A multi-target cooperative detection network deployment method and device based on a UAV group

Abstract

The application provides a kind of multi-target cooperative detection network deployment method and equipment based on UAV group, it is related to communication sensing technical field, comprising: the comprehensive energy detection probability of UAV group to multiple targets is obtained by calculation;Build multi-target cooperative detection network deployment problem model based on UAV group;The initial solution of multi-target cooperative detection network deployment problem model is obtained by spectral clustering algorithm, obtains initial UAV position set;The optimal UAV position set is obtained by the iterative solution of multi-target cooperative detection network deployment problem model to initial UAV position set and gibbs sampling algorithm.The comprehensive energy detection probability of the application considers the resource limited conditions such as UAV group size, deployment range, channel frequency range and the like, so that the optimal UAV position set obtained by final solution can make the sensing accuracy, sensitivity and other sensing performances of UAV group under resource limited conditions reach cooperative optimization.

Description

Technical Field

[0001] This invention relates to the field of communication sensing technology, and in particular to a method and device for deploying a multi-target collaborative detection network based on a swarm of unmanned aerial vehicles (UAVs). Background Technology

[0002] As a promising paradigm, target perception technology based on UAV swarms has attracted widespread attention and in-depth discussion in existing research. Cooperative detection by UAV swarms improves target perception performance and is widely used in disaster relief, power line inspection, military reconnaissance, battlefield assessment, and traffic detection. Therefore, based on the goal of efficiently meeting the perception requirements of UAV swarms for accurate reconnaissance and intelligent analysis in complex battlefield environments, and considering requirements such as airspace, complex electromagnetic environment, and superior sensing capabilities, this paper analyzes the inherent evolutionary laws of optimized deployment of UAV swarms and studies a multi-level distributed multi-target cooperative detection network deployment method. By establishing a mapping model between the topological relationship of UAV swarms and the utility of the detection network in typical scenarios, an efficient distributed deployment algorithm is designed to achieve accurate and rapid target perception. However, existing multi-target cooperative detection network deployment methods fail to consider the impact of dynamic electromagnetic environments, easily constructed network architectures, and time-varying sensor links, resulting in poor cooperative control effects of UAV swarms. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method and device for deploying a multi-target cooperative detection network based on a drone swarm, in order to solve the technical problem that the existing multi-target cooperative detection network deployment methods have poor cooperative control effects due to the failure to consider the effects of dynamic electromagnetic environment, easily constructed network architecture and time-varying sensor links.

[0004] This invention provides a method for deploying a multi-target cooperative detection network based on a drone swarm, comprising the following steps:

[0005] S1: Based on the free space propagation model, calculate the comprehensive energy detection probability of the UAV swarm for multiple targets;

[0006] S2: Based on the comprehensive energy detection probability, construct a model for the deployment of a multi-target cooperative detection network based on UAV swarms;

[0007] S3: Initially solve the multi-target cooperative detection network deployment problem model using spectral clustering algorithm to obtain the initial set of UAV locations;

[0008] S4: The optimal UAV location set is obtained by iteratively solving the multi-target cooperative detection network deployment problem model through the initial UAV location set and the Gibbs sampling algorithm.

[0009] Preferred:

[0010] In the free-space propagation model, the number of drones is M, and the position of the i-th drone is q. i =[x i y i H] T , (x i y i The coordinates (x and y) represent the location, H represents the drone's altitude, K represents the number of targets to be sensed, and the location of the k-th target is... The distance from drone i to target k is:

[0011] Preferably, step s1 specifically includes:

[0012] S11: Based on the free-space propagation model, calculate the path loss between the UAV and the target;

[0013] S12: Calculate the channel gain signal-to-noise ratio between the UAV and the target using path loss calculation;

[0014] S13: Calculate the probability of detecting a single energy of the UAV and the target within the channel frequency range using the channel gain signal-to-noise ratio.

[0015] S14: Repeat steps S11-S13 to obtain all individual energy detection probabilities;

[0016] S15: Calculate the overall energy detection probability of the UAV swarm against multiple targets by using the individual energy detection probabilities.

[0017] Preferred:

[0018] Path loss PL between UAV i and target k i，k The formula for calculating (η) is:

[0019] PL i,k (η)=20lg(f k )+20lg(d i，k )+32.4+η

[0020] Where, η∈{η L η N} represents the attenuation exponent of the LoS and NLoS links, f k The channel frequency of target k is represented;

[0021] Channel gain g between UAV i and target k i,k The formula for calculating (η) is:

[0022]

[0023] Channel gain signal-to-noise ratio γ between UAV i and target k i，k (qi The formula for calculating ) is:

[0024]

[0025] Where, p k σ is the target signal transmission power. 2 Noise power;

[0026] The probability P of a single energy detection of drone i against target k i，k (q i The formula for calculating ) is:

[0027]

[0028] Where, γ i，k Let P be the channel gain signal-to-noise ratio, Q be the right tail function of the standard normal distribution, N be the number of received signal samples, and P be the signal-to-noise ratio. fa The false alarm probability of the sensing system;

[0029] The channel frequency range of UAV i is obtained as (f i min f i max If the drone i and the target k are in the channel frequency f, then the drone i and the target k are in the channel frequency f. k Single energy detection probability within the channel frequency range The calculation formula is:

[0030]

[0031] in, This represents the number of received signal samples within the channel frequency range.

[0032] The overall energy detection probability of a drone swarm targeting target k within the channel frequency range fk. The calculation formula is:

[0033]

[0034] Preferably, step S2 specifically includes:

[0035] A total detection probability sum function is constructed based on the comprehensive energy detection probability to measure the cooperative perception accuracy of UAV swarms for multiple targets. The total detection probability sum function P ED The expression for (Q) is:

[0036]

[0037] Among them, F′ k Let Δf be the carrier frequency. k For channel spacing, For a swarm of drones targeting target k at channel frequency F′ k +i×Δf k The overall energy detection probability within the channel frequency range;

[0038] A model for the deployment of a multi-target cooperative detection network based on a drone swarm is constructed using the total detection probability and function, expressed as follows:

[0039]

[0040] Where i and j are the drone's serial numbers, Let D be the set of drone locations, and r be the deployable area. min R is the minimum safe distance between drones and ground threats. h R represents the maximum communication distance between drones. d This represents the minimum safe distance between any two drones.

[0041] Preferably, step S4 specifically includes:

[0042] S41: Set the iteration count l to 1, and the initial drone position set Q 1 Input a multi-target cooperative detection network deployment problem model;

[0043] S42: Perform the l-th iteration, calculate the transition probability of each UAV using the Gibbs sampling algorithm, update the position of each UAV using the transition probability, and obtain the UAV position set Q for the l-th iteration. l ;

[0044] S43: Let l = l + 1;

[0045] S44: Repeat steps S42-S43 until the total detection probability and function P are reached. ED (Q) When convergence or the maximum number of iterations is reached, the final set of drone locations is taken as the optimal set of drone locations.

[0046] A storage medium storing instructions and data for implementing the aforementioned multi-target cooperative detection network deployment method based on UAV swarms.

[0047] A multi-target collaborative detection network deployment device based on UAV swarms includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the multi-target collaborative detection network deployment method based on UAV swarms.

[0048] The present invention has the following beneficial effects:

[0049] A multi-target cooperative detection network deployment problem model is constructed by integrating energy detection probabilities, and the deployment problem model is solved by spectral clustering algorithm and Gibbs sampling algorithm. Since the integrated energy detection probabilities take into account resource constraints such as UAV swarm size, deployment range, and channel frequency range, the optimal UAV position set obtained in the final solution can enable the UAV swarm to achieve cooperative optimal perception performance such as perception accuracy and sensitivity under resource constraints. Attached Figure Description

[0050] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of a free-space propagation model;

[0052] Figure 3 Deployment diagram of the collaborative detection network;

[0053] Figure 4 This is a schematic diagram comparing the sensing performance of the method of the present invention with other methods;

[0054] Figure 5 This is a structural diagram of the device according to an embodiment of the present invention;

[0055] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0057] Reference Figure 1 This invention provides a method for deploying a multi-target cooperative detection network based on a drone swarm, comprising the following steps:

[0058] S1: Based on the free space propagation model, calculate the comprehensive energy detection probability of the UAV swarm for multiple targets;

[0059] As one example:

[0060] In the free-space propagation model, the number of drones is M, and the position of the i-th drone is q. i =[x i y i H] T , (x i y i The coordinates (x and y) represent the location, H represents the drone's altitude, K represents the number of targets to be sensed, and the location of the k-th target is... The distance from drone i to target k is:

[0061] Specifically, free space propagation models such as Figure 2 As shown in the figure, the drone swarm includes UAV1 to UAVM, where UAV1 corresponds to targets Target1 and Target2, and UAVM corresponds to targets TargetK-1 and TargetK.

[0062] As one example:

[0063] Step S1 is as follows:

[0064] S11: Based on the free-space propagation model, calculate the path loss between the UAV and the target;

[0065] Specifically:

[0066] Path loss PL between UAV i and target k i，k The formula for calculating (η) is:

[0067] PL i,k (η)=20lg(f k )+20lg(d i，k )+32.4+η

[0068] Where, η∈{η L η N} represents the attenuation exponent of the LoS and NLoS links, f k The channel frequency of target k is represented;

[0069] S12: Calculate the channel gain signal-to-noise ratio between the UAV and the target using path loss calculation;

[0070] Specifically:

[0071] Channel gain g between UAV i and target k i,k The formula for calculating (η) is:

[0072]

[0073] Channel gain signal-to-noise ratio γ between UAV i and target k i,k (q i The formula for calculating ) is:

[0074]

[0075] Where, p k σ is the target signal transmission power. 2 Noise power;

[0076] S13: Calculate the probability of detecting a single energy of the UAV and the target within the channel frequency range using the channel gain signal-to-noise ratio.

[0077] Specifically:

[0078] The probability P of a single energy detection of drone i against target k i，k (q i The formula for calculating ) is:

[0079]

[0080] Where, γ i，k Let P be the channel gain signal-to-noise ratio, Q be the right tail function of the standard normal distribution, N be the number of received signal samples, and P be the signal-to-noise ratio. fa The false alarm probability of the sensing system;

[0081] The channel frequency range of UAV i is obtained as follows: Then the drone i and the target k are in the channel frequency f k Single energy detection probability within the channel frequency range The calculation formula is:

[0082]

[0083] in, This represents the number of received signal samples within the channel frequency range.

[0084] S14: Repeat steps S11-S13 to obtain all individual energy detection probabilities;

[0085] S15: Calculate the overall energy detection probability of the UAV swarm against multiple targets by using the individual energy detection probabilities.

[0086] Specifically:

[0087] The overall energy detection probability of a drone swarm targeting a target k within the channel frequency range fk. The calculation formula is:

[0088]

[0089] S2: Based on the comprehensive energy detection probability, construct a model for the deployment of a multi-target cooperative detection network based on UAV swarms;

[0090] As one example:

[0091] Step S2 is as follows:

[0092] A total detection probability sum function is constructed based on the comprehensive energy detection probability to measure the cooperative perception accuracy of UAV swarms for multiple targets. The total detection probability sum function P ED The expression for (Q) is:

[0093]

[0094] Among them, F′ k Let Δf be the carrier frequency. k For channel spacing, For a swarm of drones targeting target k at channel frequency F′ k +i×Δf k The overall energy detection probability within the channel frequency range;

[0095] A model for the deployment of a multi-target cooperative detection network based on a drone swarm is constructed using the total detection probability and function, expressed as follows:

[0096]

[0097] Where i and j are the drone's serial numbers, Let D be the set of drone locations, and r be the deployable area. min R is the minimum safe distance between drones and ground threats. h R represents the maximum communication distance between drones. d This represents the minimum safe distance between any two drones.

[0098] S3: Initially solve the multi-target cooperative detection network deployment problem model using spectral clustering algorithm to obtain the initial set of UAV locations;

[0099] Specifically, the spectral clustering algorithm is used to initially solve the multi-target cooperative detection network deployment problem model, determine the center point of the UAV swarm based on the spatial distribution of ground targets, and obtain the initial UAV position set after further analysis.

[0100] S4: The optimal UAV location set is obtained by iteratively solving the multi-target cooperative detection network deployment problem model through the initial UAV location set and the Gibbs sampling algorithm.

[0101] As one example:

[0102] Step S4 is as follows:

[0103] S41: Set the iteration count l to 1, and the initial drone position set Q 1 Input a multi-target cooperative detection network deployment problem model;

[0104] S42: Perform the l-th iteration, calculate the transition probability of each UAV using the Gibbs sampling algorithm, update the position of each UAV using the transition probability, and obtain the UAV position set Q for the l-th iteration. l ;

[0105] Specifically, under the condition of satisfying the drone deployment location constraints, the position of each drone is updated sequentially, while the positions of other drones remain fixed. The formula for calculating the transition probability Pr is:

[0106]

[0107] Where t represents the drone's operating time;

[0108] S43: Let l = l + 1;

[0109] S44: Repeat steps S42-S43 until the total detection probability and function P are reached. ED (Q) When convergence or the maximum number of iterations is reached, the final set of drone locations is taken as the optimal set of drone locations.

[0110] Specifically, taking a drone swarm of 2 and 6 target objects as an example, the deployment diagram of the collaborative detection network constructed using the optimal drone location set is as follows: Figure 3 As shown, Figure 4 The perception performance of the multi-target cooperative detection network deployment method based on UAV swarms of the present invention is compared with traditional and benchmark algorithms. Specific parameter settings: the channel frequency sets of ground targets are {100,125,150}, {175,200,225}, {175,200,225,250,275,300,325}, {225,250,275}, {325,350}, and {350,375,400}, respectively, and the transmit power is 0.05W; the channel frequency ranges of the two UAVs are [100, 250] and [250, 400], respectively.

[0111] Figure 4 A comparison chart of system perception performance is presented, where the vertical axis represents the sensing performance and total detection probability of the collaborative detection network, and the horizontal axis represents the transmission power of the ground target. The chart shows that the sensing performance gradually improves as the transmission power of the ground target increases. Furthermore, for the same transmission power and network scenario, the system performance of the method described in this invention is better than other methods.

[0112] Simulation performance comparisons demonstrate that the method of this invention achieves effective deployment of a multi-target cooperative detection network for UAV swarms. This method achieves optimal cooperative sensing performance, including sensing accuracy and sensitivity, even under resource constraints such as UAV swarm size, deployment range, and computational limitations. It is foreseeable that this method will be well-suited to future wireless sensing technologies, significantly improving the performance of cooperative detection networks.

[0113] Please see Figure 5 , Figure 5This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a multi-target collaborative detection network deployment device 401 based on a drone swarm, a processor 402, and a storage medium 403.

[0114] A multi-target collaborative detection network deployment device 401 based on UAV swarms: The multi-target collaborative detection network deployment device 401 based on UAV swarms implements the multi-target collaborative detection network deployment method based on UAV swarms.

[0115] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the multi-target cooperative detection network deployment method based on UAV swarm.

[0116] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the multi-target cooperative detection network deployment method based on UAV swarm.

[0117] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0118] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the unit claims listing several devices, several of these devices may be embodied by the same hardware item. The use of the terms first, second, and third, etc., does not indicate any order and can be interpreted as identifiers.

[0119] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for deploying a multi-target cooperative detection network based on an unmanned aerial vehicle (UAV) swarm, characterized in that, Including the following steps: S1: Based on the free space propagation model, calculate the comprehensive energy detection probability of the UAV swarm for multiple targets; S2: Based on the comprehensive energy detection probability, construct a model for the deployment of a multi-target cooperative detection network based on UAV swarms; S3: Initially solve the multi-target cooperative detection network deployment problem model using spectral clustering algorithm to obtain the initial set of UAV locations; S4: The optimal UAV location set is obtained by iteratively solving the multi-target cooperative detection network deployment problem model using the initial UAV location set and the Gibbs sampling algorithm; In the free-space propagation model, the number of drones is M, and the position of the i-th drone is q. i =[x i ,y i [H] T , (x i ,y i The coordinates (x and y) represent the location, H represents the drone's altitude, K represents the number of targets to be sensed, and the location of the k-th target is... The distance from drone i to target k is: Step S2 is as follows: A total detection probability sum function is constructed based on the comprehensive energy detection probability to measure the cooperative perception accuracy of UAV swarms for multiple targets. The total detection probability sum function P ED The expression for (Q) is: Among them, F′ k Let Δf be the carrier frequency. k For channel spacing, For a swarm of drones targeting target k at channel frequency F′ k +i×Δf k The overall energy detection probability within the channel frequency range; A model for the deployment of a multi-target cooperative detection network based on a drone swarm is constructed using the total detection probability and function, expressed as follows: Where i and j are the drone's serial numbers, Let D be the set of drone locations, and r be the deployable area. min R is the minimum safe distance between drones and ground threats. h R represents the maximum communication distance between drones. d This represents the minimum safe distance between any two drones.

2. The method for deploying a multi-target cooperative detection network based on an unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, Step S1 is as follows: S11: Based on the free-space propagation model, calculate the path loss between the UAV and the target; S12: Calculate the channel gain signal-to-noise ratio between the UAV and the target using path loss calculation; S13: Calculate the probability of detecting a single energy of the UAV and the target within the channel frequency range using the channel gain signal-to-noise ratio. S14: Repeat steps S11-S13 to obtain all individual energy detection probabilities; S15: Calculate the overall energy detection probability of the UAV swarm against multiple targets by using the individual energy detection probabilities.

3. The method for deploying a multi-target cooperative detection network based on an unmanned aerial vehicle (UAV) swarm according to claim 2, characterized in that: Path loss PL between UAV i and target k i,k The formula for calculating (η) is: EN i,k (η)=20lg(f) k )+20lg(d i,k )+32.4+η Where, η∈{η L ,η N } represents the attenuation exponent of the LoS and NLoS links, f k The channel frequency of target k is represented; Channel gain g between UAV i and target k i,k The formula for calculating (η) is: Channel gain signal-to-noise ratio γ between UAV i and target k i,k (q i The formula for calculating ) is: Where, p k σ is the target signal transmission power. 2 Noise power; The probability P of a single energy detection of drone i against target k i,k (q i The formula for calculating ) is: Where, γ i,k Let P be the channel gain signal-to-noise ratio, Q be the right tail function of the standard normal distribution, N be the number of received signal samples, and P be the signal-to-noise ratio. fa The false alarm probability of the sensing system; The channel frequency range of UAV i is obtained as follows: Then the drone i and the target k are in the channel frequency f k Single energy detection probability within the channel frequency range The calculation formula is: in, This represents the number of received signal samples within the channel frequency range. A swarm of drones targets a target k at channel frequency f k Comprehensive energy detection probability within the channel frequency range The calculation formula is:

4. The method for deploying a multi-target cooperative detection network based on an unmanned aerial vehicle swarm according to claim 1, characterized in that, Step S4 is as follows: S41: Set the iteration count l to 1, and the initial drone position set Q 1 Input a multi-target cooperative detection network deployment problem model; S42: Perform the l-th iteration, calculate the transition probability of each UAV using the Gibbs sampling algorithm, update the position of each UAV using the transition probability, and obtain the UAV position set Q for the l-th iteration. l ; S43: Let l = l + 1; S44: Repeat steps S42-S43 until the total detection probability and function P are reached. ED (Q) When convergence or the maximum number of iterations is reached, the final set of drone locations is taken as the optimal set of drone locations.

5. A storage medium, characterized in that: The storage medium stores instructions and data to implement the multi-target collaborative detection network deployment method based on UAV swarms as described in any one of claims 1 to 4.

6. A multi-target cooperative detection network deployment device based on UAV swarms, characterized in that: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the multi-target cooperative detection network deployment method based on UAV swarms as described in any one of claims 1 to 4.

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