Multi-uav cooperative positioning method in low-altitude scene

Abstract

The application provides a multi-unmanned aerial vehicle cooperative positioning method in a low-altitude scene, and is applied to the technical field of wireless communication and target sensing. A multi-unmanned aerial vehicle ISAC system model is constructed according to system initial parameters in a low-altitude target positioning scene, the system initial parameters including M unmanned aerial vehicles and K targets to be positioned; according to the multi-unmanned aerial vehicle ISAC system model, a target region where the M unmanned aerial vehicles and the K targets to be positioned are located is controlled to perform sensing scanning, and scanning results of the M unmanned aerial vehicles are obtained, and then a fusion voting heat map is constructed to determine candidate regions of the K targets to be positioned; in a kth candidate region of the K candidate regions, according to ISAC transmitting beam matrices of the M unmanned aerial vehicles, the final positioning coordinates of a kth target to be positioned located in the kth candidate region are determined through multi-frame echo accumulation and initial value correction. The method can realize multi-target and high-precision continuous positioning in a low-altitude complex environment.

Description

Technical Field

[0001] This application relates to the fields of wireless communication and target perception technology, and in particular to a multi-UAV cooperative localization method in low-altitude scenarios. Background Technology

[0002] With the rapid development of the low-altitude economy, UAV inspections, and air-to-ground cooperative networks, the demand for wireless communication and target localization in low-altitude scenarios is increasing simultaneously. Facing multiple targets in complex low-altitude environments, not only is stable multi-user communication capability required, but continuous and reliable multi-target localization is also necessary under conditions of obstruction, rapid topology changes, and fluctuating link conditions. Integrated Sensing and Communications (ISAC) technology, due to its ability to achieve resource sharing between communication and sensing and improve spectrum utilization efficiency, has become an important technological direction for addressing the needs of spectrum resource scarcity and functional integration.

[0003] Existing target localization methods based on ISAC technology typically prioritize communication throughput and reliability. Target localization still largely relies on Global Navigation Satellite System (GNSS), visual sensors, or independent sensors. Consequently, they are prone to decreased positioning accuracy and insufficient continuity in urban environments with obstructions and interference. Furthermore, while traditional radar or dedicated sensing systems offer strong positioning capabilities, they often require independent hardware platforms, dedicated spectrum resources, and additional deployment costs, making efficient integration with communication systems difficult.

[0004] Therefore, how to achieve robust and high-precision continuous positioning capability for multiple targets in complex low-altitude environments by multiple UAVs under the ISAC architecture has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a multi-UAV cooperative positioning method for low-altitude scenarios. It breaks the traditional multi-target positioning architecture by utilizing ISAC technology. Through multi-UAV cooperative beam scanning and two-dimensional fusion voting mechanism, it solves the problems of signal ambiguity and multipath interference in low-altitude target positioning scenarios. Furthermore, by using the ISAC transmission beam matrices of each UAV, it dynamically balances communication interference and sensing gain. The aim is to achieve high-precision continuous positioning of multiple targets in complex low-altitude environments without increasing additional hardware costs, providing reliable underlying support for low-altitude intelligent networks.

[0006] This application provides a multi-UAV cooperative positioning method in low-altitude scenarios, including: Based on the initial system parameters in a low-altitude target localization scenario, a multi-UAV collaborative communication and sensing integrated ISAC system model is constructed. The initial system parameters include at least the following: A drone and One target to be located. It is an integer greater than 2. It is an integer greater than 1; Based on the aforementioned multi-UAV cooperative communication and sensing integrated ISAC system model, control the A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results for each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; exist The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located .

[0007] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the initial system parameters further include... One communication user, For integers greater than 1, the one mentioned is... The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located Including: according to the Communication performance indicators for each communication user and stated Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. Each UAV has its own corresponding ISAC transmission beam matrix; for The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation According to the effective ranging quantity Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located .

[0008] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the control of the... A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results for each drone include: for the aforementioned The first of the drones The drone, for the first One drone configuration One scanning beam, It is an integer greater than 1; for the above The first of the scanning beams One scanning beam, to acquire the The targets to be located are respectively in the... The first drone Received echo power under each scanning beam; according to The complex scattering coefficients corresponding to each of the received echo powers are used to determine the first... The drone in the first The first received echo signal under each scanning beam ; for the first received echo signal Perform matched filtering to obtain the first matched filter output signal. ;according to The first matched filter output signal is used to determine the first... The first range image corresponding to each UAV; peak detection is performed on the first range image to obtain a preset number of candidate peaks and these candidate peaks are used as the first... The scan results for each drone.

[0009] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided. Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located includes: S1, by analyzing the candidate regions corresponding to the target to be located. The drone, the Each drone Each scanning beam and the aforementioned S2. Accumulate the scan results to construct a first fused voting heatmap; S3. Select the coordinate position corresponding to the global maximum value in the first fused voting heatmap as the first coarse positioning center; S4. Separate the candidate peaks corresponding to each UAV from the first coarse positioning center using geometric distances that meet the consistency condition. Then, construct a second fused voting heatmap based on the remaining candidate peaks. S5. Use the second fused voting heatmap as the new first fused voting heatmap, and repeat steps S2-S4 until the desired result is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined.

[0010] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the method is based on the... Communication performance indicators for each communication user and stated Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. Each UAV's corresponding ISAC transmit beam matrix includes: for the... The first of the communication users The communication user and the first One communication user, According to the above The drone is directed towards the first The channel vector and communication beam of the first communication user, facing the first The communication beams of each communication user, and the The sensing beam corresponding to each of the drones is used to determine the first... The signal-to-interference-plus-noise ratio of collaborative communication among individual communication users According to the above The signal-to-interference-plus-noise ratio and first weight of each communication user's collaborative communication are used to determine the... Communication performance indicators for each communication user Regarding the aforementioned first Candidate regions According to the first Candidate regions Inner The drone to the first The distance between the targets to be located The first The drone for the first Normalized sensing gain of each target to be located , and the first Sensing power of a drone Determine the first The drone for the first Sensing signal-to-noise ratio of a target to be located According to the above Each of the drones is for the first... The perceived signal-to-noise ratio of the nth target to be located is used to determine the nth target. Fusion sensing signal-to-noise ratio of individual targets to be located ; and according to the above The fused sensing signal-to-noise ratio and second weight of each target to be located are used to determine the... Perception performance indicators for each target to be located According to the aforementioned communication performance indicators and the aforementioned perception performance indicators Construct the joint beamforming function for communication and sensing, and based on the optimal power allocation ratio corresponding to the joint beamforming function for communication and sensing. Determine the Each UAV has its own corresponding ISAC transmission beam matrix.

[0011] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the first... The sensing beam corresponding to each drone The construction process is as follows: According to the first Candidate regions Construct the first A drone is facing the Channel matrix of communication users and facing the Target orientation matrix of targets to be located According to the channel matrix Construct the communication user subspace projection matrix ; and according to the target guidance matrix Construct the target direction projection matrix According to the communication user subspace projection matrix and the target direction projection matrix Construct the orthogonal complementary projection matrix According to the target direction projection matrix and the orthogonal complementary projection matrix Construct the first The sensing beam corresponding to each drone .

[0012] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the... The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation Including: for the aforementioned The first of the drones The drone, obtain the first The drone in the first Second received echo signal under frame The second received echo signal Includes the The echo superposition term and noise term of the target to be located; for the second received echo signal Perform matched filtering to obtain the second matched filter output signal; according to The second matched filter output signal corresponding to each frame determines the first... The second distance image corresponding to the drone; in the first Candidate regions Within the corresponding geometric threshold window, peak detection is performed on the second distance image to obtain the first... The set of candidate ranging values ​​corresponding to each UAV; from the Among the drones, the one participating in the positioning is obtained. A collection of drones targeting a specific location ; and the drones were assembled Each UAV is assigned a candidate ranging value from its candidate ranging value set as a ranging combination. Simultaneously, the ranging standard deviation combination corresponding to the ranging combination is obtained. The consistency cost corresponding to each of the multiple ranging combinations is obtained, and the ranging combination corresponding to the minimum consistency cost among the multiple consistency costs is taken as the first... Effective ranging for each target to be located The set of distance measurement standard deviations corresponding to the minimum value is used as the first... The combination of distance standard deviations corresponding to each target to be located .

[0013] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the effective ranging measurement is used... Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located Including: S1, the first The target to be located is in the first Candidate regions The coarse positioning center within is used as the initial position estimate. S2. Based on the initial position estimate... and the effective ranging quantity Construct the Jacobi matrix , Indicates the first The estimated location of each target to be located. Indicates the current iteration number; S3, based on Huber robustness parameters Determine the effective ranging quantity Robust weights corresponding to each candidate distance value; and a combination of all robust weights and the distance measurement standard deviation. Construct a weighted matrix S4. Based on the Jacobi matrix... and the weighting matrix Determine the first The position increment corresponding to each target to be located S5. Based on the position increment and the initial position estimate Determine the first The final positioning coordinates of the target to be located .

[0014] According to an embodiment of this application, a multi-UAV cooperative positioning method in a low-altitude scenario is provided, wherein the method is based on the position increment. and the initial position estimate Determine the first The final positioning coordinates of the target to be located This includes: S51, based on the position increment and the initial position estimate Determine the first S52, Reproject the initial positioning coordinates of the target to be located back to the first target; Candidate regions The interior is used as a new initial position estimate. S53. Repeat steps S2-S52 until the latest position increment is less than a preset threshold, and use the initial positioning coordinates determined based on the latest position increment as the first... The final positioning coordinates of the target to be located .

[0015] This application also provides a multi-UAV cooperative positioning system for low-altitude scenarios, including: The model building module is used to construct a multi-UAV cooperative communication and perception integrated ISAC system model based on the initial system parameters in a low-altitude target localization scenario. The initial system parameters include at least... A drone and One target to be located. It is an integer greater than 2. It is an integer greater than 1; The coarse positioning module is used to control the multi-UAV cooperative communication and perception integrated ISAC system model. A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; The precision positioning module is used for... The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located .

[0016] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the multi-UAV cooperative positioning method in any of the above-described low-altitude scenarios.

[0017] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the multi-UAV cooperative positioning method in low-altitude scenarios as described above.

[0018] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the multi-UAV cooperative positioning method in any of the above-described low-altitude scenarios.

[0019] The multi-UAV cooperative localization method in low-altitude scenarios provided in this application constructs a multi-UAV cooperative communication and sensing integrated ISAC system model based on the initial system parameters in the low-altitude target localization scenario. These initial system parameters include at least... A drone and One target to be located. It is an integer greater than 2. The integer is greater than 1; according to the multi-UAV ISAC system model, control the... A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; in The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located This method utilizes ISAC technology to break away from the traditional multi-target positioning architecture. By employing multi-UAV collaborative beam scanning and a two-dimensional fusion voting mechanism, it solves the problems of signal ambiguity and multipath interference in low-altitude target positioning scenarios. Furthermore, by using the ISAC transmission beam matrices of each UAV, it dynamically balances communication interference and sensing gain. The aim is to achieve high-precision continuous positioning of multiple targets in complex low-altitude environments without increasing additional hardware costs, thus providing reliable underlying support for the low-altitude intelligent network. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1This is a flowchart illustrating the multi-UAV cooperative positioning method in low-altitude scenarios provided in this application embodiment; Figure 2 This is a schematic diagram of the multi-UAV ISAC system model provided in the embodiments of this application; Figure 3 This is a three-dimensional scene diagram of the ISAC system model provided in the embodiments of this application; Figure 4 This is a one-dimensional power allocation search result diagram provided in the embodiments of this application; Figure 5 This is a schematic diagram of the LM iteration convergence curve provided in an embodiment of this application; Figure 6 This is a schematic diagram of the precise positioning results provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of a multi-UAV cooperative positioning system in a low-altitude scenario provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] To better understand the embodiments of this application, the prior art will first be described in detail: Existing target localization methods based on ISAC technology not only fail to provide robust and high-precision continuous localization capabilities for multiple targets in complex low-altitude environments using multiple UAVs within the ISAC architecture, but also suffer from the following drawbacks: Disadvantage 1: Lack of unified collaborative positioning mechanism: Existing target positioning methods based on ISAC technology often only use the results of a single echo or a single base station perception to directly estimate the final location, lacking a hierarchical processing mechanism, which makes it easy to cause false detection or positioning divergence under low-altitude obstruction, multipath and false alarm conditions.

[0024] Disadvantage 2: Insufficient collaborative utilization of multiple UAVs: Although existing target positioning methods based on ISAC technology have introduced multiple UAV platforms, they mainly remain at the level of simple measurement convergence. The utilization of multiple UAV resources is scattered, and the scanning, communication and positioning processes are isolated from each other, resulting in low overall system efficiency. At the same time, there is a lack of joint beam design and resource collaborative allocation mechanism for multi-user communication and multi-target positioning, making it difficult to simultaneously ensure communication quality and positioning accuracy.

[0025] Disadvantage 3: Separation of communication signals and sensing signals leads to higher system overhead: Existing target localization methods based on ISAC technology often use independent sensing waveforms and independent communication waveforms, which increases the complexity of waveform design, spectrum occupation and time and frequency resource overhead, and is not conducive to real-time collaborative deployment in complex low-altitude scenarios.

[0026] Disadvantage 4: Lack of unified optimization between communication beams and sensing beams: There is a lack of joint beam design and resource coordination allocation mechanism for multi-user communication and multi-target positioning, making it difficult to simultaneously meet the service quality requirements of communication users and the performance requirements of target sensing.

[0027] To address the aforementioned technical problems and shortcomings, this application provides a multi-UAV cooperative positioning method for low-altitude scenarios. This method utilizes ISAC technology to break away from the traditional multi-target positioning architecture. By employing multi-UAV cooperative beam scanning and a two-dimensional fusion voting mechanism, it solves the problems of signal ambiguity and multipath interference in low-altitude target positioning scenarios. Furthermore, by using the ISAC transmission beam matrices of each UAV, it dynamically balances communication interference and sensing gain. The aim is to achieve high-precision continuous positioning of multiple targets in complex low-altitude environments without increasing additional hardware costs, thus providing reliable underlying support for the low-altitude intelligent network.

[0028] The following describes the application scenarios of the multi-UAV cooperative positioning method in low-altitude scenarios provided in the embodiments of this application: The above positioning method can be applied to low-altitude target positioning scenarios such as low-altitude logistics monitoring, low-altitude inspection, and air-ground collaborative communication.

[0029] In the low-altitude logistics monitoring scenario: for high-density drone delivery routes, this application can use communication beams to ensure the real-time transmission of scheduling instructions, and at the same time use perception capabilities to accurately locate multiple cooperative or non-cooperative targets (such as other delivery drones, obstacles, etc.) in the airspace, effectively avoiding collision risks.

[0030] In low-altitude inspection scenarios: For areas where GNSS signals are attenuated or multipath interference is severe due to obstruction by tall buildings, this application utilizes the heterogeneous spatial distribution of multiple UAV clusters and the collaborative processing of ISAC signals to compensate for the lack of continuity in positioning by a single sensor, thereby achieving sub-meter level continuous locking of the inspection target.

[0031] In air-ground collaborative communication scenarios: when ground base stations are damaged or in blind spots, multiple drone swarms can act as mobile base stations to provide communication coverage and use communication signals to synchronously achieve real-time positioning and situational awareness of ground rescue targets or disaster sources, thus building an "integrated sensing and transmission" emergency dispatch network.

[0032] It should be noted that the execution entity involved in the embodiments of this application can be a multi-UAV cooperative positioning system in low-altitude scenarios, or an electronic device. Optionally, the electronic device may include: a computer / laptop, a mobile terminal, a server, an airborne processing unit, or a control chip with computing processing functions, etc.

[0033] The following uses an electronic device as an example to illustrate in detail the multi-UAV cooperative positioning method in low-altitude scenarios provided in this application: Figure 1 This is a flowchart illustrating the multi-UAV cooperative positioning method in low-altitude scenarios provided in this application embodiment. Figure 1 As shown, the method includes the following steps 101-104.

[0034] Step 101: Based on the initial system parameters in the low-altitude target localization scenario, construct a multi-UAV cooperative communication and sensing integrated ISAC system model. The initial system parameters should include at least the following: A drone and One target to be located. It is an integer greater than 2. It is an integer greater than 1.

[0035] Among them, the low-altitude target positioning scenario is a complex low-altitude scenario, which is a radio operation airspace with highly complex and dynamically changing characteristics.

[0036] The initial parameters of the system refer to the basic physical constraints and logical configuration information required to construct the ISAC cooperative network (i.e., the multi-UAV ISAC system model), which are the discrete input conditions for the algorithm to run.

[0037] Multi-UAV ISAC system model (e.g.) Figure 2 (As shown) is a mathematical-physical architecture that integrates distributed spatial reuse and deep resource fusion.

[0038] Drones are also known as unmanned aerial vehicles (UAVs).

[0039] Optionally, the initial parameters of the system may also include: The position parameters of each drone, The array parameters corresponding to each drone individual communication users Location parameters and communication threshold parameters of each communication user and sensing threshold parameters , It is an integer greater than 1.

[0040] Optionally, A drone may include a main drone and at least one auxiliary drone.

[0041] Optionally, the working area of ​​the multi-UAV ISAC system model can be a preset two-dimensional ground area. The ground height can be set to , It is an integer greater than 0.

[0042] In this multi-UAV ISAC system model, by A drone ensemble consisting of individual drones is available It is indicated that, of these, No. 1 is the main drone, and the rest are auxiliary drones; by A set of targets to be located, consisting of several targets, can be used. Indicate; by A set of communication users consisting of a number of communication users is available. express.

[0043] Optionally, The first of the drones The three-dimensional coordinates of the drone are available. Representation, i.e., coordinate position transpose; The first of the targets to be located The three-dimensional coordinates of the target to be located can be used express; The first communication user's The three-dimensional coordinates of each communication user are available. express.

[0044] It should be noted that, for Each of the drones is equipped with a uniform linear array, and the carrier frequency is set to . , wavelength is The spacing between array elements is and the number of array elements is ,in, Represents the speed of light. (Regarding the azimuth angle) The corresponding UAV array steering vector is The drone array's steering vector The expression is: .

[0045] In addition, the The drone to the first Distance between communication users that distance The calculation formula is: .

[0046] Optionally, the initial parameters of the system may further include: communication noise power. Sensing noise power System bandwidth Maximum power of a single drone Target radar cross section Pulse accumulation number Reference sequence length Number of scanning beams of a single UAV The number of candidate peaks and the convergence threshold for location iteration, etc.

[0047] To facilitate the subsequent differentiation of received echo signals from different UAVs, each UAV is assigned a mutually distinguishable reference sequence, such as a Zadoff-Chu sequence with different cyclic shifts. The base sequence of this Zadoff-Chu sequence is... The basic sequence The calculation formula is: ,in, This represents the root sequence index.

[0048] For different UAVs, mutually distinguishable reference sequences can be constructed through cyclic shifting. Specifically, the first... The reference sequence for each drone is The reference sequence The calculation formula is: ,in, Indicates the first The cyclically shifted sequence index corresponding to the drone can be obtained after interpolation and normalization. Reference sequence of actual drone launches The reference sequence The expression is: ,in, This represents the length of the interpolated and normalized transmission reference sequence. Based on this, a multi-UAV ISAC system model can be constructed, providing a foundation for subsequent coarse positioning scanning, joint beamforming, and fine positioning iterations.

[0049] Step 102: Based on the multi-UAV ISAC system model, control... One drone pair Perform a sensor scan on the target area where the target to be located is located to obtain... The scan results for each drone.

[0050] Optionally, the target area is the aforementioned preset two-dimensional ground area. .

[0051] In some embodiments, electronic device control One drone pair Perform a sensor scan on the target area where the target to be located is located to obtain... The scan results for each drone can include: [specific data] The first of the drones The first drone, with electronic equipment being the first One drone configuration One scanning beam, For integers greater than 1; The first of the scanning beams The electronic device acquires a scanning beam. The targets to be located are respectively in the... The first drone Received echo power under each scanning beam; according to The complex scattering coefficients corresponding to each of the received echo powers are used to determine the first... The drone in the first The first received echo signal under each scanning beam ; For the first received echo signal Perform matched filtering to obtain the first matched filter output signal. The electronic device is based on The first matched filter output signal is used to determine the first... The electronic device performs peak detection on the first range image corresponding to the drone; obtains a preset number of candidate peaks and uses them as the first... The scan results for each drone.

[0052] In the embodiments of this application, for The first of the drones The first drone, with electronic equipment being the first One drone configuration One scanning beam, this A set of scanning beams consisting of several scanning beams can be used This indicates that each scanning beam in the scanning beam set corresponds to a preset center direction. and sector angle range The distance coverage range is determined by the system's maximum distance coverage range and the detected distance peak.

[0053] against The first of the scanning beams The first scanning beam, the third The scanning beam vector corresponding to each scanning beam can be used It means that, among them, Indicates the preset center direction The array manifold vector. During the scanning phase, the... The drone in the first The actual transmitted reference sequence in each scanning beam direction is: .

[0054] against The first of the targets to be located The target to be located, the first The target to be located is the first The distance between the drones is that distance The calculation formula is: Two-way propagation delay is It is a fixed value, the two-way propagation delay. The calculation formula is: Let the first... The target to be located is relative to the first The azimuth angle of the drone is At this time, the first Normalized beam gain for each drone The normalized beam gain The calculation formula is: , Represents the scanning beam vector The conjugate transpose of; further, the electronic equipment determines the first... based on the two-way radar equations. The target to be located is in the first The first drone Received echo power under each scanning beam The received echo power The calculation formula is: , Represents the system synthesis constant. Indicates the scanned transmit power. Indicates the first The electronic device then determines the scattering cross section of the target to be located; The target to be located and the target scattering phase and received echo power The relevant complex scattering coefficients, the complex scattering coefficients The calculation formula is: ,in, This indicates that the k-th target to be located is in the k-th position. The first drone The scattered phase of the echo under each scanning beam. Based on this, the electronic device can determine... The complex scattering coefficients corresponding to each received echo power.

[0055] Subsequently, the electronic device according to the above The complex scattering coefficients corresponding to each of the received echo powers are used to determine the first... The drone in the first The first received echo signal under each scanning beam The first received echo signal The calculation formula is: ,in, This represents the time delay after a signal is transmitted and then received. This represents the complex Gaussian noise vector. Then, the electronic device processes the first received echo signal. Perform matched filtering to obtain the first matched filter output signal. The first matched filter output signal The calculation formula is: ,in, This indicates the related operations of matched filtering. Represents the reference sequence The conjugate reference signal; and determine the first matched filter output signal. Corresponding range image power The distance image power The calculation formula is: Based on this, the electronic device can determine The distance image power is used to construct the first distance image power. The first range image corresponding to each UAV. It should be noted that when the effective bandwidth of the reference signal is... When, the corresponding distance resolution is The formula for calculating this distance resolution is: Finally, the electronic device performs peak detection on the first range image, obtaining the range peak position and peak intensity of each range peak (i.e., the detected peak) among all range peaks, where the... The distance peak positions are and peak intensity Then, based on peak intensity and combined with the minimum inter-peak interval constraint, only a preset number of candidate peaks are retained, and these candidate peaks are used as the first... The scan results for each drone.

[0056] The entire process effectively enhances the echo signal-to-noise ratio through refined beam scanning and range-image matched filtering. Combined with range consistency constraints, scanning beam sector coverage constraints, and peak constraints, it suppresses false peak interference and ensures that high-confidence coarse positioning observation results can be stably extracted subsequently.

[0057] It should be noted that, Each drone can form a set of candidate peaks by corresponding to a preset number of candidate peaks.

[0058] Step 103, according to Each scan result is used to construct a fusion voting heatmap. ,Sure Each target to be located corresponds to a candidate region.

[0059] Among them, the integrated voting heatmap It is a digital spatial model based on multi-UAV spatial collaborative observation, used to characterize the probability distribution of the existence of targets in the airspace.

[0060] Candidate regions refer to a subset of the potential existence space of targets with high confidence extracted from the fused voting heatmap.

[0061] It should be noted that each target to be located corresponds to a candidate region, meaning the electronic device can determine... These are candidate regions, formed by this A candidate region set consisting of candidate regions can be used express.

[0062] In some embodiments, the electronic device according to Each scan result is used to construct a fusion voting heatmap. ,Sure Each candidate region corresponding to a target to be located may include: S1, electronic devices through... One drone, Each drone Each scanning beam and The scan results are accumulated to construct a first fused voting heatmap; S2, the electronic device selects the coordinate position corresponding to the global maximum value in the first fused voting heatmap as the first coarse positioning center; S3, the electronic device determines... S4. The electronic device separates the candidate peaks corresponding to each UAV from the first coarse positioning center, and constructs a second fused voting heatmap based on the remaining candidate peaks. S5. The electronic device uses the second fused voting heatmap as the new first fused voting heatmap and repeats the above steps S2-S4 until a result is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined.

[0063] In this embodiment of the application, S1, the electronic device first scans the aforementioned two-dimensional ground area. Perform two-dimensional mesh generation and determine any two-dimensional mesh point. Then, calculate the two-dimensional grid points. To the Distance of a drone and azimuth If two-dimensional grid points Simultaneously satisfy and the two-dimensional grid points Located in the Within the coverage sector of the scanning beam, the first... Each distance peak to two-dimensional grid points To form a valid vote, among which, This indicates the preset ring band tolerance, which can be determined by both the grid size and the distance resolution. Based on this, the electronic device can... One drone, Each drone Each scanning beam and The scan results (i.e., all distance peaks) are summed to construct a two-dimensional first fused voting heatmap. Optionally, this first fused voting heatmap The main process involves projecting candidate peaks from the candidate peak set onto a two-dimensional mesh of the scene and then performing a fusion vote.

[0064] To avoid overlapping of coarse positioning centers due to the superposition of multiple targets to be located, a sequential peeling method can be used to extract the coarse positioning centers one by one. Specifically: S2, the electronic device in the above-mentioned first fusion voting heatmap Select the coordinates corresponding to the global maximum value as the first coarse localization center. The first coarse positioning center This is a coarse localization result, the first coarse localization center. The calculation formula is: S3. The electronic device acquires a candidate peak set and determines the geometric distance between each candidate peak in the set and the first coarse localization center. S4. The electronic device determines the relationship between each geometric distance and the consistency condition. If the current geometric distance satisfies the consistency condition (i.e., is less than a preset distance threshold), it indicates that the candidate peak and the first coarse localization center have a high spatial correlation and can be determined as repeated observations of the same target. At this point, the candidate peak corresponding to the geometric distance is removed from the candidate peak set, and a new candidate peak set is generated. Based on this new candidate peak set, a second fusion voting heatmap is constructed to extract the next coarse localization center. S5. The electronic device uses this second fusion voting heatmap as the new first fusion voting heatmap and repeats steps S2-S4 until a coarse localization center is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined.

[0065] The entire process effectively integrates the redundancy of multi-UAV observations through two-dimensional grid voting, and eliminates mutual interference and center overlap between multiple targets by using a sequential stripping mechanism, ensuring that the candidate regions corresponding to each coarse positioning center among multiple coarse positioning centers can still be accurately and independently delineated even in target-dense scenarios.

[0066] Optionally, the process of obtaining the candidate regions corresponding to each coarse localization center is as follows: Taking the first coarse localization center as an example, if the current implementation environment supports connected component extraction, the region of interest can be extracted in the first fusion voting heatmap where the first coarse localization center is located, based on the first coarse localization center, using a threshold connected component method (such as an eight-connected component clustering algorithm based on adaptive threshold), and the bounding rectangle of the region of interest is taken as the candidate region corresponding to the first coarse localization center; if the current implementation environment does not support connected component extraction, a fixed window centered on the first coarse localization center (such as a pixel matrix or rectangular sliding window of preset physical size) can be determined in the fusion voting heatmap, and the fixed window is taken as the candidate region corresponding to the first coarse localization center.

[0067] The entire process employs a dual-mode switching mechanism of "threshold connected component extraction" and "fixed window pruning," which not only achieves highly accurate bounding selection of the target potential space but also ensures the adaptability of the candidate region determination process under different computing power environments and the continuity of task execution.

[0068] Step 104, in The first candidate region Candidate regions Inside, according to The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first frame. Candidate regions The first The final positioning coordinates of the target to be located .

[0069] Among them, the ISAC transmit beam matrix is ​​a spatial energy distribution operator that is generated by multi-antenna array shaping technology and simultaneously carries communication information flow and radar sensing sequence.

[0070] The final positioning coordinates refer to the coordinates obtained after nonlinear optimization and residual correction, which characterize the first... The high-precision two-dimensional position of a target to be located in geospace is a fine positioning center / fine positioning result.

[0071] It should be noted that the first The ISAC signals transmitted by the drones within the joint frame are as follows: The ISAC signal The expression is: ,in, Indicates the first The joint beam matrix of a UAV, the joint beam matrix The expression is: , Indicates facing the first Communication beams for individual communication users This refers to a sensing beam designed for sensing tasks. This represents the baseband signal vector composed of various communication symbols and sensing symbols.

[0072] In some embodiments, electronic devices The first candidate region Candidate regions Inside, according to The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first frame. Candidate regions The first The final positioning coordinates of the target to be located This may include: electronic devices according to Communication performance indicators for each communication user and Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the determination Each UAV has its own corresponding ISAC transmission beam matrix; this electronic device is for... Multi-frame echo accumulation is performed using the multiplexed reference sequence of the ISAC transmit beam matrix to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation The electronic device is based on the effective ranging measurement. Combination of distance measurement standard deviation , and passed The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located .

[0073] Among them, communication performance indicators It is a method for quantifying drone swarms (i.e. (A drone) provides ground users with comprehensive quantitative data on the quality of wireless communication services.

[0074] Perceived performance indicators It is a gain metric used to evaluate the ability of a drone swarm to detect the spatial location of a target.

[0075] The joint beamforming function for communication and sensing is a multi-objective optimization mathematical model designed to maximize the utilization of system resources.

[0076] The ISAC transmit beam matrix is ​​a composite spatial filtering weight matrix that integrates communication and sensing functions, generated under an optimal resource allocation scheme.

[0077] In this embodiment of the application, the electronic device is based on Communication performance indicators for each communication user and Perception performance indicators for each target to be located Therefore, based on this communication performance index and the perception performance index Based on this, a joint beamforming function for communication and sensing is constructed. This is achieved by iterating through a pre-defined one-dimensional communication power ratio to search for variables. ,Sure Each drone has its own corresponding ISAC transmit beam matrix; then, the electronic device... Multi-frame echo accumulation is performed using the multiplexed reference sequence of the ISAC transmit beam matrix to determine the first... Effective ranging for each target to be located And determine the effective distance measurement. Corresponding standard deviation combination of distance measurement Subsequently, the electronic device acquired the first The target to be located is in the first Candidate regions Initial position estimate within Furthermore, combined with the effective ranging quantity Combination of distance measurement standard deviation Determine the first The final positioning coordinates of the target to be located .

[0078] The entire process achieves optimal synergistic configuration of communication and sensing performance through one-dimensional power ratio search. Combined with multi-frame signal accumulation and spatial consistency verification, it significantly improves the anti-interference capability and solution accuracy of multi-target localization while ensuring communication service quality. The final positioning coordinates of each target to be located are provided, and all the final positioning coordinates are relatively accurate.

[0079] In some embodiments, the first The sensing beam corresponding to each drone The construction process of the electronic device is as follows: the electronic device is based on the first Candidate regions , construct the first A drone targeting Channel matrix of communication users and facing Target orientation matrix of targets to be located The electronic device is based on the channel matrix. Construct the communication user subspace projection matrix The electronic device is based on the target guidance matrix. Construct the target direction projection matrix The electronic device is based on the communication user subspace projection matrix. and target direction projection matrix Construct the orthogonal complementary projection matrix The electronic device projects a matrix based on the target direction. and orthogonal complementary projection matrix , construct the first The sensing beam corresponding to each drone .

[0080] In the embodiments of this application, in the first The sensing beam corresponding to each drone During the construction process, electronic devices can be based on the first Candidate regions The first The drone to the first The relative positions of individual communication users determine the azimuth angle. and construct the first The drone is aimed at the first Communication guidance vector of individual communication users Then, the electronic device follows the communication guidance vector. and free space path loss coefficient Determine the first The drone to the first Channel vectors of communication users The free space path loss coefficient The calculation formula is: The channel vector The calculation formula is: Based on this, the electronic device can determine the first... A drone targeting Each communication user's channel vector is used to construct the channel matrix. The channel matrix The expression is: .

[0081] At the same time, the electronic device can, according to the first Candidate regions The first The drone to the first The relative positional relationship between the coarse positioning centers of each target to be located is used to determine the azimuth angle. and construct the first The drone is aimed at the first Target orientation vector of the target to be located Based on this, the electronic device can determine the first... A drone targeting The target orientation vectors corresponding to the coarse localization centers of each target to be located are used to construct the target orientation matrix. The target orientation matrix The expression is: .

[0082] Subsequently, the electronic device employs the regularized minimum mean square error method. Initial communication beam matrix of the UAV , ,in For regularization parameters, Represent the identity matrix, and then... After normalizing each column, the communication beam matrix is ​​obtained. .

[0083] Furthermore, this electronic device is used to construct sensing beams. According to the channel matrix Construct the communication user subspace projection matrix The communication user subspace projection matrix The calculation formula is: Simultaneously, the electronic device operates according to the target guidance matrix. Construct the target direction projection matrix The projection matrix of the target direction The calculation formula is: Subsequently, the electronic device projects the communication user subspace matrix. and the projection matrix of the target direction Construct the orthogonal complementary projection matrix The orthogonal complementary projection matrix The calculation formula is: , Represents the identity matrix; the electronic device projects the matrix according to the target direction. and orthogonal complementary projection matrix , construct the first The sensing beam corresponding to each drone .

[0084] Let the target direction superposition vector be... The target direction superimposed vector The calculation formula is: At this time, the first The initial vector of the sensing beam corresponding to each UAV is: The initial vector of the sensing beam The calculation formula is: Finally, the electronic device initializes the sensing beam's initial vector. After normalization, we obtain the first... The sensing beam corresponding to each drone .

[0085] Based on this, the electronic device can determine Each drone has its own corresponding sensing beam.

[0086] The entire process accurately guides the sensed energy to the target space by constructing an orthogonal complementary projection matrix, while forming a deep null in the direction of the communication user. This physically suppresses spatial interference between the sensing signals and significantly improves the signal-to-noise ratio and reliability of multi-target positioning in complex low-altitude environments.

[0087] In some embodiments, the electronic device according to Communication performance indicators for each communication user and Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the determination The ISAC transmit beam matrix corresponding to each drone can include: for The first of the communication users The communication user and the first One communication user, Electronic devices according to The drone is aimed at the first The channel vector and communication beam for the first communication user, oriented towards the first The communication beams of each communication user, and The sensing beam corresponding to each of the drones is used to determine the first... The signal-to-interference-plus-noise ratio of collaborative communication among individual communication users The electronic device is based on The signal-to-interference-plus-noise ratio and first weight of each communication user's collaborative communication are determined. Communication performance indicators for each communication user ; Regarding the first Candidate regions The electronic device is based on the first Candidate regions Inner The drone to the first The distance between the targets to be located , No. The drone for the first Normalized sensing gain of each target to be located , and passed Sensing power of a drone Determine the first The drone for the first Sensing signal-to-noise ratio of a target to be located The electronic device is based on Each of the drones is for the first The perceived signal-to-noise ratio of the nth target to be located is used to determine the nth target. Fusion sensing signal-to-noise ratio of individual targets to be located ; and according to The fusion sensing signal-to-noise ratio and second weight of each target to be located are determined. Perception performance indicators for each target to be located The electronic device is based on communication performance indicators. and perceived performance indicators A joint beamforming function for communication and sensing is constructed, and the optimal power allocation ratio corresponding to the joint beamforming function for communication and sensing is determined. ,Sure Each UAV has its own corresponding ISAC transmission beam matrix.

[0088] In this embodiment, the electronic device can perform multi-UAV sensing joint beamforming and communication power based on the joint beamforming problem of communication and sensing. and sensing power The allocation is determined under the conditions of satisfying the communication service quality constraints and perception performance constraints. The ISAC transmit beam matrix for each UAV is defined as follows: To balance communication and sensing performance, a one-dimensional communication power ratio search variable can be set as... This can also be called the power distribution ratio; the first The communication power of each drone is The communication power is The calculation formula is: ,in, Indicates the first The maximum transmit power of the first drone; Sensing power of a drone The sensing power The calculation formula is: .

[0089] For different power allocation ratios Perform a traversal. (Targeting...) The first of the communication users The communication user and the first One communication user, Electronic devices according to The drone is aimed at the first The conjugate transpose of the channel vectors of each communication user and communication beam Oriented towards the first Communication beams of individual communication users ,and The sensing beam corresponding to each of the drones is used to determine the first... The signal-to-interference-plus-noise ratio of collaborative communication among individual communication users The signal-to-interference-to-noise ratio of this collaborative communication The calculation formula is: ,in, This indicates the received noise power during communication. Furthermore, the... The communication rate corresponding to each communication user is: This communication rate The calculation formula is: .

[0090] Based on this, the electronic device can determine The signal-to-interference-plus-noise ratio of each communication user's collaborative communication, and then combined with... The first weight of each communication user is determined. Communication performance indicators for each communication user This communication performance indicator The calculation formula is: ,in, Indicates the first The first weight of each communication user.

[0091] Regarding the first Candidate regions Electronic devices obtain the first The drone to the first The distance between coarse positioning points corresponding to each target to be located that distance The calculation formula is: At the same time, the electronic device first acquires the first The drone for the first Normalized sensing gain of each target to be located The normalized sensing gain The calculation formula is: Then, the electronic device determines the distance based on that distance. The normalized perceived gain and the Sensing power of a drone Determine the first The drone for the first Sensing signal-to-noise ratio of a target to be located The perceived signal-to-noise ratio The calculation formula is: ,in, This represents the sensing integration constant. Based on this, the electronic device can determine... Each of the drones is for the first The perceived signal-to-noise ratio of the nth target to be located is used to determine the nth target. Fusion sensing signal-to-noise ratio of individual targets to be located The fusion sensing signal-to-noise ratio The calculation formula is: .

[0092] Next, the electronic device according to The fusion sensing signal-to-noise ratio and second weight of each target to be located are determined. Perception performance indicators for each target to be located This perceived performance index The calculation formula is: ,in, Indicates the first The second weight of the target to be located.

[0093] Subsequently, the electronic device was configured according to the aforementioned communication performance indicators. and the aforementioned perception performance indicators A joint beamforming function for communication and sensing is constructed, and its expression is as follows: ,in, Indicates a joint indicator of communication and sensing. This represents a trade-off coefficient between communication performance and sensing performance. Under the condition of satisfying the perception threshold constraint, Among the candidate power allocation ratios, the one that best combines communication and sensing metrics is selected. The corresponding optimal power allocation ratio .

[0094] Finally, the electronic device is configured according to the optimal power allocation ratio. Determine the first UAV communication sub-beam matrix for a single drone and sensing beam The UAV communication sub-beam matrix The calculation formula is: The sensing beam The calculation formula is: Based on this, the electronic device can determine the first... The corresponding ISAC transmit beam matrix for each UAV The ISAC transmit beam matrix The expression is: , It also represents the communication beam matrix after power allocation.

[0095] Based on this, the electronic device can determine the above. Each UAV has its own corresponding ISAC transmission beam matrix.

[0096] The entire process, by introducing a dynamic power allocation factor and a joint objective function, achieves weighted collaborative optimization of communication rate and perceived signal-to-noise ratio, ensuring that while meeting the basic communication service quality, it maximizes the energy gain of multi-target detection and achieves Pareto optimal allocation of system resources.

[0097] In some embodiments, the electronic device Multi-frame echo accumulation is performed using the multiplexed reference sequence of the ISAC transmit beam matrix to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation This can include: targeting The first of the drones The drone, electronic equipment acquires the first The drone in the first Second received echo signal under frame Second received echo signal Include The echo superposition term and noise term of the target to be located; for the second received echo signal Perform matched filtering to obtain the second matched filter output signal; according to The second matched filter output signal corresponding to each frame determines the first frame. The electronic device in the second distance image corresponding to the drone; Candidate regions Within the corresponding geometric threshold window, peak detection is performed on the second distance image to obtain the first... The electronic device uses a set of candidate ranging values ​​corresponding to each drone; Among the drones, the one participating in the positioning was obtained. A collection of drones targeting a specific location ; and assemble drones The electronic device takes a candidate ranging value from the candidate ranging value set corresponding to each UAV as a ranging combination, and obtains the ranging standard deviation combination corresponding to the ranging combination. The electronic device then obtains the consistency cost corresponding to each of the multiple ranging combinations, and takes the ranging combination with the minimum consistency cost as the first... Effective ranging for each target to be located The set of standard deviations of the distance measurement corresponding to the minimum value is used as the first... The combination of distance standard deviations corresponding to each target to be located .

[0098] In the embodiments of this application, for The first of the drones The drone, in the When a UAV performs multi-frame repeated transmission and echo reception using a pre-configured reference sequence, let the slow time frame index be... At this time, the electronic device acquires the first The drone in the first Second received echo signal under frame It includes echo superposition terms and noise terms for all targets to be located; if the cumulative frame count is Then the second received echo signal The calculation formula is: ,in, Indicates the first The first frame The complex scattering coefficients corresponding to each target to be located. Indicates the first Complex Gaussian noise in the frame. Then, the electronic device receives the second echo signal. A matched filter is performed to obtain a second matched filter output signal. Based on this, the electronic device can determine... The second matched filter output signal corresponding to each frame is used to determine the first frame. The second distance image corresponding to each drone.

[0099] At the same time, regarding the first The location of the target to be located is the first Candidate regions Electronic devices can calculate the first The drone arrived at the first Candidate regions The minimum distance is The maximum distance is The electronic device will have a minimum distance of and the maximum distance This serves as the upper and lower bounds for the distance, and a preset distance margin is added to these upper and lower bounds. To construct geometric threshold windows Among them, the preset distance margin This can be determined based on the distance resolution, for example, by taking a value that is not less than a preset constant and not less than a certain multiple of the distance resolution. Only within this geometric threshold window... Candidate peak extraction is performed within the corresponding distance range to effectively reduce the impact of irrelevant peaks and spurious peaks on the ranging results.

[0100] Subsequently, the electronic device in the geometric threshold window Within the second distance image, peak detection is performed. Multiple candidate peaks can be extracted by sorting them according to their peak intensity, and a preset number of candidate peaks are retained by combining the minimum inter-peak spacing constraint. For the extracted candidate peak positions, parabolic interpolation can be used for refinement to obtain more precise candidate ranging values. Based on this, the electronic device can determine the first... The set of candidate ranging values ​​corresponding to each drone. It should be noted that if the current geometric threshold window... If no valid peak is detected, the peak detection threshold can be lowered or the geometric threshold window can be appropriately expanded to re-extract the peak. If no valid peak is still detected, the predicted distance based on the coarse positioning center can be used as the compensation ranging value (i.e., the candidate ranging value).

[0101] Then, the electronic device from Among the drones, the one participating in the positioning was obtained. A collection of drones targeting a specific location The drone collection The number of drones in the system is at least three; specifically, the drone ensemble... The number of drones in the distance is the distance to the first The electronic device then locates at least three of the nearest targets from the drone ensemble. The process iterates through the candidate ranging value set corresponding to each UAV, determines a set of ranging combinations, and then repeats the process of determining ranging combinations to obtain multiple sets of ranging combinations. Simultaneously, it obtains the standard deviation of the ranging values ​​for each of these multiple sets of ranging combinations. It should be noted that for the UAV set... For each UAV, the candidate ranging value can be determined based on its equivalent peak signal-to-noise ratio and the magnitude of the ranging value. The standard deviation of the ranging value can be determined, and this standard deviation must not be less than a preset lower limit. Then, the UAVs are aggregated... The standard deviations of the candidate ranging values ​​corresponding to each UAV constitute a set of standard deviations. For any given ranging set, its corresponding set of standard deviations is formed by sequentially arranging the standard deviations of the candidate ranging values ​​in that set.

[0102] In order to From the candidate ranging values ​​extracted by the UAV, a set of valid ranging values ​​that are consistent with each other can be determined. A combined consistency decision method can be used. Specifically, the electronic device determines the consistency cost corresponding to each of the above multiple ranging combinations. This consistency cost The residual level of the current ranging combination in spatial geometry, i.e., the sum of normalized deviations between each observation and the temporary positioning center point, is used to characterize the degree of matching or physical consistency between candidate ranging values ​​and target spatial geometric constraints. This consistency cost The calculation formula is: ,in, This indicates the drone collection The number of drones in the country This represents the provisional position estimate obtained from the current range measurement combination. Represents a collection of drones The first in The location of the drones involved in the positioning. Indicates the corresponding number in the current candidate ranging combination. The first target to be located One candidate ranging value, Indicates the corresponding number in the current candidate ranging combination. The first target to be located Candidate ranging values The corresponding standard deviation of the distance measurement is used to characterize the uncertainty of the candidate distance measurement value. The smaller the value, the higher the confidence level of the candidate ranging value.

[0103] Then, the electronic device selects the minimum value from all consistency costs and uses the set of ranging combinations corresponding to the minimum value as the first... Effective ranging for each target to be located This effective distance measurement The expression is: At the same time, the set of distance measurement standard deviations corresponding to the minimum value is taken as the first... The combination of distance standard deviations corresponding to each target to be located This combination of distance measurement standard deviations The expression is: This provides reliable ranging input for the subsequent precise positioning process.

[0104] The entire process effectively suppressed false peak interference in complex low-altitude scenarios through the dual constraints of multi-frame echo accumulation and geometric threshold window, and used the consistency cost function to realize the spatial self-consistency verification of multi-machine ranging, thus providing high-confidence and physically consistent observation input for the precise positioning process.

[0105] Optionally, the electronic device according to The second matched filter output signal corresponding to each frame determines the first frame. The second range image corresponding to each UAV can include: in the case of coherent accumulation, the range image of the electronic equipment. The second matched filter output signals corresponding to each frame are accumulated in the complex domain, and then the amplitude is squared to obtain the first frame. The accumulated range image power corresponding to each UAV, i.e., the second range image; in the case of incoherent accumulation, the electronic equipment for... The power of the second matched filter output signal corresponding to each frame is directly accumulated to obtain the accumulated range image power. The entire process, through multi-frame accumulation, can improve the detectability of target peaks and the stability of ranging.

[0106] In some embodiments, the electronic device is based on the effective ranging measurement. Combination of distance measurement standard deviation , and passed The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located This may include: S1, the electronic device will... The target to be located is in the first Candidate regions The coarse positioning center within is used as the initial position estimate. S2, The electronic device is based on the initial position estimate. and effective distance measurement Construct the Jacobi matrix , Indicates the first The estimated location of each target to be located. S3 indicates the current iteration number; the electronic device is based on the Huber robustness parameter. Determine the effective distance measurement Robust weights corresponding to each candidate distance value; and a combination of all robust weights and the distance measurement standard deviation. Construct a weighted matrix S4. This electronic device is based on the Jacobi matrix. and weighted matrix Determine the first The position increment corresponding to each target to be located S5. The electronic device adjusts its position increments accordingly. and initial position estimate Determine the first The final positioning coordinates of the target to be located .

[0107] In this embodiment of the application, when determining the first The final positioning coordinates of the target to be located During the process, S1, for the first The electronic device will locate the first target. The target to be located is in the first Candidate regions The coarse positioning center within is used as the initial position estimate. S2, The electronic device uses effective ranging. As an observation, construct based on effective distance measurement. The weighted least squares localization model, the first of which is the weighted least squares localization model. Each residual term is The residual term Second-rate The calculation formula is: Then construct the Jacobi matrix. , where the Jacobi matrix The Middle The row corresponds to the first Each effective ranging residual term Regarding the first Target position estimation in the second iteration The vector composed of the partial derivatives is denoted as The partial derivative The calculation formula is: ,in, Indicates the first Target position estimation in the next iteration This represents the x-coordinate component of the target's estimated location. This represents the ordinate component of the target's estimated location. Indicates the current iteration position up to the nth iteration. The geometric distance of each drone, based on which the Jacobian matrix is ​​calculated. The expression is: Based on the standard deviation of each effective distance measurement, a basic weighted term can be constructed first.

[0108] Furthermore, to reduce the impact of outlier ranging on the positioning results (i.e., the final positioning coordinates) The influence of this can be mitigated by introducing Huber robustness parameters. S3, for the first For each residual, first define the normalized residual as... The normalized residual The calculation formula is: ,in, This represents an extremely small positive number to prevent the denominator from being zero. At this point, the effective distance measurement... The Middle The robust weights corresponding to the candidate ranging values ​​are: The robust weight The calculation formula is: Based on this, the electronic device can determine the effective ranging value. The robust weights corresponding to each candidate distance value are determined; then, the electronic device combines all robust weights with the ranging standard deviation. Construct a weighted matrix The weighted matrix The expression is: ,in, S4. This electronic device uses the Levenberg-Marquardt (LM) iterative algorithm to update the target position estimate (i.e., the final positioning coordinates). Specifically, first based on the Jacobi matrix and weighted matrix Determine the first The position increment corresponding to each target to be located Increment at this position The calculation formula is: ,in, Indicates the first The damping coefficient of the next iteration; then, in S5, it is combined with the initial position estimate. Determine the first The final positioning coordinates of the target to be located .

[0109] The entire process uses the coarse positioning point as the initial value. By introducing Huber robust weights, outlier ranging interference is effectively suppressed. Combined with the LM iterative algorithm, a highly reliable solution for the nonlinear positioning model is achieved, ensuring that high-precision final positioning coordinates can still be obtained in complex observation environments.

[0110] In some embodiments, the electronic device adjusts the position increment. and initial position estimate Determine the first The final positioning coordinates of the target to be located This may include: S51, electronic devices based on location increments and initial position estimate Determine the first S52, The electronic device reprojects the initial positioning coordinates back to the target to be located; Candidate regions The interior is used as a new initial position estimate. S53. The electronic device repeats steps S2-S52 until the latest position increment is less than a preset threshold, and uses the initial positioning coordinates determined based on the latest position increment as the first... The final positioning coordinates of the target to be located .

[0111] In this embodiment of the application, during the process of updating the target location estimate, S51, the electronic device updates the target location estimate based on the location increment. and step size coefficient The product of the initial position estimate and the initial position estimate. Add them together to determine the first one. The initial positioning coordinates corresponding to the target to be located; S52, to avoid the iteration results deviating from the above-mentioned... Candidate regions After each iteration update, the electronic device reprojects the initial positioning coordinates back to the previous one. Candidate regions The projected initial positioning coordinates are then used as the new initial position estimate. S53. To characterize the fitting error of the current iteration, the weighted root mean square value of the residuals can also be calculated. The electronic device repeats the above steps S2-S52 until the latest position increment is less than a preset threshold, i.e. Stop iteration, where, This represents the convergence threshold for the positioning iteration; then, the electronic device will use the initial positioning coordinates determined based on the latest position increment as the first... The final positioning coordinates of the target to be located The final positioning coordinates The expression is: Thus, the electronic device can ultimately determine the above. The final positioning coordinates of each target to be located.

[0112] Based on this, the update formula for the target location estimate is: It should be noted that when the position increment magnitude exceeds the preset increment threshold, the step size coefficient can be scaled to improve iteration stability.

[0113] The entire process effectively prevents numerical divergence and boundary overflow problems in nonlinear iteration by introducing step size scaling and spatial projection constraints. It also uses position increment threshold to determine iteration convergence and combines weighted residuals to characterize fitting errors, thereby achieving stable solutions for accurate positioning coordinates and ensuring the continuity and reliability of positioning output in complex low-altitude environments.

[0114] Optionally, the damping coefficient The damping coefficient can be adaptively adjusted based on the magnitude of the position increment during the iteration process. Specifically, when the position increment is large, the damping coefficient is increased. To improve iterative stability; reduce the damping coefficient when the position increment is small. This is to improve the local convergence speed.

[0115] Optionally, after step 104, the method may further include: electronic device output. The final location coordinates of each target to be located are output, and communication performance indicators are also output. Perceived performance indicators And positioning performance results.

[0116] The positioning performance result refers to the multi-dimensional quantitative evaluation of the reliability and accuracy of each final positioning coordinate. Optionally, the positioning performance result may include at least: including but not limited to: root mean square error of positioning, positioning confidence, Cramer-Rhodes lower bound, and geometric precision factor reflecting the influence of spatial topology.

[0117] The entire process outputs high-precision final coordinates and simultaneously quantifies sensory indicators and positioning performance, enabling comprehensive monitoring and multi-dimensional evaluation of operational quality. This not only verifies the reliability of the positioning results but also provides a basis for decision-making regarding the subsequent flight path planning and resource scheduling of the UAV swarm.

[0118] Through steps 101-104 above, the electronic device can organically combine multi-UAV beam scanning, candidate region reduction, joint beam design of communication and sensing, multi-frame sensing echo processing, and robust precision positioning with constraints, making it particularly suitable for multi-target positioning and multi-user communication collaboration scenarios in complex low-altitude environments.

[0119] Furthermore, by incorporating multi-UAV beam scanning, candidate region extraction, joint beamforming, and constrained fine localization into a single processing framework, continuous processing from coarse to fine localization can be achieved in complex low-altitude scenarios. Specifically: in the coarse localization stage, the target search range is narrowed using a fused voting heatmap of multi-UAV scan echoes, reducing the search complexity of subsequent fine localization; in the joint beamforming stage, communication user service quality and target perception performance are considered uniformly, improving the efficiency of collaborative utilization of communication and perception resources; in the fine localization stage, iterative localization through multi-frame accumulation, candidate peak combination consistency selection, and candidate region constraints can effectively suppress false peak interference and erroneous associations, improving target localization accuracy and robustness. Through steps 101-104 above, the aforementioned drawbacks 1-4 can be resolved.

[0120] In this embodiment of the application, the technical solution described in steps 101-104 above utilizes ISAC technology to break the traditional multi-target positioning architecture. By using multi-UAV collaborative beam scanning and a two-dimensional fusion voting mechanism, it solves the problems of signal ambiguity and multipath interference in low-altitude target positioning scenarios. Furthermore, by using the ISAC transmission beam matrices of each UAV, it dynamically balances communication interference and sensing gain. The aim is to achieve high-precision continuous positioning of multiple targets in complex low-altitude environments without increasing additional hardware costs, thus providing reliable underlying support for low-altitude intelligent networks.

[0121] To better understand the embodiments of this application, a simulation experiment is conducted on the multi-UAV cooperative localization method in low-altitude scenarios provided by the embodiments of this application, and the simulation results are described in detail below: Figure 3 This is a 3D scene diagram of the ISAC system model provided in the embodiments of this application. For example... Figure 3 As shown, the 3D scene includes one main UAV, three auxiliary UAVs, multiple communication users, and two targets to be located. The main UAV and auxiliary UAVs together constitute an aerial collaborative sensing platform, the communication users are distributed across the ground area, and the targets to be located are situated at the corresponding ground location in the low-altitude scene. Figure 3 In the diagram, the red pentagram represents the main UAV, the red dot represents the auxiliary UAV, the cyan inverted triangle represents the communication user, the green square represents the actual target location (i.e., the target to be located), the blue triangle represents the coarse positioning result (i.e., the coarse positioning center), and the purple diamond represents the fine positioning result (i.e., the fine positioning center). The cyan dashed line represents the communication link between the main UAV and the ground communication user, the green dotted line represents the perception link between the UAV and the target to be located, and the blue dashed box represents the boundary of the candidate area extracted from the coarse positioning stage. Figure 3 The bottom ground region shown uses color depth to describe the spatial distribution intensity of the coarse localization stage fusion voting heatmap, with color bars reflecting the trend of detection intensity from low to high. Darker colors indicate higher support for the corresponding region after multi-UAV scanning beams, range peak detection, and 2D grid voting fusion; lighter colors indicate a lower probability of target presence in the candidate region. Figure 3 As can be seen, distinct high-intensity regions were formed near both targets to be located, indicating that this application, through multi-UAV scanning, matched filtering, peak detection, and voting fusion, can effectively filter out the area where the target is located from a large ground area in a 3D scene, and form a coarse positioning center and candidate area adjacent to the actual target location. Simultaneously, the communication link and sensing link are displayed in the same 3D scene, demonstrating that this application, within the framework of a unified ISAC system model, balances communication coverage and multi-target positioning capabilities, achieving integrated sensing and communication collaborative operation.

[0122] Figure 4 This is a one-dimensional power allocation search result diagram provided in an embodiment of this application. For example... Figure 4 As shown, the horizontal axis represents the proportion of communication power η, and the vertical axis represents the value of the joint objective function. Figure 4 The blue curve in the graph represents the search results of the joint objective function as the communication power ratio changes; the red star marks the final selected optimal operating point; and the red dashed line indicates the location of the optimal communication power ratio. From... Figure 4 As can be seen, the objective function value generally increases with the gradual increase of the communication power ratio. This indicates that within the feasible range of satisfying the sensing constraints, increasing the communication power ratio helps improve communication performance, thereby increasing the objective function value. However, the communication power ratio cannot be increased indefinitely; as the communication power continues to increase, the sensing power will decrease accordingly, which may lead to the target sensing signal-to-noise ratio failing to meet the preset sensing threshold. Therefore, this application does not simply take the rightmost endpoint corresponding to the maximum value of the objective function, but rather selects the largest possible communication power ratio within the feasible region under the premise of "satisfying the sensing threshold constraint," so that the electronic device can improve communication quality as much as possible while ensuring sensing performance. Figure 4 The results show that the optimal communication power ratio is approximately η=0.83, which means that the electronic device allocates about 83% of the transmission power to the communication link and uses the remaining power for the sensing link, thus achieving an effective balance between communication performance and sensing performance.

[0123] Figure 5 This is a schematic diagram of the LM iterative convergence curve provided in an embodiment of this application. For example... Figure 5 As shown, the horizontal axis represents the number of iterations, i.e., the number of steps the LM iterative algorithm performs recursive optimization, and the vertical axis represents the root mean square of the weighted residuals, in meters (m), used to characterize the overall deviation between the current estimated position and each effective distance measurement. From Figure 5 As can be seen, based on the technical solutions described in steps 101-104, the target position estimation can reach the convergence threshold (i.e., the positioning iteration convergence threshold) within a very short iteration cycle (e.g., 3-5 times). Even if there is a deviation of several meters in the initial position, the LM iterative algorithm can still correct the positioning accuracy to the decimeter level or even better through the rapid decay of the weighted residual. In addition, the two curves eventually tend to stabilize, proving the robustness of the LM iterative algorithm combined with robust weights in dealing with nonlinear multi-target localization problems.

[0124] Figure 6 This is a schematic diagram of the fine positioning results provided in an embodiment of this application. In the diagram, green squares represent the actual target location, blue triangles represent coarse positioning results, purple rhombuses represent fine positioning results, red pentagrams represent the main UAV, and red hollow circles represent auxiliary UAVs. Figure 6It is clear that for two targets to be located, the coarse positioning result can constrain the target positions to a relatively small range, while the fine positioning result further approximates the true target positions. Specifically, the fine positioning error of target 1 is approximately 0.52m, and the fine positioning error of target 2 is approximately 0.37m. This indicates that this application, through candidate region constraints, consistency screening of candidate peaks from multiple UAVs, and fine iterative optimization using LM, can achieve sub-meter level positioning accuracy in complex low-altitude scenarios. Meanwhile, from... Figure 6 It can also be seen that the fine positioning results are significantly closer to the actual target location than the coarse positioning results, indicating that the two-stage positioning mechanism of "coarse positioning-candidate region constraint-fine positioning" proposed in this application can significantly improve the final positioning accuracy.

[0125] In summary, this application provides a multi-UAV ISAC localization method for low-altitude scenarios. This method obtains candidate regions through multi-UAV scanning, improves communication and sensing coordination through joint beamforming, enhances effective ranging reliability through multi-frame accumulation and candidate peak combination consistency, and improves precise positioning accuracy through robust weighted least squares iteration under candidate region constraints. It has significant engineering application value and addresses the aforementioned drawbacks 1-4.

[0126] The following describes the multi-UAV cooperative positioning system in low-altitude scenarios provided in the embodiments of this application. The multi-UAV cooperative positioning system in low-altitude scenarios described below can be referred to in correspondence with the multi-UAV cooperative positioning method in low-altitude scenarios described above.

[0127] Figure 7 This is a schematic diagram of the structure of a multi-UAV cooperative positioning system in a low-altitude scenario provided in an embodiment of this application. Figure 7 As shown, the system includes: a model building module 701, a coarse positioning module 702, and a fine positioning module 703.

[0128] Model building module 701 is used to construct a multi-UAV cooperative communication and perception integrated ISAC system model based on the system's initial parameters in a low-altitude target localization scenario. These initial parameters include at least... A drone and One target to be located. It is an integer greater than 2. It is an integer greater than 1; The coarse positioning module 702 is used to control the multi-UAV ISAC system model based on the model. A drone Perform a sensor scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; Precision positioning module 703, used in The first candidate region Candidate regions Inside, according to this The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in that position. Candidate regions The first The final positioning coordinates of the target to be located .

[0129] Optionally, the initial parameters of the system also include One communication user, For integers greater than 1, the precision positioning module 703 is specifically used to determine the position based on the given information. Communication performance indicators for each communication user and the Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. Each UAV has its own corresponding ISAC transmission beam matrix; for The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first ISAC transmit beam matrix. Effective ranging for each target to be located Combination of distance measurement standard deviation Based on this effective distance measurement Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located .

[0130] Optionally, the coarse positioning module 702 is specifically used for the following... The first of the drones The drone, for the first One drone configuration One scanning beam, It is an integer greater than 1; for this The first of the scanning beams One scanning beam, to acquire the The targets to be located are respectively in the first... The first drone Received echo power under each scanning beam; according to The complex scattering coefficients corresponding to each received echo power determine the first... The drone in the first The first received echo signal under each scanning beam ; Regarding the first received echo signal Perform matched filtering to obtain the first matched filter output signal. ;according to The first matched filter output signal is used to determine the first... The first range image corresponding to each UAV; peak detection is performed on the first range image to obtain a preset number of candidate peaks and use them as the first UAV. The scan results for each drone.

[0131] Optionally, the coarse positioning module 702 is specifically used in S1 to... One drone, the Each drone Each scanning beam and the S2. Accumulate the scan results to construct a first fused voting heatmap; S3. Select the coordinate position corresponding to the global maximum value in the first fused voting heatmap as the first coarse localization center; S4. Separate the candidate peaks corresponding to each UAV from the first coarse positioning center using geometric distances that meet the consistency condition. Then, construct a second fused voting heatmap based on the remaining candidate peaks. S5. Use this second fused voting heatmap as the new first fused voting heatmap and repeat steps S2-S4 until the first fused voting heatmap is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined.

[0132] Optionally, the precision positioning module 703 is specifically used for this... The first of the communication users The communication user and the first One communication user, According to this The drone was aimed at the first The channel vector and communication beam for the first communication user, oriented towards the first... The communication beam of each communication user, and the The sensing beam corresponding to each drone is used to determine the first drone. The signal-to-interference-plus-noise ratio of collaborative communication among individual communication users According to this The signal-to-interference-plus-noise ratio and first weight of each communication user's collaborative communication are used to determine the... Communication performance indicators for each communication user Regarding the first Candidate regions According to the first Candidate regions The innermost The drone arrived at the first The distance between the targets to be located The first The drone for this first Normalized sensing gain of each target to be located , and the first Sensing power of a drone Determine the first The drone for this first Sensing signal-to-noise ratio of a target to be located According to this Each drone is responsible for the first... The perceived signal-to-noise ratio of the first target to be located is used to determine the first target. Fusion sensing signal-to-noise ratio of individual targets to be located ; and according to this The fusion sensing signal-to-noise ratio and second weight of each target to be located are used to determine the target. Perception performance indicators for each target to be located According to this communication performance index and the perception performance index The joint beamforming function for communication and sensing is constructed, and the optimal power allocation ratio corresponding to the joint beamforming function is determined. Determine the Each UAV has its own corresponding ISAC transmission beam matrix.

[0133] Optionally, the precision positioning module 703 is specifically used to, according to the first Candidate regions , construct the first A drone was directed at this Channel matrix of communication users and facing that Target orientation matrix of targets to be located According to the channel matrix Construct the communication user subspace projection matrix ; and based on the target guidance matrix Construct the target direction projection matrix According to the communication user subspace projection matrix and the projection matrix of the target direction Construct the orthogonal complementary projection matrix According to the projection matrix of the target direction and the orthogonal complementary projection matrix , construct the first The sensing beam corresponding to each drone .

[0134] Optionally, the precision positioning module 703 is specifically used for this... The first of the drones The drone, to obtain the first The drone in the first Second received echo signal under frame The second received echo signal Includes The echo superposition term and noise term of the target to be located; the second received echo signal Perform matched filtering to obtain the second matched filter output signal; according to The second matched filter output signal corresponding to each frame determines the third frame. The second distance image corresponding to the drone; in the first Candidate regions Within the corresponding geometric threshold window, peak detection is performed on the second distance image to obtain the second... The set of candidate ranging values ​​corresponding to each UAV; from this Among the drones, the one participating in the positioning was obtained. A collection of drones targeting a specific location ; and the drones were assembled For each UAV, a candidate ranging value from the candidate ranging value set is taken as a ranging combination. Simultaneously, the standard deviation of the ranging combination is obtained. The consistency cost of each of the multiple ranging combinations is obtained, and the ranging combination with the minimum consistency cost among the multiple consistency costs is taken as the first... Effective ranging for each target to be located The set of standard deviations of the distance measurement corresponding to the minimum value is used as the first... The combination of distance standard deviations corresponding to each target to be located .

[0135] Optionally, the precision positioning module 703 is specifically used in S1 to position the first... The target to be located is in the first Candidate regions The coarse positioning center within is used as the initial position estimate. S2, Based on the initial position estimate and the effective distance measurement Construct the Jacobi matrix , Indicates the first The estimated location of each target to be located. Indicates the current iteration number; S3, based on Huber robustness parameters Determine the effective distance measurement. Robust weights corresponding to each candidate distance value; and a combination of all robust weights and the standard deviation of the distance measurement. Construct a weighted matrix S4. Based on this Jacobi matrix and the weighted matrix Determine the first The position increment corresponding to each target to be located S5. Increment based on this position and the initial position estimate Determine the first The final positioning coordinates of the target to be located .

[0136] Optionally, the precision positioning module 703 is specifically used in S51 to adjust the position increment. and the initial position estimate Determine the first S52, Reproject the initial positioning coordinates of the target to be located back to the target; Candidate regions The inner part is used as the new estimate of this initial position. S53. Repeat steps S2-S52 until the latest position increment is less than a preset threshold, and use the initial positioning coordinates determined based on the latest position increment as the first... The final positioning coordinates of the target to be located .

[0137] Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute a multi-UAV cooperative localization method in a low-altitude scenario. This method includes: constructing a multi-UAV cooperative communication and perception integrated ISAC system model based on initial system parameters in the low-altitude target localization scenario. The initial system parameters include at least... A drone and One target to be located. It is an integer greater than 2. The integer is greater than 1; according to the multi-UAV ISAC system model, control the... A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; in The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located .

[0138] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multi-UAV cooperative localization method in low-altitude scenarios provided by the above methods. This method includes: constructing a multi-UAV cooperative communication and perception integrated ISAC system model based on the system initial parameters in the low-altitude target localization scenario. The system initial parameters include at least... A drone and One target to be located. It is an integer greater than 2. The integer is greater than 1; according to the multi-UAV ISAC system model, control the... A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; in The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located .

[0140] Furthermore, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, this computer program implements the multi-UAV cooperative localization method in low-altitude scenarios provided by the methods described above. This method includes: constructing a multi-UAV cooperative communication and sensing integrated ISAC system model based on initial system parameters in the low-altitude target localization scenario. The initial system parameters include at least... A drone and One target to be located. It is an integer greater than 2. The integer is greater than 1; according to the multi-UAV ISAC system model, control the... A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; in The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located .

[0141] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-UAV cooperative positioning method in low-altitude scenarios, characterized in that, include: Based on the initial system parameters in a low-altitude target localization scenario, a multi-UAV collaborative communication and sensing integrated ISAC system model is constructed. The initial system parameters include at least the following: One drone, Individual targets to be located and One communication user, It is an integer greater than 2. It is an integer greater than 1. It is an integer greater than 1; Based on the aforementioned multi-UAV cooperative communication and sensing integrated ISAC system model, control the A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results for each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponding to a target to be located; Wherein, according to Each scan result is used to construct a fusion voting heatmap. Determine the The steps for identifying the candidate regions for each target to be located include: S1, by referring to the The drone, the Each drone Each scanning beam and the aforementioned S2. Accumulate the scan results to construct a first fused voting heatmap; S3. Select the coordinate position corresponding to the global maximum value in the first fused voting heatmap as the first coarse positioning center; S4. Separate the candidate peaks corresponding to each UAV from the first coarse positioning center using geometric distances that meet the consistency condition. Then, construct a second fused voting heatmap based on the remaining candidate peaks. S5. Use the second fused voting heatmap as the new first fused voting heatmap, and repeat steps S2-S4 until the desired result is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined. exist The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located ; Among them, the one mentioned The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located The steps include: According to the above Communication performance indicators for each communication user and stated Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. Each UAV has its own corresponding ISAC transmission beam matrix; for The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation According to the effective ranging quantity Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located .

2. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 1, characterized in that, The control A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results for each drone include: Regarding the above The first of the drones The drone, for the first One drone configuration One scanning beam, It is an integer greater than 1; Regarding the above The first of the scanning beams One scanning beam, to acquire the The targets to be located are respectively in the... The first drone Received echo power under each scanning beam; according to The complex scattering coefficients corresponding to each of the received echo powers are used to determine the first... The drone in the first The first received echo signal under each scanning beam ; for the first received echo signal Perform matched filtering to obtain the first matched filter output signal. ; according to The first matched filter output signal is used to determine the first... The first distance image corresponding to each drone; Peak detection is performed on the first distance image to obtain a preset number of candidate peaks, which are then used as the first peak. The scan results for each drone.

3. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 1, characterized in that, According to the Communication performance indicators for each communication user and stated Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. The ISAC transmit beam matrices corresponding to each UAV include: Regarding the above The first of the communication users The communication user and the first One communication user, According to the above The drone is directed towards the first The channel vector and communication beam of the first communication user, facing the first The communication beams of each communication user, and the The sensing beam corresponding to each of the drones is used to determine the first... The signal-to-interference-plus-noise ratio of collaborative communication among individual communication users ; According to the above The signal-to-interference-plus-noise ratio and first weight of each communication user's collaborative communication are used to determine the... Communication performance indicators for each communication user ; Regarding the first Candidate regions According to the first Candidate regions Inner The drone to the first The distance between the targets to be located The first The drone for the first Normalized sensing gain of each target to be located , and the first Sensing power of a drone Determine the first The drone for the first Sensing signal-to-noise ratio of a target to be located ; According to the above Each of the drones is for the first... The perceived signal-to-noise ratio of the nth target to be located is used to determine the nth target. Fusion sensing signal-to-noise ratio of individual targets to be located ; and according to the above The fused sensing signal-to-noise ratio and second weight of each target to be located are used to determine the... Perception performance indicators for each target to be located ; According to the communication performance indicators and the aforementioned perception performance indicators Construct the joint beamforming function for communication and sensing, and based on the optimal power allocation ratio corresponding to the joint beamforming function for communication and sensing. Determine the Each UAV has its own corresponding ISAC transmission beam matrix.

4. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 3, characterized in that, The first The sensing beam corresponding to each drone The construction process is as follows: According to the first Candidate regions Construct the first A drone is facing the Channel matrix of communication users and facing the Target orientation matrix of targets to be located ; According to the channel matrix Construct the communication user subspace projection matrix ; And according to the target guidance matrix Construct the target direction projection matrix ; According to the communication user subspace projection matrix and the target direction projection matrix Construct the orthogonal complementary projection matrix ; According to the target direction projection matrix and the orthogonal complementary projection matrix Construct the first The sensing beam corresponding to each drone .

5. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 1, characterized in that, The pair The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation ,include: Regarding the above The first of the drones The drone, obtain the first The drone in the first Second received echo signal under frame The second received echo signal Includes the The echo superposition term and noise term of the target to be located; for the second received echo signal Perform matched filtering to obtain the second matched filter output signal; according to The second matched filter output signal corresponding to each frame determines the first... The second distance image corresponding to each drone; In the first Candidate regions Within the corresponding geometric threshold window, peak detection is performed on the second distance image to obtain the first... A set of candidate ranging values ​​corresponding to each UAV; From the above Among the drones, the one participating in the positioning is obtained. A collection of drones targeting a specific location ; and the drones were assembled One candidate ranging value from the candidate ranging value set corresponding to each UAV is taken as a ranging combination, and the ranging standard deviation combination corresponding to the ranging combination is obtained. Obtain the consistency cost corresponding to each of the multiple ranging combinations, and take the ranging combination with the minimum consistency cost as the first set. Effective ranging for each target to be located The set of distance measurement standard deviations corresponding to the minimum value is used as the first... The combination of distance standard deviations corresponding to each target to be located .

6. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 1 or 5, characterized in that, The effective ranging quantity Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located ,include: S1, the first The target to be located is in the first Candidate regions The coarse positioning center within is used as the initial position estimate. ; S2, Based on the initial position estimate and the effective ranging quantity Construct the Jacobi matrix , Indicates the first The estimated location of each target to be located. Indicates the current iteration number; S3, based on Huber robust parameters Determine the effective ranging quantity Robust weights corresponding to each candidate distance value; and a combination of all robust weights and the distance measurement standard deviation. Construct a weighted matrix ; S4. Based on the Jacobi matrix and the weighting matrix Determine the first The position increment corresponding to each target to be located ; S5. Based on the position increment and the initial position estimate Determine the first The final positioning coordinates of the target to be located .

7. The multi-UAV cooperative positioning method in low-altitude scenarios according to claim 6, characterized in that, The position increment and the initial position estimate Determine the first The final positioning coordinates of the target to be located ,include: S51, based on the position increment and the initial position estimate Determine the first The initial positioning coordinates of each target to be located; S52, Reproject the initial positioning coordinates back to the first... Candidate regions The interior is used as a new initial position estimate. ; S53. Repeat steps S2-S52 until the latest position increment is less than a preset threshold, and use the initial positioning coordinates determined based on the latest position increment as the first... The final positioning coordinates of the target to be located .

8. A multi-UAV cooperative positioning system for low-altitude scenarios, characterized in that, The system is used to implement the multi-UAV cooperative positioning method in low-altitude scenarios as described in any one of claims 1-7, including: The model building module is used to construct a multi-UAV cooperative communication and perception integrated ISAC system model based on the initial system parameters in a low-altitude target localization scenario. The initial system parameters include at least... A drone and One target to be located. It is an integer greater than 2. It is an integer greater than 1; The coarse positioning module is used to control the multi-UAV cooperative communication and perception integrated ISAC system model. A drone for the Perform a sensory scan on the target area where the target to be located is located to obtain the... The scan results corresponding to each drone; according to Each scan result is used to construct a fusion voting heatmap. Determine the Each candidate region corresponds to a target to be located; wherein, the step of... Each scan result is used to construct a fusion voting heatmap. Determine the The steps for identifying candidate regions for each target to be located include: S1, by analyzing the candidate regions corresponding to the target to be located. The drone, the Each drone Each scanning beam and the aforementioned S2. Accumulate the scan results to construct a first fused voting heatmap; S3. Select the coordinate position corresponding to the global maximum value in the first fused voting heatmap as the first coarse positioning center; S4. Separate the candidate peaks corresponding to each UAV from the first coarse positioning center using geometric distances that meet the consistency condition. Then, construct a second fused voting heatmap based on the remaining candidate peaks. S5. Use the second fused voting heatmap as the new first fused voting heatmap, and repeat steps S2-S4 until the desired result is obtained. Each target to be located has its own coarse localization center, and the candidate region corresponding to each coarse localization center is determined. The precision positioning module is used for... The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located ; wherein, the above-mentioned in The first candidate region Candidate regions Inside, according to the The ISAC transmit beam matrix corresponding to each UAV is determined by multi-frame echo accumulation and initial value correction to determine the position of the UAV in the first UAV. Candidate regions The first The final positioning coordinates of the target to be located The steps include: according to the Communication performance indicators for each communication user and stated Perception performance indicators for each target to be located By constructing a joint beamforming function for communication and sensing, the [beamforming function] is determined. Each UAV has its own corresponding ISAC transmission beam matrix; for The multiplexed reference sequence of the ISAC transmit beam matrix is ​​used to perform multi-frame echo accumulation to determine the first... Effective ranging for each target to be located Combination of distance measurement standard deviation According to the effective ranging quantity Combination of distance measurement standard deviation , and the first The target to be located is in the first Candidate regions Initial position estimate within Determine the first The final positioning coordinates of the target to be located .

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