A low-altitude fleet distributed aggregation optimization method and device for common sense enhancement

Abstract

The application provides a low-altitude aircraft group distributed aggregation optimization method and device for sensing enhancement, and relates to the technical field of sensing integration. The method comprises the following steps: establishing a sensing integration system model; establishing a probabilistic line-of-sight and non-line-of-sight channel model, a forward sensing link model, a backward backhaul link model and a sensing ranging error model, and constructing a sensing integration joint optimization problem; obtaining a geometric accuracy attenuation factor to calculate the local geometric anchor node position of each unmanned aerial vehicle according to the unmanned aerial vehicle position and the target user coarse position estimation set; establishing a backward backhaul cooperative optimization objective function according to the aggregation variable and the local geometric anchor node position, and performing distributed aggregation variable updating, gradient tracking variable updating and unmanned aerial vehicle position updating based on neighbor information interaction, and outputting the unmanned aerial vehicle cluster deployment result and performing sensing integration service. The application can provide low-altitude sensing cooperative support capability for a position service platform in an uncovered or weakly covered area.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication, UAV swarms, low-altitude intelligent networks, integrated sensing and communication, and distributed optimization control, and in particular to a distributed aggregation optimization method and apparatus for low-altitude UAV swarms with enhanced sensing and communication. Background Technology

[0002] With the development of low-altitude economy, emergency communications, and 6G mobile communication technology, drone swarms are widely regarded as an important aerial infrastructure for communication and sensing services in areas with no coverage. In scenarios such as mountain search and rescue, earthquake relief, complex urban obstructions, border patrols, and underground space entrance monitoring, ground communication infrastructure may be damaged or unavailable. Traditional cellular networks are unable to provide continuous coverage, and satellite positioning may be affected by obstruction, multipath propagation, or non-line-of-sight propagation, making it difficult to perceive, locate, and transmit the status of target users in a timely manner.

[0003] Sensing-combination technology enables communication systems to simultaneously possess target perception and data transmission capabilities by sharing spectrum resources, hardware platforms, and signal waveforms. Deploying sensing-combination capabilities in UAV swarms allows UAVs to act as aerial perception anchors for ranging, positioning, and status awareness of ground targets, while simultaneously acting as aerial communication nodes to provide forward communication services to target users and transmit perception results and control information back to collaborating nodes or the core network via backward links. However, existing UAV swarm sensing-combination technologies still have shortcomings. First, existing methods often optimize for coverage, link throughput, or energy consumption, lacking explicit constraints on the target positioning geometry, leading to significant positioning errors when UAVs are approximately collinear or spatially irrationally distributed. Second, over-the-air links in areas with no coverage often exhibit probabilistic line-of-sight / non-line-of-sight characteristics; links at longer distances or lower elevation angles are more prone to non-line-of-sight propagation, introducing additional propagation delays and positive ranging biases, further deteriorating perception and positioning results. Third, existing centralized optimization methods require a central control node to collect global information, making them unsuitable for emergency scenarios with inadequate infrastructure and high real-time requirements. Fourth, existing distributed methods typically only optimize local performance and lack an aggregation variable mechanism that can characterize the global collaborative state of the cluster.

[0004] Therefore, there is a need for an integrated sensory optimization method for UAV swarms that can simultaneously consider sensing geometry, forward communication reliability, backward communication coordination capability, and distributed low-complexity updates, in order to improve service reliability and deployment efficiency in areas with no coverage. Summary of the Invention

[0005] To address the technical problems in existing UAV swarm sensing services, such as unstable positioning accuracy, lack of collaborative optimization in the backhaul link, high complexity of centralized optimization, and severe long-tail positioning errors in complex non-line-of-sight environments, this invention provides a distributed aggregation optimization method and apparatus for low-altitude UAV swarms with enhanced sensing capabilities. The technical solution is as follows: On the one hand, a distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing is provided. This method is implemented by a distributed aggregation collaborative optimization device for low-altitude aircraft clusters, and includes: S1. Establish a sensor-integrated system model for areas with no coverage; the sensor-integrated system model includes a drone swarm, a set of target users, and a collaborative backhaul node. The drone swarm provides sensing and communication services to the set of target users through a forward link, and the drone swarm sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link.

[0006] S2. Based on the integrated sensing system model, establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward backhaul link models, and sensing ranging error models. Based on the established models, calculate the average channel power gain, forward link rate, backward backhaul link rate, sensing mutual information, and ranging error, and construct a joint optimization problem for integrated sensing.

[0007] S3. Obtain the coarse position estimation set of the target user, obtain the geometric accuracy attenuation factor based on the position of the UAV and the coarse position estimation set of the target user, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor.

[0008] S4. Define the drone cluster aggregation variable based on the drone's location. Establish a backward collaborative optimization objective function based on the aggregation variable and the local geometric anchor node position of each drone. Perform distributed aggregation variable update, gradient tracking variable update and drone location update based on neighbor information interaction, and then output the drone cluster deployment result and perform integrated sensing service.

[0009] Optionally, S2 establishes a probabilistic line-of-sight and non-line-of-sight channel model, a forward sensing link model, a backward return link model, and a sensing ranging error model. Based on the established models, it calculates the average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error, including: S21. Obtain the altitude difference, horizontal distance, and elevation angle between any UAV and any target user, and establish a probabilistic line-of-sight and non-line-of-sight channel model.

[0010] S22. Establish line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain. Based on the probabilistic line-of-sight and non-line-of-sight channel models, line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain, establish the average channel power gain between the UAV and the target user, and between the UAV and the cooperative backhaul node.

[0011] S23. Input the average channel power gain into the forward inductive link model and the backward backhaul link model respectively to obtain the forward link rate and the backward backhaul link rate.

[0012] S24. Establish the propagation delay from any UAV to any target user, obtain the ranging observation based on the propagation delay, and input the ranging observation into the sensing ranging error model to obtain sensing mutual information and ranging error.

[0013] Optionally, the joint optimization problem for constructing synesthetic integration in S2 includes: Based on the definition of the sensing error proxy quantity according to the sensing mutual information, a joint optimization problem of sensing integration is established with the minimization of the sensing error proxy quantity as the optimization objective, and the constraints of forward communication reliability, backward communication reliability, transmission power and feasible flight area of ​​UAV are satisfied.

[0014] Optionally, S3 includes: S31. The drone swarm performs initial ranging, coarse positioning, and state awareness of the target user through the forward link, and obtains a coarse position estimate set of the target user.

[0015] S32. Obtain the initial forward sensing link quality and initial backward return link quality through the forward sensing link model and the backward return link model, and determine whether the initial UAV cluster deployment meets the communication rate constraint in the sensing integration joint optimization problem based on the initial forward sensing link quality and the initial backward return link quality.

[0016] S33. Calculate the ranging direction vector between the UAV and the target user based on the UAV's position and the target user's coarse position estimation set, and construct the ranging Jacobian matrix. Define the geometric accuracy attenuation factor based on the ranging Jacobian matrix, and obtain the local geometric anchor node position of each UAV based on minimizing the geometric accuracy attenuation factor.

[0017] Optionally, the geometric precision attenuation factor in S3 is as shown in equation (1) below: (1) in, (2) In the formula, Indicates the geometric precision attenuation factor. Representation matrix The trace calculation, Represents the distance measurement Jacobian matrix. Indicates matrix transpose. Indicates drone The target user group of the service Represents the unit direction vector. Indicates the first The location of the target user Indicates the first The location of the drone.

[0018] Optionally, the local geometric anchor node positions of each UAV in S3 are shown in the following equation (3): (3) In the formula, Indicates the first The local geometric anchor node position of the drone. Indicates the feasible flight area for drones. Indicates drone The target user group of the service This represents the geometric precision factor value under the current deployment. Indicates the first The location of the drone.

[0019] Optionally, the backpropagation collaborative optimization objective function in S4 is shown in equation (4) below: (4) In the formula, Indicates the first The objective function for the backward collaborative optimization of the UAV. Indicates the first The location of the drone This represents the aggregated variable of the drone swarm. This indicates that the anchor node retains its weight. Indicates the first The local geometric anchor node position of the drone. Indicates aggregated collaborative weights. Indicates the forward link reliability penalty weight. This represents the minimum forward link communication rate requirement. This represents the minimum required rate for backward link communication.

[0020] On the other hand, a distributed aggregation optimization device for low-altitude aircraft clusters with enhanced sensing capabilities is provided. This device is applied to a distributed aggregation optimization method for low-altitude aircraft clusters with enhanced sensing capabilities. The device includes: The system modeling module is used to establish a sensor-integrated system model for areas with no coverage. The sensor-integrated system model includes a drone swarm, a set of target users, and a collaborative backhaul node. The drone swarm provides sensing and communication services to the set of target users through a forward link, and sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link.

[0021] The joint optimization problem construction module is used to establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward return link models, and sensing ranging error models based on the integrated sensing system model. Based on the established models, the module calculates the average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error, and constructs the integrated sensing joint optimization problem.

[0022] The local anchor node generation module is used to obtain the target user coarse position estimation set, obtain the geometric accuracy attenuation factor based on the UAV position and the target user coarse position estimation set, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor.

[0023] The service output module is used to define the drone cluster aggregation variables based on the drone's location, establish a backward collaborative optimization objective function based on the aggregation variables and the local geometric anchor node positions of each drone, and perform distributed aggregation variable updates, gradient tracking variable updates, and drone location updates based on neighbor information interaction, thereby outputting the drone cluster deployment results and performing integrated sensing services.

[0024] On the other hand, a low-altitude aircraft cluster distributed aggregation and collaborative optimization device is provided, the low-altitude aircraft cluster distributed aggregation and collaborative optimization device comprising: a processor; a memory, the memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods in the above-described sensory enhancement-oriented low-altitude aircraft cluster distributed aggregation optimization method.

[0025] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods of the distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing.

[0026] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, the ranging geometry between the UAV and the target is explicitly considered through a local anchor node optimization mechanism driven by the geometric accuracy factor, which avoids the positioning ill-conditioning caused by the near-collinear deployment of UAVs, thereby reducing perception positioning error and long-tail error.

[0027] This invention transforms the backward coordination problem into an optimization problem of UAV swarm aggregation variables. By using swarm-centric aggregation variables to represent the backward coordination state, UAVs can achieve global coordination through neighbor information exchange without a central control node.

[0028] This invention can simultaneously optimize local sensing geometry, forward link reliability, and backward link backhaul capability, thereby achieving comprehensive enhancement of integrated sensing services for areas with no coverage.

[0029] This invention employs a distributed aggregation variable update and gradient tracking mechanism to reduce the complexity of centralized global search and improve the real-time deployment capability of drone swarms in emergency scenarios. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of a distributed aggregation optimization method for low-altitude aircraft clusters with enhanced sensing capabilities, provided by an embodiment of the present invention. Figure 2 This is a model diagram of a sensory integration service system based on UAV swarm assistance in areas with weak network coverage, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the overall operation process provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the collaborative optimization process of local geometric accuracy factor optimization and global backpropagation center node provided in the embodiments of the present invention; Figure 5 This is a block diagram of a distributed aggregation optimization method and device for low-altitude aircraft clusters with enhanced sensing provided by an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a low-altitude cluster distributed aggregation and collaborative optimization device provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0033] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0034] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0035] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0036] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0037] This invention provides a distributed aggregation optimization method for low-altitude aircraft clusters aimed at enhancing sensor connectivity. This method can be implemented using a distributed aggregation collaborative optimization device for low-altitude aircraft clusters, which can be a terminal or a server. Figure 1 The flowchart shown is a distributed aggregation optimization method for low-altitude aircraft clusters aimed at enhancing sensory perception. The processing flow of this method may include the following steps: S1. Establish a sensor-integrated system model for areas with no coverage; the sensor-integrated system model includes a drone swarm, a set of target users, and a collaborative backhaul node. The drone swarm provides sensing and communication services to the set of target users through a forward link, and the drone swarm sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link.

[0038] One feasible implementation method is, for example Figure 2 As shown, a sensor-integrated system model is established, consisting of a drone swarm, ground target users, and collaborative backhaul nodes. The drone swarm is used to perform perception and positioning, forward communication, and backward backhaul communication for the target users.

[0039] Specifically, the air-to-ground network modeling adopts the following approach: the drone swarm includes... The drone, the target user group includes The target user, the first The location of the drone is , No. The location of the target user is The location of the collaborative backhaul node is The drone provides perception and communication services to the target user through the forward link, and sends perception results, control information or business data to the collaborative backhaul node through the backward link.

[0040] S2. Based on the integrated sensing system model, establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward backhaul link models, and sensing ranging error models. Based on the established models, calculate the average channel power gain, forward link rate, backward backhaul link rate, sensing mutual information, and ranging error, and construct a joint optimization problem for integrated sensing.

[0041] One feasible implementation method is, for example Figure 3 As shown, based on the integrated sensing system model, a probabilistic line-of-sight / non-line-of-sight channel model, a forward sensing link model, a backward return link model, and a sensing ranging error model are established. Based on the above models, the average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error are calculated. Based on the forward link rate, backward return link rate, sensing mutual information, and ranging error, a joint optimization problem of integrated sensing is constructed with the goal of minimizing sensing error and constraints of forward communication reliability, backward return communication reliability, transmit power, and feasible flight area of ​​UAV.

[0042] Optionally, the establishment of probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward return link models, and sensing ranging error models in S2, and the calculation of average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error based on the established models, may include the following steps S21-S24: S21. Obtain the altitude difference, horizontal distance, and elevation angle between any UAV and any target user, and establish a probabilistic line-of-sight and non-line-of-sight channel model.

[0043] In one feasible implementation, the drone is evaluated based on the altitude difference, horizontal distance, and elevation angle between it and the target user. With target users For air-to-ground links between them, a probabilistic line-of-sight / non-line-of-sight channel model is established, where the elevation angle... Represented as:

[0044] In the formula, Indicates the height difference. and The horizontal projections are for the drone and the target user, respectively.

[0045] Furthermore, the probabilistic line-of-sight / non-line-of-sight channel model is used to calculate the average channel power gain between the UAV and the target user, and between the UAV and the cooperative backhaul node.

[0046] S22. Establish line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain. Based on the probabilistic line-of-sight and non-line-of-sight channel models, line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain, establish the average channel power gain between the UAV and the target user, and between the UAV and the cooperative backhaul node.

[0047] In one feasible implementation, the LoS channel gain, NLoS channel gain, and average channel power gain are obtained by combining the LoS / NLoS (Line-of-Sight / Non-Line-of-Sight) path loss exponent and additional loss.

[0048] Specifically, the line-of-sight probability and the non-line-of-sight probability are respectively:

[0049] In the formula, Indicates the probability of line-of-sight distance. , Indicates environmental parameters, This represents the non-line-of-sight probability.

[0050] Establish large-scale channel gain for LoS / NLoS:

[0051] In the formula, This represents the large-scale channel gain at line-of-sight. This represents a reference value for large-scale channel gain. , , This represents the path loss index. , This indicates additional losses. This represents the non-line-of-sight large-scale channel gain.

[0052] And establish the average channel power gain:

[0053] Furthermore, the average channel power gain is used as the channel quality input for the forward inductive link model and the backward backhaul link model to calculate the signal-to-interference-plus-noise ratio and communication rate of the corresponding link.

[0054] S23. Input the average channel power gain into the forward inductive link model and the backward backhaul link model respectively to obtain the forward link rate and the backward backhaul link rate.

[0055] In one feasible implementation, based on the average channel power gain and the UAV transmit signal model, the forward link signal-to-interference-plus-noise ratio (SIR) and forward link rate in the forward sensing link model from the UAV to the target user are calculated; based on the average channel power gain from the UAV to the cooperative backhaul node, the backward link SIR and backward backhaul rate in the backward backhaul link model are calculated.

[0056] Specifically, the average channel power gain is a fundamental parameter for both the forward sensing link model and the backward backhaul link model. For the forward sensing link, the average channel power gain is used to determine the channel vector or equivalent channel gain from the UAV to the target user, and then to calculate the forward communication signal-to-interference-plus-noise ratio (SIR), forward communication rate, and perceived mutual information. For the backward backhaul link, the average channel power gain is used to determine the channel quality from the UAV to the cooperative backhaul node, and then to calculate the backward backhaul SIR and backward backhaul rate.

[0057] S24. Establish the propagation delay from any UAV to any target user, obtain the ranging observation based on the propagation delay, and input the ranging observation into the sensing ranging error model to obtain sensing mutual information and ranging error.

[0058] In one feasible implementation, a propagation delay model and a ranging observation model are established based on the LoS / NLoS propagation state. The ranging observation is the direct input to the sensing ranging error model, which consists of the true distance, non-line-of-sight (NOS) additional ranging bias, and random measurement noise. Based on this, the sensing ranging error model is obtained. The sensing ranging error model then uses this ranging observation to construct ranging error, the ranging Jacobian matrix, and positioning accuracy indicators such as GDOP (Geometric Dilution of Precision) / PEB (Position Error Bound). Simultaneously, the sensing ranging error model is used to characterize the impact of NOS and random noise in the target user's ranging observation, and further used to construct the sensing error objective function or positioning accuracy constraints in the joint optimization problem.

[0059] Specifically, the ranging bias model caused by non-line-of-sight propagation, UAV. To target users The propagation delay is:

[0060] In the formula, Represents the speed of light. This indicates the additional time delay measurement error caused by non-line-of-sight propagation.

[0061] The corresponding distance measurement observation is represented as follows:

[0062] in,

[0063] In the formula, Indicates the distance measurement value. Represents the speed of light. This indicates the distance measurement error. This is a positive ranging bias induced by NLoS. Since the probability of NLoS occurrence increases as the elevation angle decreases, longer horizontal distances typically lead to a larger probability of excessive delay, resulting in a larger ranging error. Therefore, in the UAV swarm-assisted ISAC (Integrated Sensing and Communication Link) scenario considered, long-distance links are more likely to be affected by both a decrease in the received signal-to-noise ratio and an increase in ranging bias caused by delay, which together reduce the amount of sensing information and estimation accuracy.

[0064] Optionally, the joint optimization problem for constructing synesthetic integration in S2 includes: Based on the definition of the sensing error proxy quantity according to the sensing mutual information, a joint optimization problem of sensing integration is established with the minimization of the sensing error proxy quantity as the optimization objective, and the constraints of forward communication reliability, backward communication reliability, transmission power and feasible flight area of ​​UAV are satisfied.

[0065] In one feasible implementation, the forward link rate, the backward return link rate, and the sensing and positioning error index together constitute the objective function and constraints of the integrated sensory optimization problem.

[0066] Specifically, with minimizing the perception error as the objective function, and constrained by the following conditions: the forward link rate is no less than a preset forward rate threshold, the backward return link rate is no less than a preset backward rate threshold, the UAV's transmit power does not exceed a preset power limit, and the UAV's position is within a feasible flight area, a joint optimization problem integrating sensing and communication is constructed. This joint optimization problem guides subsequent GDOP local anchor node generation, backward return aggregation collaborative modeling, and distributed position iterative update.

[0067] The forward communication rate is:

[0068] In the formula, This indicates the bandwidth of the forward link. This indicates the signal-to-interference-plus-noise ratio (SIR / NNR) of the forward communication link.

[0069] The backhaul rate is:

[0070] In the formula, Indicates the bandwidth of the backlink. This indicates the signal-to-interference-plus-noise ratio (SIR) of the backward communication link.

[0071] Perceived mutual information is:

[0072] In the formula, Indicates drone For users Provided perceptual mutual information, This represents the perceived signal-to-interference-plus-noise ratio (SIR). Indicates participating users A collection of drone nodes, Indicates user From the set All perceptual mutual information obtained.

[0073] The perceived error proxy quantity is defined as:

[0074] In the formula, This represents the surrogate reference quantity for perceived error.

[0075] Establish a joint optimization problem with the objective of minimizing perception error while simultaneously satisfying constraints on forward communication rate and backward transmission rate:

[0076] In the formula, This represents the communication rate threshold of the forward link. This represents the communication rate threshold for the backlink. Indicates drone For users The provided beamforming matrix, Indicates drone Beamforming matrix to the shared backward transmission node, Indicates drone The power threshold.

[0077] Step S2 of this invention uses a probabilistic line-of-sight / non-line-of-sight channel model, a forward sensing link model, a backward return link model, and a sensing ranging error model to provide the channel gain, link rate, sensing mutual information, ranging bias, and sensing error constraints / objective functions in the joint optimization problem. Specifically, the probabilistic LoS / NLoS channel model is used to calculate the average channel power gain between the UAV and the target user, and between the UAV and the cooperative backhaul node; the forward sensing link model calculates the forward link signal-to-interference-plus-noise ratio and forward communication rate based on this average channel power gain; the backward return link model calculates the backward return rate based on the channel gain between the UAV and the cooperative backhaul node; and the sensing ranging error model calculates the ranging error, ranging Jacobian matrix, and sensing error amount based on the LoS / NLoS state, propagation delay, and ranging observations. These models together form the objective function and constraints in the optimization problem, namely, minimizing the sensing error while simultaneously satisfying the forward sensing link rate, backward return rate, power constraints, and UAV feasible flight area constraints.

[0078] Furthermore, the joint optimization problem obtained in step S2 serves as the overall optimization objective and constraint source for subsequent steps. The GDOP local anchor node optimization in S3 is a geometric decomposition of the perception error minimization objective in the joint optimization problem; the aggregated variable and backhaul collaborative optimization objective in S4 is a distributed reconstruction of the backhaul link constraints and collaborative backhaul objective in the joint optimization problem; position update, aggregated variable update, and gradient tracking variable update are distributed iterative solutions to the decomposed local objective function; finally, the final UAV deployment result is output based on the convergence conditions of the joint optimization problem.

[0079] S3. Obtain the coarse position estimation set of the target user, obtain the geometric accuracy attenuation factor based on the position of the UAV and the coarse position estimation set of the target user, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor.

[0080] One feasible implementation method is, for example Figure 4 As shown, the coarse position estimation set of the target user and the initial communication link quality are obtained based on the initial sensing transmission. Then, the ranging Jacobian matrix and the geometric accuracy attenuation factor GDOP are constructed based on the coarse position estimation set of the target user to generate the local geometric anchor node position of the UAV.

[0081] Optionally, step S3 above may include the following steps S31-S33: S31. The drone swarm performs initial ranging, coarse positioning, and state awareness of the target user through the forward link, and obtains a coarse position estimate set of the target user.

[0082] S32. Obtain the initial forward sensing link quality and initial backward return link quality through the forward sensing link model and the backward return link model, and determine whether the initial UAV cluster deployment meets the communication rate constraint in the sensing integration joint optimization problem based on the initial forward sensing link quality and the initial backward return link quality.

[0083] In one feasible implementation, the joint optimization problem is related to the target user coarse location estimation set and the initial communication link quality as "initial input and constraint evaluation." The target user coarse location estimation set is used to initialize the target user's position and serves as the computational basis for subsequent generation of the ranging Jacobian matrix, GDOP index, and local anchor nodes. The initial communication link quality is used to initialize the forward and backward link rate evaluations and determine whether the initial deployment meets the communication reliability constraints. During subsequent optimization, as the UAV's position is updated, the target user position estimation, GDOP, forward link rate, and backward link rate can be periodically updated, thereby driving the decomposition and solution of the joint optimization problem.

[0084] S33. Calculate the ranging direction vector between the UAV and the target user based on the UAV's position and the target user's coarse position estimation set, and construct the ranging Jacobian matrix. Define the geometric accuracy attenuation factor based on the ranging Jacobian matrix, and obtain the local geometric anchor node position of each UAV based on minimizing the geometric accuracy attenuation factor.

[0085] In one feasible implementation, a local anchor node optimization mechanism is constructed based on the ranging Jacobian matrix and the geometrical accuracy attenuation factor (GDOP): For distance measurement functions Its Jacobian matrix terms are:

[0086] Constructing a ranging Jacobian matrix for target localization Its normalized geometric vector is:

[0087] In the formula, This represents the unit direction vector.

[0088] The Jacobian matrix is ​​obtained by stacking multiple ranging directions. And define the geometric precision attenuation factor:

[0089] In the formula, Indicates the geometric precision attenuation factor. Representation matrix The trace calculation, Represents the distance measurement Jacobian matrix. Indicates matrix transpose. Indicates drone The target user group of the service Represents the unit direction vector. Indicates the first The location of the target user Indicates the first The location of the drone.

[0090] drones The local anchor node positions are obtained through the following optimization:

[0091] In the formula, Indicates the first The local geometric anchor node position of the drone. Indicates the feasible flight area for drones. Indicates drone The target user group of the service This represents the geometric precision factor under the current deployment. Indicates the first The location of the UAV. A set of local anchor nodes for the UAV with better geometric observability is obtained by minimizing GDOP, denoted as... .

[0092] S4. Define the drone cluster aggregation variable based on the drone's location. Establish a backward collaborative optimization objective function based on the aggregation variable and the local geometric anchor node position of each drone. Perform distributed aggregation variable update, gradient tracking variable update and drone location update based on neighbor information interaction, and then output the drone cluster deployment result and perform integrated sensing service.

[0093] In one feasible implementation, an aggregate variable is constructed based on the location of the drone cluster, and a backward collaborative optimization objective function is established based on the aggregate variable; distributed aggregate variable updates, gradient tracking variable updates, and drone location updates are performed based on neighbor information interaction; when the drone location change is less than a preset threshold or the maximum number of iterations is reached, the final deployment result of the drone cluster is output, and the integrated sensing service is executed.

[0094] Specifically, in the iterative optimization, the aggregation variable for the drone swarm is defined as follows:

[0095] In the formula, This represents the aggregated variable of the drone swarm. Indicates drone The contribution value in the aggregate variable.

[0096] For example when hour,

[0097] Aggregate variables include, but are not limited to, those representing the centralized backward coordination state of the drone swarm and omnidirectional wireless charging nodes. The backward coordination problem optimizes the location of the coordinating nodes. For example, the optimization problem can be approximated as:

[0098] This transforms the backward coordination problem into an optimization problem based on the aggregation variables of the UAV swarm.

[0099] Furthermore, based on the local geometry optimization nodes, this invention proposes a method for updating distributed aggregation variables based on neighbor information interaction: No. The local aggregation objective function for the UAV is:

[0100] In the formula, Indicates the first The objective function for the backward collaborative optimization of the UAV. Indicates the first The location of the drone This represents the aggregated variable of the drone swarm. This indicates that the anchor node retains its weight. Indicates the first The local geometric anchor node position of the drone. Indicates aggregated collaborative weights. Indicates the forward link reliability penalty weight. This represents the minimum forward link communication rate requirement. This represents the forward communication rate under the current deployment. The first term is used to maintain the local GDOP anchor node configuration, the second term is used to guide the UAV swarm to form an aggregation configuration that facilitates backhaul, and the third term is used to ensure the reliability of forward communication.

[0101] Furthermore, perform distributed aggregation and collaborative optimization.

[0102] drones The location has been updated to:

[0103] In the formula, Indicates step size, Indicates regarding Local partial gradient, Indicates drone Local estimation of cluster aggregation variables, This represents the local partial gradient with respect to the aggregate variable. Indicates drone Gradient tracking variables.

[0104] The local aggregation variable is updated to:

[0105] Gradient tracking variable updated to:

[0106] In the formula, represents the adjacency weight in the UAV communication graph.

[0107] The corresponding gradient expression is:

[0108] Next, determine the convergence condition.

[0109] When the following conditions are met:

[0110] Alternatively, when the number of iterations reaches a preset limit, the iteration will stop and the final drone deployment result will be output. This represents the threshold.

[0111] This invention proposes a distributed aggregation and collaborative optimization method for low-altitude UAV swarms to enhance integrated sensing services in uncovered areas such as mountainous regions, post-disaster areas, underground spaces, and urban environments with obstructed views. The method includes: establishing an integrated sensing system model consisting of a UAV swarm, ground target users, and collaborative backhaul nodes; calculating average channel power gain, forward link rate, backhaul link rate, sensing mutual information, and ranging error based on probabilistic line-of-sight / non-line-of-sight channel models, non-line-of-sight additional ranging bias models, forward sensing link models, and backward backhaul link models; constructing a joint optimization problem for integrated sensing with the objective of minimizing sensing error and constraints on forward link reliability and backward backhaul link reliability; obtaining a coarse location estimation set for target users and initial communication link quality based on initial sensing transmission; constructing a local anchor node optimization mechanism based on the ranging Jacobian matrix and geometric accuracy factor; constructing a backward backhaul collaborative optimization objective based on UAV swarm aggregation variables; and achieving collaborative reconfiguration of the UAV swarm under decentralized control conditions through distributed aggregation variable updates, gradient tracking variable updates, and iterative updates of UAV positions.

[0112] Step 101: Initialize the integrated sensor service environment in the coverage area. Set the service area size, number of drones, number of target users, drone flight altitude, target user altitude, cooperative backhaul node altitude, forward link bandwidth, backward link bandwidth, transmit power, noise power spectral density, and non-line-of-sight ranging deviation parameters.

[0113] Step 102: Establish the probabilistic LoS / NLoS channel model, forward sensing link model, backward return link model, and sensing ranging error model. Calculate the air-to-ground link elevation angle, line-of-sight probability, non-line-of-sight probability, LoS / NLoS channel gain, and average channel power gain based on the positional relationships of the UAV, target user, and cooperative return nodes. Calculate the forward link signal-to-interference-plus-noise ratio (SIR), forward communication rate, backward return SIR, and backward return rate based on the average channel power gain. Establish propagation delay and ranging observation models based on the LoS / NLoS propagation state to obtain the sensing ranging error model, which consists of the actual distance, non-line-of-sight ranging deviation, and random measurement noise.

[0114] Step 103: Establish the integrated sensing and communication joint optimization problem. Based on the forward communication rate, backward transmission rate, and sensing ranging error model obtained in Step 102, construct a joint optimization problem with the objective of minimizing sensing error, while simultaneously satisfying the constraints of forward communication rate, backward transmission rate, transmit power, and feasible flight area of ​​the UAV.

[0115] Step 104: Perform initial sensing transmission and obtain the initial input for the joint optimization problem. The UAV swarm performs initial ranging, coarse localization, and state awareness of the target user through the forward sensing link, obtaining a coarse position estimate set for the target user. Based on the link model in step 102, the initial forward link quality and backward link quality are obtained. The coarse position estimate set for the target user is used for subsequent calculations of the ranging Jacobian matrix and GDOP index, and the initial communication link quality is used to evaluate whether the initial deployment satisfies the communication rate constraints in the joint optimization problem.

[0116] Step 105: Generate local GDOP anchor nodes. Based on the target user's coarse position estimation set, calculate the ranging direction vector between the UAV and the target user, construct the ranging Jacobian matrix, calculate the GDOP index, and obtain the local geometric anchor node positions through discrete search or distributed direction search. This step corresponds to the geometric decomposition of the perception error minimization objective in the joint optimization problem.

[0117] Step 106: Establish the backhaul aggregation and collaboration structure. Calculate aggregation variables based on the current deployment location of the UAV and use them as representations of the backhaul collaboration nodes or virtual backhaul centers. This step corresponds to the aggregation decomposition of backhaul communication reliability constraints in the joint optimization problem.

[0118] Step 107: Perform distributed aggregation collaborative optimization. Each drone initializes its own position, local aggregation variables, and gradient tracking variables; at each iteration, it only exchanges information with neighboring drones and updates its position, aggregation variables, and gradient tracking variables according to the formula.

[0119] Step 108: Determine convergence and output the deployment result. If the maximum position change is less than the threshold, the drone swarm is considered to have reached stable deployment; otherwise, continue iterating. Ultimately, the drone swarm forms a deployment configuration that balances GDOP positioning geometry, forward communication reliability, and backward data transmission coordination capabilities.

[0120] In one specific embodiment, the service area is 1000m × 1000m, and five drones are deployed to provide sensing services to ground target users. The drones fly at an altitude of 120m, the target user is at an altitude of 1.5m, the forward link bandwidth is 20MHz, the backward link bandwidth is 40MHz, and the carrier frequency is 3.5GHz. Using the method of this invention, the drone swarm completes position reconstruction within approximately several dozen iterations, significantly reducing target PEB positioning errors and improving forward and backward communication rates.

[0121] In this embodiment, if the UAV only adopts a coverage extension strategy, it may move further away from the target user, resulting in a decrease in received signal quality and an increase in the probability of non-line-of-sight transmission. If the UAV only adopts a strategy of moving towards the target user, it may form an approximately collinear configuration, resulting in insufficient geometric diversity. This invention optimizes the UAV by using GDOP anchor nodes and aggregation coordination in two stages, ensuring that the UAV maintains a reasonable distance and spatial angle while guaranteeing the cluster coordination capability near the backhaul node.

[0122] This invention can be applied to various business scenarios such as ground / low-altitude map navigation, low-altitude traffic management, low-altitude logistics scheduling, emergency rescue, mountain patrol, and service enhancement in complex urban areas with obstructions. It provides location service platforms such as Gaode Maps with low-altitude sensing and collaborative support capabilities in areas with no or weak coverage. By constructing temporary aerial sensing and communication infrastructure through UAV swarms, it can enhance the positioning, link backhaul, and environmental awareness capabilities of low-altitude aircraft, ground users, and emergency targets, providing real-time information support for low-altitude route planning, low-altitude traffic situational awareness, emergency path guidance, and air-ground collaborative scheduling. This invention can improve the positioning accuracy, forward link reliability, and backward backhaul capability of integrated sensing services in areas with no coverage, such as mountainous areas, post-disaster ruins, underground spaces, and urban areas with obstructions, reduce the complexity of centralized optimization, and effectively suppress long-tail positioning errors caused by poor geometric measurement structures in complex non-line-of-sight environments.

[0123] In this embodiment of the invention, the ranging geometry between the UAV and the target is explicitly considered through a local anchor node optimization mechanism driven by the geometric accuracy factor, which avoids the positioning ill-conditioning caused by the near-collinear deployment of the UAV, thereby reducing the perception positioning error and long-tail error.

[0124] This invention transforms the backward coordination problem into an optimization problem of UAV swarm aggregation variables. By using swarm-centric aggregation variables to represent the backward coordination state, UAVs can achieve global coordination through neighbor information exchange without a central control node.

[0125] This invention can simultaneously optimize local sensing geometry, forward link reliability, and backward link backhaul capability, thereby achieving comprehensive enhancement of integrated sensing services for areas with no coverage.

[0126] This invention employs a distributed aggregation variable update and gradient tracking mechanism to reduce the complexity of centralized global search and improve the real-time deployment capability of drone swarms in emergency scenarios.

[0127] Figure 5 This is a block diagram illustrating a sensor-enhanced low-altitude aircraft cluster distributed aggregation optimization apparatus, which is used in a sensor-enhanced low-altitude aircraft cluster distributed aggregation optimization method. (Refer to...) Figure 5 The device includes a system modeling module 310, a joint optimization problem construction module 320, a local anchor node generation module 330, and a service output module 340. Among them: The system modeling module 310 is used to establish a sensor-integrated system model for areas with no coverage. The sensor-integrated system model includes a drone swarm, a set of target users, and a collaborative backhaul node. The drone swarm provides sensing and communication services to the set of target users through a forward link, and sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link.

[0128] The joint optimization problem construction module 320 is used to establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward return link models, and sensing ranging error models based on the integrated sensing system model. Based on the established models, the module calculates the average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error, and constructs the integrated sensing joint optimization problem.

[0129] The local anchor node generation module 330 is used to obtain the target user coarse position estimation set, obtain the geometric accuracy attenuation factor based on the UAV position and the target user coarse position estimation set, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor.

[0130] The service output module 340 is used to define the drone cluster aggregation variables based on the drone's location, establish a backward collaborative optimization objective function based on the aggregation variables and the local geometric anchor node positions of each drone, and perform distributed aggregation variable updates, gradient tracking variable updates, and drone location updates based on neighbor information interaction, thereby outputting the drone cluster deployment results and performing integrated sensing services.

[0131] In this embodiment of the invention, the ranging geometry between the UAV and the target is explicitly considered through a local anchor node optimization mechanism driven by the geometric accuracy factor, which avoids the positioning ill-conditioning caused by the near-collinear deployment of the UAV, thereby reducing the perception positioning error and long-tail error.

[0132] This invention transforms the backward coordination problem into an optimization problem of UAV swarm aggregation variables. By using swarm-centric aggregation variables to represent the backward coordination state, UAVs can achieve global coordination through neighbor information exchange without a central control node.

[0133] This invention can simultaneously optimize local sensing geometry, forward link reliability, and backward link backhaul capability, thereby achieving comprehensive enhancement of integrated sensing services for areas with no coverage.

[0134] This invention employs a distributed aggregation variable update and gradient tracking mechanism to reduce the complexity of centralized global search and improve the real-time deployment capability of drone swarms in emergency scenarios.

[0135] Figure 6 This is a schematic diagram of the structure of a low-altitude cluster distributed aggregation and collaborative optimization device provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the low-altitude aircraft cluster distributed aggregation and collaborative optimization device may include the above-mentioned Figure 5 The illustrated low-altitude aircraft cluster distributed aggregation optimization device is designed for enhanced sensing. Optionally, the low-altitude aircraft cluster distributed aggregation collaborative optimization device 410 may include a first processor 2001.

[0136] Optionally, the low-altitude cluster distributed aggregation and collaborative optimization device 410 may also include a memory 2002 and a transceiver 2003.

[0137] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0138] The following is combined with Figure 6 A detailed introduction to each component of the low-altitude aircraft cluster distributed aggregation and collaborative optimization device 410 is provided below: The first processor 2001 is the control center of the low-altitude aircraft cluster distributed aggregation and collaborative optimization device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0139] Optionally, the first processor 2001 can perform various functions of the low-altitude cluster distributed aggregation collaborative optimization device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0140] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 6 CPU0 and CPU1 are shown in the diagram.

[0141] In a specific implementation, as one example, the low-altitude cluster distributed aggregation and collaborative optimization device 410 may also include multiple processors, for example... Figure 6 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0142] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0143] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the low-altitude cluster distributed aggregation collaborative optimization device 410. Figure 6 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0144] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0145] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 6 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0146] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected to the interface circuit of the low-altitude cluster distributed aggregation collaborative optimization device 410. Figure 6 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0147] It should be noted that, Figure 6 The structure of the low-altitude cluster distributed aggregation collaborative optimization device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0148] Furthermore, the technical effects of the low-altitude aircraft cluster distributed aggregation and collaborative optimization device 410 can be referred to the technical effects of the low-altitude aircraft cluster distributed aggregation optimization method for enhanced sensing described in the above method embodiments, and will not be repeated here.

[0149] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0150] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0151] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0152] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0153] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0154] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0155] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0157] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0158] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0160] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, 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 invention. 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.

[0161] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A distributed aggregation optimization method for low-altitude aircraft fleets oriented towards enhanced sensory perception, characterized in that, The method includes: S1. Establish a sensor-integrated system model for areas with no coverage; wherein, the sensor-integrated system model includes a drone swarm, a target user set, and a collaborative backhaul node, the drone swarm provides sensing and communication services to the target user set through a forward link, and the drone swarm sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link. S2. Based on the integrated sensing system model, establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward backhaul link models, and sensing ranging error models. Based on the established models, calculate the average channel power gain, forward link rate, backward backhaul link rate, sensing mutual information, and ranging error, and construct a joint optimization problem for integrated sensing. S3. Obtain the coarse position estimation set of the target user, obtain the geometric accuracy attenuation factor based on the position of the UAV and the coarse position estimation set of the target user, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor. S4. Define the drone cluster aggregation variable based on the drone's location. Establish a backward collaborative optimization objective function based on the aggregation variable and the local geometric anchor node position of each drone. Perform distributed aggregation variable update, gradient tracking variable update and drone location update based on neighbor information interaction, and then output the drone cluster deployment result and perform integrated sensing service.

2. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing, as described in claim 1, is characterized in that... The establishment of probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward return link models, and sensing ranging error models in S2, and the calculation of average channel power gain, forward link rate, backward return link rate, sensing mutual information, and ranging error based on the established models, includes: S21. Obtain the altitude difference, horizontal distance, and elevation angle between any UAV and any target user, and establish a probabilistic line-of-sight and non-line-of-sight channel model. S22. Establish line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain. Based on the probabilistic line-of-sight and non-line-of-sight channel models, line-of-sight large-scale channel gain and non-line-of-sight large-scale channel gain, establish the average channel power gain between the UAV and the target user, and between the UAV and the cooperative backhaul node. S23. Input the average channel power gain into the forward inductive link model and the backward backhaul link model respectively to obtain the forward link rate and the backward backhaul link rate. S24. Establish the propagation delay from any UAV to any target user, obtain the ranging observation based on the propagation delay, and input the ranging observation into the sensing ranging error model to obtain sensing mutual information and ranging error.

3. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing, as described in claim 1, is characterized in that... The joint optimization problem for constructing synesthetic integration in S2 includes: Based on the definition of the sensing error proxy quantity according to the sensing mutual information, a joint optimization problem of sensing integration is established with the minimization of the sensing error proxy quantity as the optimization objective, and the constraints of forward communication reliability, backward communication reliability, transmission power and feasible flight area of ​​UAV are satisfied.

4. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensory perception, as described in claim 1, is characterized in that... The S3 includes: S31. The drone swarm performs initial ranging, coarse localization and state awareness of the target user through the forward link, and obtains a coarse position estimate set of the target user. S32. Obtain the initial forward sensing link quality and initial backward transmission link quality through the forward sensing link model and the backward transmission link model, and determine whether the initial UAV cluster deployment meets the communication rate constraint in the sensing integration joint optimization problem based on the initial forward sensing link quality and initial backward transmission link quality. S33. Calculate the ranging direction vector between the UAV and the target user based on the UAV's position and the target user's coarse position estimation set, and construct the ranging Jacobian matrix. Define the geometric accuracy attenuation factor based on the ranging Jacobian matrix, and obtain the local geometric anchor node position of each UAV based on minimizing the geometric accuracy attenuation factor.

5. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensory perception, as described in claim 1, is characterized in that... The geometric precision attenuation factor in S3 is shown in equation (1) below: （1） in, （2） In the formula, Indicates the geometric precision attenuation factor. Representation matrix The trace calculation, Represents the distance measurement Jacobian matrix. Indicates matrix transpose. Indicates drone The target user group of the service Represents the unit direction vector. Indicates the first The location of the target user Indicates the first The location of the drone.

6. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing, as described in claim 1, is characterized in that... The local geometric anchor node positions of each UAV in S3 are shown in the following equation (3): （3） In the formula, Indicates the first The local geometric anchor node position of the drone. Indicates the feasible flight area for drones. Indicates drone The target user group of the service This represents the geometric precision factor value under the current deployment. Indicates the first The location of the drone.

7. The distributed aggregation optimization method for low-altitude aircraft clusters oriented towards enhanced sensing, as described in claim 1, is characterized in that... The backward collaborative optimization objective function in S4 is shown in equation (4) below: （4） In the formula, Indicates the first The objective function for the backward collaborative optimization of the UAV. Indicates the first The location of the drone Represents the aggregated variables of the drone swarm. This indicates that the anchor node retains its weight. Indicates the first The local geometric anchor node position of the drone. Indicates aggregated collaborative weights. Indicates the forward link reliability penalty weight. This represents the minimum forward link communication rate requirement. This indicates the forward communication rate under the current deployment.

8. A distributed aggregation optimization device for low-altitude aircraft fleets with enhanced sensing capabilities, wherein the device is used to implement the distributed aggregation optimization method for low-altitude aircraft fleets with enhanced sensing capabilities as described in any one of claims 1-7, characterized in that, The device includes: The system modeling module is used to establish a sensor-integrated system model for areas with no coverage. The sensor-integrated system model includes a drone swarm, a target user set, and a collaborative backhaul node. The drone swarm provides sensing and communication services to the target user set through a forward link, and the drone swarm sends sensing results, control information, and / or service data to the collaborative backhaul node through a backward link. The joint optimization problem construction module is used to establish probabilistic line-of-sight and non-line-of-sight channel models, forward sensing link models, backward backhaul link models, and sensing ranging error models based on the integrated sensing system model. Based on the established models, the module calculates the average channel power gain, forward link rate, backward backhaul link rate, sensing mutual information, and ranging error, and constructs the integrated sensing joint optimization problem. The local anchor node generation module is used to obtain the target user coarse position estimation set, obtain the geometric accuracy attenuation factor based on the UAV position and the target user coarse position estimation set, and obtain the local geometric anchor node position of each UAV based on the geometric accuracy attenuation factor. The service output module is used to define the drone cluster aggregation variables based on the drone's location, establish a backward collaborative optimization objective function based on the aggregation variables and the local geometric anchor node positions of each drone, and perform distributed aggregation variable updates, gradient tracking variable updates, and drone location updates based on neighbor information interaction, thereby outputting the drone cluster deployment results and performing integrated sensing services.

9. A low-altitude aircraft cluster distributed aggregation and collaborative optimization device, characterized in that, The low-altitude aircraft cluster distributed aggregation and collaborative optimization device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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