Unmanned aerial vehicle cooperative sensing method and system based on multi-source grey correlation data fusion

Abstract

The invention provides an unmanned aerial vehicle cooperative sensing method and system based on multi-source grey correlation data fusion, and belongs to the technical field of multi-unmanned aerial vehicle cooperative reconnaissance and feature fusion. According to the method, a multi-station data fusion method is utilized to perform data association, fusion and optimization on radiation source signals acquired by multiple unmanned aerial vehicles, so that cooperative reconnaissance among the multiple unmanned aerial vehicles in the field of electronic countermeasures is realized, and high-precision radiation source signal parameters can be stably acquired in a multi-interference environment; compared with the prior art that only single unmanned aerial vehicle system reconnaissance is carried out and traditional multi-unmanned aerial vehicle reconnaissance results are directly and averagely fused, multi-station data fusion optimization is utilized, high-precision radiation source signal measurement and information association under a multi-station data fusion framework are achieved, the effect of electronic confrontation reconnaissance is greatly improved, and the method is suitable for large-scale popularization and application. And higher requirements on reconnaissance precision and efficiency in a modern electronic warfare environment are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-UAV cooperative reconnaissance and feature fusion, and specifically relates to a UAV cooperative perception method and system based on multi-source grey correlation data fusion. Background Art

[0002] With the rapid development of electronic warfare technology, modern warfare has entered a highly information-based electronic warfare era. As the core of electronic warfare, electronic reconnaissance uses electronic reconnaissance equipment to intercept electromagnetic signals sent by enemy radars or other communication equipment in space, analyze the characteristics of the signals, obtain the enemy's electromagnetic information, provide target guidance for electronic attack and defense, and provide intelligence sources for battlefield perception.

[0003] Unmanned Aerial Vehicle (UAV) has the advantages of small size, low cost, high concealment, etc., and can perform more complex and dangerous tasks. UAV can effectively improve the efficiency and quality of reconnaissance, play an increasingly important role in the reconnaissance system, and is an important development direction of future reconnaissance.

[0004] In the complex and ever-changing battlefield, the rapid development of drone technology has brought great changes to modern military strategy. However, in the scenario of sudden electronic reconnaissance network intervention, the traditional single drone reconnaissance is inflexible and limited in function, which easily leads to blind spots in signal capture and analysis. Especially in the case of frequent changes in signal sources or multi-source interference, the detection accuracy and response speed of traditional single drones are difficult to meet the requirements. In this regard, drone collaborative electronic reconnaissance has been proposed and received widespread attention.

[0005] UAV cluster collaborative electronic reconnaissance can comprehensively consider the electronic reconnaissance results of multiple UAV platforms, effectively reduce the impact of complex electromagnetic environment, interference, fading, etc. on electronic reconnaissance results, and enhance the reliability of detection results. Multi-platform electronic reconnaissance data fusion is the core of UAV cluster collaborative electronic reconnaissance, and higher accuracy is achieved by weighted fusion of reconnaissance results of different UAVs. However, traditional multi-UAV data fusion methods often set the weights of data fusion through experience or directly perform average weighted fusion. This method gradually exposes its limitations in the face of modern warfare scenarios. Especially when dealing with dynamic, nonlinear and complex electromagnetic environments, traditional data fusion technology often lacks adaptability to environmental changes, which makes the judgment results of joint electronic reconnaissance have greater uncertainty. Therefore, it is very necessary to improve the existing data fusion algorithm. Summary of the invention

[0006] In view of the shortcomings of the existing technology of traditional multi-UAV data fusion, the present invention proposes a UAV collaborative perception method and system based on multi-source grey correlation data fusion. In view of the inflexibility of single UAV reconnaissance and the weak robustness of traditional single data fusion, a method is designed to associate the radiation source parameter data measured by multiple UAVs, introduce grey correlation and data fusion optimization algorithm based on parameter measurement error, process the radiation source parameters, realize multi-UAV collaborative reconnaissance, and optimize the problems of low efficiency and low precision of single UAV in the field of electronic reconnaissance.

[0007] A UAV collaborative perception method based on multi-source grey correlation data fusion, comprising:

[0008] Step 1: Assume that there are n drones and m radiation sources in the multi-drone collaborative sensing system;

[0009] Step 2, each drone independently receives the signal from the radiation source and measures the radiation source parameters;

[0010] Step 3, calculate the grey correlation between the radiation source parameters of different drones; according to the correlation, associate the same radiation source parameters measured by different drones;

[0011] Step 4: fuse the parameters of the same radiation source of the associated multiple UAVs to achieve multi-UAV collaborative perception.

[0012] Preferably, the step 3 includes:

[0013] Take one of the UAVs as the main UAV and the others as the secondary UAVs, and calculate the parameter sequence of the ath radiation source of the main UAV and X ij The correlation degree in the bth parameter dimension is:

[0014]

[0015] Where a=1,2,…m; X ij represents the jth radiation source parameter sequence of the i-th secondary UAV, i = 2, 3…n and j = 1, 2,…m; △ a (b) represents the parameter sequence of the a-th radiation source of the main UAV and X ij The absolute difference of the bth parameter; k represents the total number of radiation source parameters; b = 1, 2, ... k; The minimum difference between the two levels of the hth parameter dimension in the measurement data sequence of the main UAV and the measurement data sequence of the i-th secondary UAV, represents the maximum difference between the two levels of the hth parameter dimension in the measurement data sequence of the master UAV and the measurement data sequence of the ith slave UAV; h = 1, 2, ... k; ρ∈(0, +∞) is the resolution coefficient;

[0016] Get the parameter sequence of the ath radiation source of the main UAV and the parameter sequence X ij The total correlation across all parameter dimensions:

[0017]

[0018] Thus, the correlation matrix between each self-radiation source parameter sequence of the main UAV and each radiation source parameter sequence of the i-th secondary UAV is obtained:

[0019] ξ={ξ 1 ,ξ 2 …ξ m};

[0020] Find the maximum value in the correlation matrix, and assume that the radiation source number of the maximum value is x. Then the x-th radiation source of the main UAV is associated with the parameter sequence of the current radiation source of the ith secondary UAV; and so on, the radiation source parameters measured by different UAVs are associated.

[0021] Preferably, in step 4, the resolution coefficient ρ is taken as 0.5.

[0022] Preferably, in step 4, fusing the observation data of the same radiation source parameter of the associated multiple UAVs comprises:

[0023] The fusion weight of each UAV is determined, and the weight is used to perform weighted summation on the observation data of each UAV on the same radiation source to obtain the fused data.

[0024] Preferably, in step 4, the fusion weight of each UAV is calculated based on the variance of the estimated parameters of each UAV.

[0025] Preferably, in step 4, the fusion weight ω of each drone is calculated based on the variance of the estimated parameters of each drone. i The formula is:

[0026]

[0027] Among them, σ i Represents the measurement variance of the radiation source parameters by the i-th UAV.

[0028] A UAV collaborative perception system based on multi-source grey correlation data fusion, comprising a first module, a second module, a third module and a fourth module;

[0029] The first module sets parameters, including: there are n drones and m radiation sources in the multi-drone collaborative perception system;

[0030] The second module stores the radiation source parameters measured by each drone independently receiving the signal from the radiation source;

[0031] The third module calculates the grey correlation between the radiation source parameters of different drones; and according to the correlation, associates the same radiation source parameters measured by different drones;

[0032] The fourth module fuses the parameters of the same radiation source of the associated multiple UAVs to achieve multi-UAV collaborative perception.

[0033] Preferably, the third module calculates the grey correlation between the radiation source parameters of different drones; and according to the correlation, the method of associating the same radiation source parameters measured by different drones comprises:

[0034] Take one of the UAVs as the main UAV and the others as the secondary UAVs, and calculate the parameter sequence of the ath radiation source of the main UAV and X ij The correlation degree in the bth parameter dimension is:

[0035]

[0036] Where a=1,2,…m; X ij represents the jth radiation source parameter sequence of the i-th secondary UAV, i = 2, 3…n and j = 1, 2,…m; △ a (b) represents the parameter sequence of the a-th radiation source of the main UAV and X ij The absolute difference of the bth parameter; k represents the total number of radiation source parameters; b = 1, 2, ... k; The minimum difference between the two levels of the hth parameter dimension in the measurement data sequence of the main UAV and the measurement data sequence of the i-th secondary UAV, represents the maximum difference between the two levels of the hth parameter dimension in the measurement data sequence of the master UAV and the measurement data sequence of the ith slave UAV; h = 1, 2, ... k; ρ∈(0, +∞) is the resolution coefficient;

[0037] Get the parameter sequence of the ath radiation source of the main UAV and the parameter sequence X ij The total correlation across all parameter dimensions:

[0038]

[0039] Thus, the correlation matrix between each self-radiation source parameter sequence of the main UAV and each radiation source parameter sequence of the i-th secondary UAV is obtained:

[0040] ξ={ξ 1 ,ξ 2 …ξ m};

[0041] Find the maximum value in the correlation matrix, and assume that the radiation source number of the maximum value is x. Then the x-th radiation source of the main UAV is associated with the parameter sequence of the current radiation source of the ith secondary UAV; and so on, the radiation source parameters measured by different UAVs are associated.

[0042] The present invention has the following beneficial effects:

[0043] Aiming at the problems of low efficiency and poor accuracy of signal parameter processing of a single UAV in electronic countermeasure reconnaissance mission, the present invention proposes a UAV collaborative perception method and system based on multi-source grey correlation data fusion. The multi-station data fusion method is used to perform data association, fusion and optimization on the radiation source signals collected by multiple UAVs, thereby realizing collaborative reconnaissance between multiple UAVs in the field of electronic countermeasures, and stably obtaining high-precision radiation source signal parameters in a multi-interference environment.

[0044] Compared with the prior art of only conducting reconnaissance of a single UAV system and the traditional direct average fusion of multi-UAV reconnaissance results, the present invention utilizes multi-station data fusion optimization to achieve high-precision radiation source signal measurement and information association under the multi-station data fusion framework, greatly improving the effect of electronic countermeasure reconnaissance and adapting to the higher requirements for reconnaissance accuracy and efficiency in modern electronic warfare environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the multi-UAV collaborative perception system model in the present invention;

[0046] Figure 2 It is a flow chart of a UAV collaborative perception method based on multi-source grey correlation data fusion of the present invention;

[0047] Figure 3 This is the curve of the measurement error of the radiation source carrier frequency changing with the signal-to-noise ratio under different algorithms;

[0048] Figure 4 This is the curve of the radiation source pulse width measurement error changing with the signal-to-noise ratio under different algorithms;

[0049] Figure 5 This is the curve of the radiation source bandwidth measurement error changing with the signal-to-noise ratio under different algorithms;

[0050] Figure 6 This is the curve of the measurement error of the radiation source pulse repetition period changing with the signal-to-noise ratio under different algorithms. DETAILED DESCRIPTION

[0051] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0052] The purpose of the present invention is to propose a UAV collaborative perception method based on multi-source grey correlation data fusion, which mainly includes the following steps:

[0053] Step 1: Design a multi-UAV collaborative perception system model. Specifically, the multi-UAV collaborative perception system consists of n UAVs and m radiation sources, where the n UAVs are divided into 1 main UAV and n-1 secondary UAVs, and each UAV can independently receive signals and measure radiation source parameters.

[0054] Step 2: Each UAV uses the signals received by each UAV to obtain the relevant parameters of the radiation source; specifically, each UAV will receive the signals emitted by m radiation sources respectively, and independently estimate the parameters of the received signals;

[0055] Step 3: Correlate the radiation source parameter data measured by different drones; calculate the grey correlation between the measurement data of radiation sources by different drones. According to the correlation, the same or similar radiation source parameters measured by different drones are correlated to achieve effective aggregation of the same radiation source;

[0056] Step 4, fuse the associated radiation source parameters; use the improved data fusion algorithm to fuse the associated radiation source parameters. First, introduce the variance of each UAV parameter estimate to calculate the fusion weight of each UAV, and then use the weight to integrate the observation data of multiple UAVs on the same radiation source, ultimately achieving the purpose of reducing errors and improving parameter measurement accuracy.

[0057] In step 1, there are n drones and m radiation sources in the multi-drone cooperative perception system, where n drones are divided into 1 main drone and n-1 secondary drones, and each drone can independently receive signals and measure radiation source parameters. At the beginning of the system, multiple drones will sense multiple radiation sources at the same time, and each drone will independently intercept the radiation source signal.

[0058] In step 2, the drone will independently pre-process the intercepted signal to obtain the radiation source parameter information, which generally includes parameters such as pulse repetition interval (PRI), radio frequency (RF), pulse width (PW) and bandwidth (BW). Taking drone 1 as an example, its perception result can be expressed as:

[0059]

[0060] Where, X 11 and X 12represents the parameter measurement results of UAV 1 and UAV 2 for the first radiation source they perceive, and so on; let k represent the k parameters of the radiation source, and the matrix X 11 Medium X 11 (k) represents the kth parameter data corresponding to the first radiation source measured by UAV 1;

[0061] The n UAV perception result sequences form the following matrix:

[0062]

[0063] Where, X i Represents the sequence of reconnaissance results of the i-th drone.

[0064] In step 3, the system associates the radiation source parameters measured by different drones. Specifically, drone 1 is the main drone, and the rest are slave drones. The radiation sources sensed by the slave drones are associated with the main drone in turn, with the jth radiation source X of the i-th slave drone being the ij For example, where i = 2, 3, ... n and j = 1, 2, ... m.

[0065] First calculate X ij The absolute difference between the main UAV radiation source parameter sequence and the absolute difference matrix is ​​obtained:

[0066]

[0067] In the formula, △ a (b) represents the parameter sequence of the a-th radiation source of the main UAV and X ij The absolute difference of the bth parameter is:

[0068] △ a (b) = |X 1a (b)-X ij (b)|, a=1,2,…m and b=1,2,…k

[0069] Furthermore, calculate X ij The correlation between the parameter sequences of the radiation sources of the main UAV and X ij The correlation degree in the bth parameter dimension is:

[0070]

[0071] In the formula, Called X 1 With X i The minimum difference between the two levels of the jth parameter, is the maximum difference between the two levels. ρ∈(0,+∞) is the resolution coefficient, which controls ξ iThe smaller ρ is, the greater the resolution is. When ρ tends to infinity, the correlation coefficient approaches 1. At this time, correlation analysis cannot be performed. It is generally taken as 0.5.

[0072] Similarly, the parameter sequence of the ath radiation source of the main UAV and X ij Total correlation across all dimensions:

[0073]

[0074] Thus, the correlation matrix between each self-radiation source parameter sequence of the main UAV and each radiation source parameter sequence of the i-th secondary UAV can be obtained:

[0075] ξ={ξ 1 ,ξ 2 …ξ m}

[0076] Find the maximum value in the correlation matrix, and assume that the radiation source number of the maximum value is x. Then the x-th radiation source of the main UAV is associated with the parameter sequence of the current radiation source of the ith secondary UAV; and so on. This method is used to finally associate the radiation source parameters measured by different UAVs.

[0077] In step 4, the parameter measurement variance of each drone is introduced to obtain the optimal weighted fusion coefficient. Taking frequency measurement as an example, the variance of frequency measurement of n drones is In this case, each drone measures a radiation source with a frequency of f 1 ,f 2 ,…f n In order to obtain the optimal measurement frequency, the weighting coefficient is calculated using the measurement variance of each UAV.

[0078] Assume that the weighting coefficients of each drone are ω 1 ,ω 2 ,…ω n , then the parameter value after fusion should be:

[0079]

[0080] In the formula, the weighting coefficient should satisfy:

[0081]

[0082] In order to achieve the highest measurement accuracy, the variance of the fused parameters should be minimized. The variance of the fused parameters is:

[0083]

[0084] Since the parameters of each drone are measured independently, there are:

[0085] E[(ffi )(ff j )]=0

[0086] Therefore, we can get:

[0087]

[0088] In order to minimize the variance, construct the Lagrangian function:

[0089]

[0090] Yes i ,λ, and find the partial derivative to get

[0091]

[0092] Finally, the optimal weight fusion coefficient can be obtained:

[0093]

[0094] The obtained weight coefficients are used to fuse the frequency parameters measured by each UAV:

[0095]

[0096] Similarly, this method can be used to perform weighted fusion on other parameters to obtain radiation source parameters with higher accuracy.

[0097] Based on the above-mentioned UAV collaborative perception method based on multi-source grey correlation data fusion, the present invention also provides a UAV collaborative perception system based on multi-source grey correlation data fusion, including a first module, a second module, a third module and a fourth module.

[0098] The first module assumes that there are n drones and m radiation sources in the multi-drone collaborative reconnaissance system;

[0099] The second module stores the radiation source parameters measured by each drone independently receiving the signal from the radiation source;

[0100] The third module calculates the grey correlation between the radiation source parameters of different drones; and according to the correlation, associates the same radiation source parameters measured by different drones;

[0101] The fourth module fuses the parameters of the same radiation source of the associated multiple UAVs to achieve multi-UAV collaborative reconnaissance.

[0102] Example:

[0103] In order to verify the effectiveness of the solution of the present invention, the following simulation experiment is carried out.

[0104] First, the scenario of the embodiment is briefly introduced. It is assumed that there are three UAVs and five radiation sources in the scenario. Each UAV receives signals independently and measures parameters. The grey correlation theory and the improved data fusion method are used to correlate and merge the perception results of each UAV. The actual parameters of the five radiation sources are shown in Table 1.

[0105] Table 1 Radiation source parameters

[0106]

[0107] Figure 3 The curves of the root mean square error of the carrier frequency measurement of the radiation source with the signal-to-noise ratio under different algorithms are given. These curves respectively represent the carrier frequency measurement error change curves of the three UAVs, the carrier frequency measurement error change curves after weighted average fusion of the measurement results of the three UAVs, and the carrier frequency measurement error change curves after data fusion using the method of the present invention. It can be seen from the figure that in the single UAV carrier frequency measurement, the measurement error of UAV 1 is the lowest, while the measurement error of UAV 3 is relatively low. In the case of high signal-to-noise ratio, the performance of the average weighted algorithm is better than that of the three UAVs measured separately, while in the case of low signal-to-noise ratio, the description of the carrier frequency of the radiation source by the average weighted algorithm is not as accurate as that of UAV 3 measured separately. In contrast, the algorithm of the present invention can fully consider the parameter measurement accuracy of each UAV, and can reach the highest under different signal-to-noise ratios.

[0108] Figure 4 The curves of the pulse width measurement error of the radiation source with the signal-to-noise ratio under different algorithms are given. It can be seen from the figure that the measurement accuracy of the pulse width of UAV 1 is lower than that of other UAVs, while the measurement accuracy of UAV 3 is higher. Due to the large measurement error of UAV 1, the performance of the weighted average algorithm is relatively reduced, resulting in the accuracy of the weighted average algorithm being inferior to that of the single measurement of UAV 3. In contrast, the measurement error of the method of the present invention is the smallest among all methods at any signal-to-noise ratio.

[0109] Figure 5 The curves of the radiation source bandwidth measurement error and signal-to-noise ratio under different algorithms are given. It can be seen from the figure that the measurement accuracy of the pulse bandwidth of UAV 2 is lower than that of other UAVs, while the measurement accuracy of UAV 3 is higher. However, since the weighted average algorithm is greatly affected by the data measured by UAV 2, its accuracy is not as good as that of the single measurement of UAV 3. In contrast, the method of the present invention shows higher measurement accuracy.

[0110] Figure 6The curves of the measurement error of the pulse repetition period of the radiation source with the signal-to-noise ratio under different algorithms are given. It can be seen from the figure that the measurement accuracy of the pulse repetition frequency of UAV 3 is lower than that of other UAVs, and that of UAV 1 is higher. The accuracy of the weighted average algorithm is worse than that of UAVs 2 and 3, but not as good as that of UAV 1.

[0111] The simulation results verify the superiority of the method of the present invention. It can be seen that the algorithm of the present invention has greatly improved the estimation effect of various parameters, and its performance under all signal-to-noise ratios is better than the weighted average method and the separate measurement of three UAVs, showing a strong anti-interference ability.

[0112] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A UAV collaborative perception method based on multi-source grey correlation data fusion, characterized in that: include: Step 1: Assume that there are n drones and m radiation sources in the multi-drone collaborative sensing system; Step 2, each drone independently receives the signal from the radiation source and measures the radiation source parameters; Step 3, calculate the grey correlation between the radiation source parameters of different drones; according to the correlation, associate the same radiation source parameters measured by different drones; Step 4: fuse the parameters of the same radiation source of the associated multiple UAVs to achieve multi-UAV collaborative perception.

2. The UAV collaborative perception method based on multi-source grey correlation data fusion according to claim 1 is characterized in that: The step 3 includes: Take one of the UAVs as the main UAV and the others as the secondary UAVs, and calculate the parameter sequence of the ath radiation source of the main UAV and X ij The correlation degree in the bth parameter dimension is: Where a=1,2,…m; X ij represents the jth radiation source parameter sequence of the i-th secondary UAV, i = 2, 3…n and j = 1, 2,…m; △ a (b) represents the parameter sequence of the a-th radiation source of the main UAV and X ij The absolute difference of the bth parameter; k represents the total number of radiation source parameters; b = 1, 2, ... k; The minimum difference between the two levels of the hth parameter dimension in the measurement data sequence of the main UAV and the measurement data sequence of the i-th secondary UAV, represents the maximum difference between the two levels of the hth parameter dimension in the measurement data sequence of the master UAV and the measurement data sequence of the ith slave UAV; h = 1, 2, ... k; ρ∈(0, +∞) is the resolution coefficient; Get the parameter sequence of the ath radiation source of the main UAV and the parameter sequence X ij The total correlation across all parameter dimensions: Thus, the correlation matrix between each self-radiation source parameter sequence of the main UAV and each radiation source parameter sequence of the i-th secondary UAV is obtained: ξ={ξ1,ξ2…ξ m }; Find the maximum value in the correlation matrix, and assume that the radiation source number of the maximum value is x. Then the x-th radiation source of the main UAV is associated with the parameter sequence of the current radiation source of the ith secondary UAV; and so on, the radiation source parameters measured by different UAVs are associated.

3. The UAV collaborative perception method based on multi-source grey correlation data fusion as claimed in claim 2 is characterized in that: In step 4, the resolution coefficient ρ is taken as 0.

5.

4. The UAV collaborative perception method based on multi-source grey correlation data fusion as described in claim 1, 2 or 3, characterized in that: In step 4, fusing the observation data of the same radiation source parameter of the associated multiple UAVs includes: The fusion weight of each UAV is determined, and the weight is used to perform weighted summation on the observation data of each UAV on the same radiation source to obtain the fused data.

5. The UAV collaborative perception method based on multi-source grey correlation data fusion as claimed in claim 4 is characterized in that: In step 4, the fusion weight of each UAV is calculated based on the variance of the estimated parameters of each UAV.

6. The UAV collaborative perception method based on multi-source grey correlation data fusion as claimed in claim 5, characterized in that: In step 4, the fusion weight ω of each drone is calculated based on the variance of the estimated parameters of each drone. i The formula is: Among them, σ i Represents the measurement variance of the radiation source parameters by the i-th UAV.

7. A UAV collaborative perception system based on multi-source grey correlation data fusion, characterized in that: It includes a first module, a second module, a third module and a fourth module; The first module sets parameters, including: there are n drones and m radiation sources in the multi-drone collaborative perception system; The second module stores the radiation source parameters measured by each drone independently receiving the signal from the radiation source; The third module calculates the grey correlation between the radiation source parameters of different drones; and according to the correlation, associates the same radiation source parameters measured by different drones; The fourth module fuses the parameters of the same radiation source of the associated multiple UAVs to achieve multi-UAV collaborative perception.

8. The UAV collaborative perception system based on multi-source grey correlation data fusion as claimed in claim 7, characterized in that: The third module calculates the grey correlation between the radiation source parameters of different drone pairs; According to the degree of correlation, the methods for associating the same radiation source parameters measured by different drones include: Take one of the UAVs as the main UAV and the others as the secondary UAVs, and calculate the parameter sequence of the ath radiation source of the main UAV and X ij The correlation degree in the bth parameter dimension is: Where a=1,2,…m; X ij represents the jth radiation source parameter sequence of the i-th secondary UAV, i = 2, 3…n and j = 1, 2,…m; △ a (b) represents the parameter sequence of the a-th radiation source of the main UAV and X ij The absolute difference of the bth parameter; k represents the total number of radiation source parameters; b = 1, 2, ... k; The minimum difference between the two levels of the hth parameter dimension in the measurement data sequence of the main UAV and the measurement data sequence of the i-th secondary UAV, represents the maximum difference between the two levels of the hth parameter dimension in the measurement data sequence of the master UAV and the measurement data sequence of the ith slave UAV; h = 1, 2, ... k; ρ∈(0, +∞) is the resolution coefficient; Get the parameter sequence of the ath radiation source of the main UAV and the parameter sequence X ij The total correlation across all parameter dimensions: Thus, the correlation matrix between each self-radiation source parameter sequence of the main UAV and each radiation source parameter sequence of the i-th secondary UAV is obtained: ξ={ξ1,ξ2…ξ m }; Find the maximum value in the correlation matrix, and assume that the radiation source number of the maximum value is x. Then the x-th radiation source of the main UAV is associated with the parameter sequence of the current radiation source of the ith secondary UAV; and so on, the radiation source parameters measured by different UAVs are associated.

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