 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 cooperative communication and perception integrated system; wherein, all satellites and all ground stations in the satellite-ground cooperative 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 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 cooperative communication and perception integrated system and negatively correlated with the Cramer-Rao bound of all ISAC devices' perception of all unmanned aerial vehicles; based on the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices, determining the joint beamforming strategy of the satellite-ground cooperative communication and perception integrated system, so as to perform high-precision positioning on the unmanned aerial vehicles within the system coverage range.

[0006] Optionally, high-precision positioning of the drones within the system coverage is performed based on the joint beamforming strategy, including: Step 201, determining the transmitted 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 drone 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 drone based on the echo signal and the transmitted signal of the target ISAC device; where the target ISAC device represents any integrated sensing and communication ISAC device in the satellite-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, performing 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, suppressing the echo signal of the target drone, and returning to execute Step 202 until each ISAC device determines the position coordinates of each drone within its sensing range; Step 205, determining the high-precision positioning result of each drone based on the position coordinates of each drone 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 drone 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; where the target subspace includes: the signal subspace, the interference subspace, and the noise subspace; determining the number of drones 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; and determining the position corresponding to the peak of the MUSIC spectrum within the preset angle search range as the DOA parameters of the target drone.

[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 time 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 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 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 perception integrated system. The satellite-ground collaborative communication and perception integrated system includes: a plurality of satellites, a plurality of ground stations, and one satellite application center, and includes: an acquisition module, configured to acquire the status 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 status data includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects; a processing module, configured to process the status data of all ISAC devices by using a distributed reinforcement learning model with the goal of maximizing the cumulative reward, to obtain a target communication beamforming matrix and a target perception 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 status data of the corresponding ISAC device, the actions of the agent are the communication beamforming matrix and the perception 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 Cramér-Rao lower bound of the perception of all UAVs by all ISAC devices; a determination module, configured to determine a joint beamforming strategy for the satellite-ground collaborative communication and perception integrated system based on the target communication beamforming matrix and the target perception beamforming matrix of all ISAC devices, so as to perform high-precision positioning on the UAVs 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, where a computer program that can run on the processor is stored on the memory, and 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, where computer instructions are stored, and 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 sensing 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, 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 Cramer-Rao 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 by ground stations and high-altitude large-scale observation by 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 use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description 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 sensing integrated system provided by an embodiment of the present invention; 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; 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; 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; Figure 5 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention 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. Usually, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0019] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying 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 shall fall within the scope of protection of the present invention.

[0020] The following will describe in detail some embodiments of the present invention in conjunction with the accompanying drawings. Without conflict, the embodiments and features in the following embodiments may be combined with each other.

[0021] Embodiment 1 In the existing wireless communication system, 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, achieve adaptive beamforming, and effectively suppress interference at the same time; how to design the satellite-ground collaborative communication-sensing integration network architecture; how to achieve the trade-off between 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.

[0022] The purpose 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 global coverage and spectrum sharing; and propose a communication-sensing integration beamforming method suitable for this architecture, so as to improve the accuracy of UAV target sensing while meeting communication services.

[0023] 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. As Figure 1 shown, the satellite-ground collaborative communication and sensing integration 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, provide preliminary detection with low resolution and communication relay, support regional sensing enhancement, generate preliminary target direction and distance estimates; and at the same time support 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 nearby UAV targets, obtains accurate DOA parameters (azimuth and elevation angles) and delay estimates; and at the same time serves as a communication terminal for satellites; a 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.

[0024] Figure 2The flowchart of a joint beamforming method based on satellite-ground collaborative communication and multi-target perception provided by an embodiment of the present invention is as follows Figure 2 As shown, the method includes the following steps: Step 102, obtain the state data of each integrated sensing and communication (ISAC) device in the satellite-ground collaborative communication and sensing integrated system.

[0025] Among them, all satellites and all ground stations in the satellite-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 transmit antenna array. Each satellite has transmit antennas and receive antennas. Each ground station has transmit antennas and receive antennas. In the embodiment of the present invention, it is default that the transmit antenna arrays and receive antenna arrays of all satellites are the same, and the transmit antenna arrays and receive antenna arrays of all ground stations are also the same.

[0026] The set of aerial drone targets co-perceived by satellites and ground stations is represented as , and the total number of drone targets is unknown before drone target perception and is specifically determined by the satellite application center: in the drone perception stage, each satellite and ground station sends its respective perception results (that is, the position coordinates of the drone targets in its coverage area) to the satellite application center. The satellite application center takes the union of all perception results as the set of aerial drone targets, and then determines the total number of aerial drone targets. In the embodiment of the present invention, it is assumed that the distances between individual drone targets are far enough. Therefore, the satellite application center regards multiple drones within a certain small spatial distribution range as the same drone.

[0027] The state data of the ISAC device includes: position coordinates, transmitted signals, received echo signals, and channel matrices for communicating with external objects.

[0028] 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 unmanned aerial vehicle (perception object) within the satellite coverage. Considering that the number of ground station devices within each satellite coverage is limited, the ground station devices have disabled time periods, and a ground station can only communicate with one satellite in a single time period, appropriate ground station devices should be matched for 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 actual situations.

[0029] Therefore, if the ISAC device is configured on a satellite, the transmitted signal (integrated signal of 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 sensing beamforming matrix of the ISAC device m, represents the sensing beamforming vector designed for the ISAC device m to sense the unmanned aerial vehicle target k, which can control the directivity and intensity of the signal and determines the detection ability of the ISAC device m for the unmanned aerial vehicle target k; represents the sensing signal vector of the ISAC device m, represents the sensing symbol vector transmitted by the ISAC device m to the unmanned aerial vehicle target k for target sensing.

[0030] If the transmitting and receiving arrays of the ISAC device m are respectively and uniform planar arrays (UPA, uniform planar array) with a half-wavelength antenna spacing, i.e., . Considering that the signals received by the radar receiver of the ISAC device include the echo signals reflected from the unmanned aerial vehicle target, the interference signals of non-expected clutter, as well as self-interference and noise, the echo signal model received by the ISAC device m is expressed as: ; where, represents the reflection coefficient of the unmanned aerial vehicle k, Denote the reflection coefficient of clutter j. The above two reflection coefficients mainly depend on the radar cross-section, transmission distance, and carrier frequency; Denote the angle of the UAV k relative to the horizontal direction of the ISAC device m, i.e., the azimuth angle; Denote the angle of the UAV k relative to the vertical direction of the ISAC device m, i.e., the elevation angle. Similarly, That is, denote the azimuth angle and elevation angle of clutter j relative to the ISAC device m. Denote The receiving antenna steering vector in the direction of , where H represents the conjugate transpose, Denote The transmitting antenna steering vector in the direction of Denote the noise plus self-interference signal with variance (known parameter), , .

[0031] If the ISAC device is configured on the 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 (i.e., the ground station n) can be determined.

[0032] In the embodiments of the present invention, 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 matching ground station, 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.

[0033] 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 should not only act as an ISAC device to sense UAV targets but also act 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 of the ground station n can be expressed as: ; where Denote the Gaussian white noise with variance (known parameter) at the ground station n, Denote the channel matrix between the satellite m and the ground station n. Based on the free space model and considering the Doppler effect, , Denote the path gain, Denote the transmission gain of satellite m, Denote the reception gain of ground station n, Denote the propagation distance between satellite m and ground station n at time t, Denote the wavelength of the transmitted signal, Denote the carrier frequency of the transmitted signal, Denote the propagation delay between satellite m and ground station n at time t, Denote 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.

[0034] 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 of each ISAC device.

[0035] 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, in which 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 negatively correlated with the Cramer-Rao lower bound of all ISAC devices' sensing of all UAVs.

[0036] Based on the state, 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-ground collaborative communication and sensing integrated system, represents the Cramer-Rao lower bound of the ISAC device z's sensing of the UAV target k.

[0037] 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.

[0038] Step 106: Based on the target communication beamforming matrices and target sensing beamforming matrices of all ISAC devices, determine the joint beamforming strategy of the space-ground collaborative communication and sensing integrated system, so as to perform high-precision positioning on the UAVs within the coverage of the system based on the joint beamforming strategy.

[0039] The joint beamforming strategy of the space-ground collaborative communication and sensing integrated system includes the target communication beamforming matrices and target sensing beamforming matrices 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 directions and sensing beam directions of each ISAC device are optimal, the ground station performs low-altitude high-precision sensing on the UAVs, the satellite performs high-altitude low-precision sensing on the UAVs, 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 space-ground data fusion, so as to achieve high-precision positioning on the UAVs within the coverage of the system.

[0040] The embodiment of the present invention provides a joint beamforming method based on space-ground collaborative communication and multi-target sensing, which is applied to a space-ground collaborative communication and sensing 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, use a distributed reinforcement learning model to process the status data of all ISAC devices to obtain the target communication beamforming matrix and 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 UAVs by all ISAC devices. Therefore, the present invention can ensure the timeliness, continuity and accuracy of UAV positioning based on the collaborative sensing of the low-altitude high-precision sensing of the ground station and the high-altitude large-scale observation of the satellite, and provides a highly reliable service for UAV target sensing and data transmission.

[0041] In an optional implementation manner, in the above step 106, performing high-precision positioning on the UAVs within the coverage of the system based on the joint beamforming strategy specifically includes the following steps: 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.

[0042] Specifically, the joint beamforming strategy includes the target communication beamforming matrix and the target sensing beamforming matrix of all ISAC devices. By 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 and unchanged, while the position coordinates of the ISAC device arranged on the satellite are not fixed and unchanged. Therefore, to perform high-precision positioning of the UAV, the position coordinates of the ISAC device should also be updated periodically.

[0043] Step 202: Determine 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, 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; 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 UAV include: the azimuth angle and the elevation angle of the target UAV relative to the antenna array of the target ISAC device.

[0044] In the embodiment of the present invention, 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 specifically includes the following steps: Step 202a: 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; where the target subspace includes: the signal subspace, the interference subspace, and the noise subspace.

[0045] Taking the ISAC device m set on the satellite as an example, the method for determining the DOA parameters of the target UAV will be introduced below. The method for the ISAC device set on the ground station to determine the DOA parameters of the target UAV within its sensing range is the same in principle.

[0046] In the embodiment of the present invention, in order to estimate the DOA parameters 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, including 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-desired clutter interference signal, represents the propagation matrix of the clutter interference signal, describing 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.

[0047] For the covariance matrix the eigenvalue decomposition 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.

[0048] Step 202b, determine the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace.

[0049] 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, the eigenvalues of the signal subspace can be intercepted according to the position with the largest eigenvalue change rate, and the number of eigenvalues of the signal subspace is the number of UAVs within the sensing range of this ISAC device.

[0050] 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.

[0051] 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.

[0052] The signal subspace is spanned by the receiving antenna steering vector , 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, and the position corresponding to the peak of the MUSIC spectrum is the DOA parameter of the target UAV , which can be expressed as: .

[0053] 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: .

[0054] In an embodiment of the present invention, based on the echo signal and the transmitted signal of the target ISAC device, the signal propagation delay between the target ISAC device and the target UAV is determined, which specifically includes the following steps: Step 2021, perform matched filtering on the transmitted signal and the echo signal to obtain a matched filtering result.

[0055] 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 enhance the amplitude of the UAV target signal and at the same time 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 represents the i-th column of represents the transmitted signal 's conjugate flip; represents the convolution operation, represents the matched filtering result.

[0056] Step 2022, based on the matched filtering result and the transmitted signal, determine the multi-channel generalized cross-correlation function.

[0057] In order to accurately estimate the propagation delay between the echo signal and the transmitted signal, the embodiment of the present invention uses generalized cross-correlation (GCC-PHAT) to process the signal, thereby enhancing the matching robustness between signals. The multi-channel generalized cross-correlation function reveals the similarity of 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 of 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: ; represents the j-th column of represents 's complex conjugate, represents the i-th column of represents the inverse Fourier transform, represents the numerical stability term.

[0058] 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.

[0059] Specifically, in order to further suppress noise, the embodiments of the present invention use robust statistics to construct a dynamic detection threshold: , where , represents the noise floor estimate, represents the dynamic detection threshold, that is, the above-mentioned noise detection threshold; , and respectively represent the median, median absolute deviation, and standard deviation.

[0060] Assuming that the time delays corresponding to each UAV target are sufficiently separated, the multi-channel method is used to decouple the reflected signals of each UAV target, and the peak position of the generalized cross-correlation function is searched within the preset peak search range . The sampling time delay index of the preliminary time delay estimate of the target UAV k is: ; represents the preset peak search range, represents the sampling points within the search range.

[0061] Step 2024, optimize the initial peak position using the weighted quadratic fitting method to obtain the target peak position.

[0062] In the above time delay estimation, the initially detected sampling time 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 time delay estimate. Specifically, for the sampling time delay index of the preliminary time delay estimate, a fitting window is set, represents the window size. Select within the window range and fit the quadratic polynomial , where a, b, and c all represent fitting parameters. To enhance the robustness of the fitting, a weight matrix is introduced, and the weight is defined based on the distance from the sampling point to the sampling time delay index of the preliminary time delay estimate as: .

[0063] Next, construct the weighted least squares equation: , , , and the least squares solution is . The extreme point (peak) of the quadratic curve is the sampling time delay index (that is, the target peak position in the above text) , which can be solved by setting the second derivative to zero from to obtain .

[0064] Step 2025: 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.

[0065] 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.

[0066] Step 203: Perform 3D 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.

[0067] Specifically, according to the DOA parameters of the target UAV and the delay estimation result, use geometric relationships to perform 3D positioning on the target UAV: ; 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.

[0068] 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.

[0069] 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.

[0070] The process of the ISAC device deployed on the satellite sensing the UAV target is described in detail above. In the embodiment of the present invention, the remote sensing and communication fusion mechanism of the satellite can be extended to the ground station subsystem. While providing low-resolution sensing of airspace UAV targets at the satellite layer, the ground station layer conducts joint communication and close-range target sensing. The DOA and delay estimation algorithms are similar to those of the satellite, achieving precise sensing of UAV targets 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, Denote the signal propagation delay between the ISAC device \(n\) and the UAV target \(k\). Denote the DOA parameter estimation result of the ISAC device \(n\) for the UAV target \(k\).

[0071] 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: .

[0072] Step 205: 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.

[0073] Specifically, fuse 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 Denote the preset sensing accuracy coefficient of the ISAC device, which is proportional to the sensing accuracy of the ISAC device. Denote the true position coordinates of the UAV target \(k\).

[0074] Solve the above objective function using the weighted least squares method to obtain the high-precision positioning result of the UAV target \(k\): .

[0075] In an optional implementation manner, the embodiment of the present invention further includes the step of calculating the Cramer-Rao bound of the target ISAC device's perception of the target UAV: 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.

[0076] 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 Denote The receiving antenna steering vector in the direction of Denote the time delay phase shift, and the operation symbol Denote matrix dot product.

[0077] Step 302: Construct a Fisher information matrix based on the combined steering vector.

[0078] If the i-th parameter in the parameter vector is denoted as , and the j-th parameter is denoted as , then the element in the i-th row and j-th column of the Fisher information matrix (FIM) defined in the embodiments of the present invention is: , where represents the noise power perturbation. Based on this, the Fisher information matrix is expanded as: , where Sym represents symmetric elements, that is, the elements in the lower triangular part of the matrix are equal to their corresponding elements in the upper triangular part.

[0079] Step 303: Determine the Cramer-Rao bound for the target ISAC device to sense the target UAV based on the Fisher information matrix.

[0080] The Cramer-Rao bound of the i-th parameter in the parameter vector 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 respectively expressed as: , , . Therefore, the combined Cramer-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 Cramer-Rao bounds for all ISAC devices to sense all UAVs can be calculated.

[0081] 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 area; 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.

[0082] The embodiments of the present invention further include the following steps for calculating the total throughput of the satellite-ground cooperative communication and sensing integrated system: Step 401: Obtain the channel matrix between each satellite and each ground station it matches, the channel matrix between each satellite and each unmanned aerial vehicle (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 (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.

[0083] In the embodiment of the present invention, the first SINR of the downlink between the target satellite m and the target ground station n is calculated by the following formula: . In the formula, represents the channel matrix between satellite i and the ground station j it matches, represents the channel matrix between satellite i and the UAV target k within its coverage. The interference mainly includes co-frequency signal interference from other satellites, downlink received signal interference from other ground stations, and radar sensing waveform interference.

[0084] Step 402: 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.

[0085] The throughput can reflect the actual communication capacity of the system. The throughput of the downlink communication link between the target satellite m and the target ground station n can be expressed as: . Among them, represents the authorized bandwidth occupied by the transmitted signal of the target satellite m.

[0086] 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, 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.

[0087] In the embodiment of the present invention, the second SINR of the downlink between the target ground station n and the satellite application center center is calculated by the following formula: ; where represents the channel matrix from the target ground station n to the satellite application center center, and respectively represent the number of transmitting antennas of the ground station and the number of receiving antennas of the satellite application center, represents the channel matrix between ground station e and the UAV target k within its coverage, and respectively represent the target communication beamforming matrix and the target sensing beamforming matrix in the integrated communication and sensing transmission signal of the ground station e.

[0088] Step 404: Calculate the throughput of the downlink communication link between the target ground station and the satellite application center based on the second signal-to-interference-plus-noise ratio and the authorized bandwidth occupied by the transmission signal of the target ground station.

[0089] The throughput of the downlink communication link between the target ground station n and the satellite application center is: , where represents the authorized bandwidth occupied by the transmission signal of the target ground station n.

[0090] Step 405: Determine the total throughput of the satellite-ground cooperative communication and sensing integrated system based on the throughput of the downlink communication links between all satellites and their corresponding 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: .

[0091] 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 cooperative sensing accuracy. Therefore, the objective function for jointly optimizing beamforming can be expressed as: . Where 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 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, and this constraint requires that the total power of the transmission signal is limited by hardware and cannot exceed the power upper limit; represents the minimum gain of the radar full-space scan, represents the set of angles in the full space, and 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 does not differ by more than the ratio in all spatial directions, that is, the gain in the weakest direction is not less than times the gain in the strongest direction, to avoid insufficient sensing in some directions.

[0092] To verify the performance of the embodiments of the present invention, assume that the satellite-ground cooperative communication and sensing integrated system includes 1 satellite and 2 ground stations, and all are equipped with integrated communication and sensing transmitters and radar receivers, , , , , the initial positions of two drones 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 200m / s, and the system noise power is set to -110dBm. Figure 3 It is a schematic diagram of the relationship between the DOA estimation accuracy MSE and the device transmission power after positioning using the method provided by the embodiment of the present invention. From Figure 3 it can be seen that as the transmission power increases, the DOA estimation accuracy MSE gradually decreases.

[0093] 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. With the goal of jointly optimizing throughput and sensing accuracy, it combines DOA, time delay estimation, and beamforming optimization to achieve UAV target positioning and sensing, and through space-ground collaborative sensing, fuses multi-source data, improves the UAV target positioning accuracy, ensures the fairness of communication and sensing, and realizes the collaborative and efficient sensing and communication optimization between satellites and ground stations in complex multi-target scenarios.

[0094] Embodiment 2 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-mentioned Embodiment 1. The following is a specific introduction to the joint beamforming device based on space-ground collaborative communication and multi-target sensing provided by the embodiment of the present invention.

[0095] Figure 4 It is a functional module diagram of a joint beamforming device based on space-ground collaborative communication and multi-target sensing provided by 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: 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.

[0096] The processing module 20 is configured to process the status data of all ISAC devices by using a distributed reinforcement learning model with the goal of maximizing the cumulative reward, so as 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, 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 for sensing all UAVs.

[0097] The determining module 30 is configured to determine the joint beamforming strategy of 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 UAVs within the coverage range of the system based on the joint beamforming strategy.

[0098] An embodiment of the present invention provides a joint beamforming device based on satellite-ground collaborative communication and multi-target sensing, which is applied to a satellite-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 bound of all ISAC devices for sensing all UAVs. Therefore, the embodiment of the present invention can ensure the timeliness, continuity and accuracy of UAV positioning based on the collaborative sensing of low-altitude high-precision sensing of the ground station and high-altitude large-range observation of the satellite, and provides a highly reliable service for UAV target sensing and data transmission.

[0099] Optionally, the determining module 30 includes: The first determining unit is 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.

[0100] A second determination unit, configured to determine 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, 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; 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 the elevation angle of the target UAV relative to the antenna array of the target ISAC device.

[0101] A positioning unit, configured to perform 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, so as to obtain the position coordinates of the target UAV sensed by the target ISAC device.

[0102] 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.

[0103] 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.

[0104] Optionally, the second determination unit is specifically configured to: 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; wherein, the target subspace includes: the signal subspace, the interference subspace, and the noise subspace.

[0105] Determine the number of UAVs within the sensing range of the target ISAC device based on the eigenvalues of the target subspace.

[0106] 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.

[0107] 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.

[0108] Optionally, the second determination unit is further configured to: Perform matched filtering on the transmitted signal and the echo signal to obtain the matched filtering result.

[0109] Determine the multi-channel generalized cross-correlation function based on the matched filtering result and the transmitted signal.

[0110] 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.

[0111] Optimize the initial peak position using the weighted quadratic fitting method to obtain the target peak position.

[0112] 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.

[0113] Optionally, the device is further configured to: 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.

[0114] Construct a Fisher information matrix based on the joint steering vector.

[0115] Determine the Cramer-Rao bound for the target ISAC device to sense the target UAV based on the Fisher information matrix.

[0116] 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.

[0117] 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.

[0118] Optionally, the device is further configured to: 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, to calculate the first signal-to-interference-plus-noise ratio 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.

[0119] Based on the first signal-to-interference-plus-noise ratio 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.

[0120] 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 signal-to-interference-plus-noise ratio of the downlink between the target ground station and the satellite application center.

[0121] Based on the second signal-to-interference-plus-noise ratio 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.

[0122] Based on the throughput of the downlink communication links between all satellites and their matching ground stations 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 integration system.

[0123] Embodiment III 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, and the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0124] 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. Through at least one communication interface 63 (which can be wired or wireless), a communication connection between the system network element and at least one other network element can be realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0125] The bus 62 may be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 5 only a bidirectional arrow is used in

[0126] Among them, the memory 61 is used to store a program, and 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.

[0127] The processor 60 may be an integrated circuit chip with the ability to process signals. 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 the 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 the 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.

[0128] A computer program product of a joint beamforming method based on satellite-ground cooperative 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, which will not be elaborated here.

[0129] 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.

[0130] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable 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 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.

[0131] 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.

[0132] 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 distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0133] In addition, the terms "horizontal", "vertical", "overhanging", etc. do not mean that the components are required to be absolutely horizontal or overhanging, 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.

[0134] In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, 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 internal communication of two components. 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.

[0135] 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 it; 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 on 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 various embodiments of the present invention.

Claims

1. A joint beamforming method based on satellite-ground coordinated communication and multi-target perception, characterized in that: Applied to a satellite-ground coordinated communication and perception integrated system, the satellite-ground coordinated communication and perception integrated system comprising: a plurality of satellites, a plurality of ground stations and a satellite application center, the method comprising: Acquire status data of each integrated sensing and communication ISAC device in the satellite-ground coordinated communication and perception integrated system; wherein all satellites and all ground stations in the satellite-ground coordinated communication and perception integrated system are equipped with ISAC devices; the status data include: position coordinates, transmitted signals, received echo signals and channel matrices for communicating with external objects; With the goal of maximizing the cumulative reward, the state data of all ISAC devices are processed using a distributed reinforcement learning model to obtain a target communication beamforming matrix and a target perception beamforming matrix of each ISAC device; wherein the agent in the distributed reinforcement learning model corresponds to the ISAC device one-to-one, 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 perception beamforming matrix of the corresponding ISAC device, and the reward after all agents perform actions is positively correlated with the total throughput of the satellite-ground coordinated communication and perception integrated system, and negatively correlated with the Cramer-Rao bound perceived by all ISAC devices on all drones; Based on the target communication beamforming matrix and the target perception beamforming matrix of all ISAC devices, the joint beamforming strategy of the satellite-ground collaborative communication and perception integrated system is determined, so as to perform high-precision positioning of the UAV within the coverage of the system based on the joint beamforming strategy.

2. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 1 is characterized in that: Based on the joint beamforming strategy, high-precision positioning of UAVs within the coverage of the system is performed, including: Step 201, determining the transmission signal of each ISAC device based on the joint beamforming strategy, and obtaining the position coordinates of each ISAC device and the echo signal received by it; Step 202, determining the DOA parameters of the target UAV that is closest to it within the 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 the transmission signal of the target ISAC device; wherein the target ISAC device represents any integrated sensing and communication ISAC device in the satellite-ground coordinated communication and perception integrated system; the DOA parameters of the target UAV include: the azimuth and pitch angle of the target UAV relative to the antenna array of the target ISAC device; 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, the target UAV is three-dimensionally positioned 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 step 202, until each ISAC device determines the position coordinates of each UAV within its sensing range; Step 205: Determine a high-precision positioning result for each drone based on the position coordinates of each drone sensed by all the ISAC devices and a preset perception accuracy coefficient corresponding to each ISAC device.

3. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 2 is characterized in that: Based on the echo signal of the target ISAC device, the DOA parameters of the nearest target drone within its sensing range are determined, including: Constructing a covariance matrix of the echo signal to perform eigendecomposition on the covariance matrix to obtain an eigenvector matrix and eigenvalues ​​of a target subspace; wherein the target subspace includes: a signal subspace, an interference subspace, and a noise subspace; Determine the number of drones within the sensing range of the target ISAC device based on the eigenvalue of the target subspace; Calculating a MUSIC spectrum within a preset angle search range based on an eigenvector matrix of the noise subspace and a receiving antenna steering vector function; The position corresponding to the peak of the MUSIC spectrum within the preset angle search range is determined as the DOA parameter of the target UAV.

4. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 2 is characterized in that: Determining a signal propagation delay between the target ISAC device and the target UAV based on the echo signal and the transmission signal of the target ISAC device includes: Performing matched filtering on the transmission 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 transmit 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, wherein the initial peak position represents a sampling delay index of a preliminary delay estimate of the target UAV; The initial peak position is optimized by using a weighted quadratic fitting method to obtain a target peak position; The signal propagation delay between the target ISAC device and the target UAV is calculated based on the sampling rate of the transmitted signal and the target peak position.

5. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 2 is characterized in that: Also 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; wherein the joint steering vector includes a spatial phase response and a delay effect; constructing a Fisher information matrix based on the joint steering vector; The Cramer-Rao bound of the target ISAC device's perception of the target UAV is determined based on the Fisher information matrix.

6. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 1 is characterized in that: 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; 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 drone within its coverage area.

7. The joint beamforming method based on satellite-ground coordinated communication and multi-target perception according to claim 6 is characterized in that: Also includes: Obtain a channel matrix between each satellite and each ground station matched with it, a channel matrix between each satellite and each UAV within its coverage, and a communication beamforming matrix and a perception beamforming matrix of an ISAC device on each satellite, so as to calculate a first signal to interference and noise ratio of a downlink between a target satellite and a target ground station; wherein the target satellite represents any satellite in the satellite-ground coordinated communication and perception integrated system; and the target ground station represents any ground station in the satellite-ground coordinated communication and perception integrated system; Calculating the throughput of the downlink communication link between the target satellite and the target ground station based on the first signal to interference and noise ratio and the authorized bandwidth occupied by the transmission signal of the target satellite; Obtain a channel matrix between each ground station and the satellite application center, a channel matrix between each ground station and each UAV within its coverage, a communication beamforming matrix and a sensing beamforming matrix of an ISAC device on each ground station, so as to calculate a second signal to interference and noise ratio of a 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 signal to interference and noise ratio and the authorized bandwidth occupied by the transmission signal of the target ground station; 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 total throughput of the satellite-ground coordinated communication and perception integrated system is determined.

8. A joint beamforming device based on satellite-ground coordinated communication and multi-target perception, characterized in that: Applied to a satellite-ground coordinated communication and perception integrated system, the satellite-ground coordinated communication and perception integrated system comprising: multiple satellites, multiple ground stations and a satellite application center, including: An acquisition module is used to acquire status data of each integrated sensing and communication ISAC device in the satellite-ground coordinated communication and perception integrated system; wherein all satellites and all ground stations in the satellite-ground coordinated communication and perception 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; A processing module is used to process the state data of all ISAC devices 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 perception beamforming matrix of each ISAC device; wherein the intelligent agent in the distributed reinforcement learning model corresponds to the ISAC device one by one, the state of the intelligent agent is the state data of the corresponding ISAC device, the action of the intelligent agent is the communication beamforming matrix and the perception beamforming matrix of the corresponding ISAC device, and the reward after all intelligent agents perform actions is positively correlated with the total throughput of the satellite-ground coordinated communication and perception integrated system, and negatively correlated with the Cramer-Rao bound perceived by all ISAC devices on all drones; The determination module is used to determine the joint beamforming strategy of the satellite-ground collaborative communication and perception integrated system based on the target communication beamforming matrix and the target perception beamforming matrix of all ISAC devices, so as to perform high-precision positioning of the UAV within the coverage of the system based on the joint beamforming strategy.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the joint beamforming method based on satellite-ground collaborative communication and multi-target perception described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the joint beamforming method based on satellite-ground collaborative communication and multi-target perception according to any one of claims 1 to 7 is implemented.

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