Weak cyclostationary signal detection method based on unmanned aerial vehicle passive radar network

Abstract

The invention discloses a weak cyclostationary signal detection method based on an unmanned aerial vehicle-mounted passive radar network, and the method comprises the steps: firstly building an active radar transmitting signal and unmanned aerial vehicle passive radar receiving signal model, and then building a binary hypothesis model based on cyclostationary generalized likelihood ratio detection through block cyclic matrix approximation; and constructing generalized likelihood ratio test detection statistics and obtaining local detection results of the unmanned aerial vehicles under a colored noise background by using the cyclostationary characteristic of target echoes, finally fusing the local detection results of the unmanned aerial vehicles based on a Bayesian minimum risk criterion at a fusion center, and outputting global detection judgment. And a detection process of cyclostationary generalized likelihood ratio test-Bayesian minimum risk fusion is formed. According to the method, the detection statistics are constructed by using the cyclostationary characteristics of the target echoes, the global detection accuracy and robustness can be improved under the conditions of low signal-to-noise ratio and complex colored noise, and the method is suitable for a multi-unmanned aerial vehicle cooperative passive radar detection scene.

Description

Technical Field

[0001] This invention belongs to the field of radar target detection technology, specifically relating to a method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network. Background Technology

[0002] In recent years, the activity of low-altitude, slow-moving, and small targets in urban clusters, coastal areas, and complex electromagnetic environments has been increasing. These targets have small cross-sections, high maneuverability, and are easily superimposed with ground clutter and interference signals, leading to a significant decline in the detection performance of existing energy detection and matched filtering methods under low signal-to-noise ratio conditions. Meanwhile, passive radar utilizes external radiation sources such as broadcast television and communication base stations to achieve "passive covert detection," offering advantages such as resistance to anti-radiation attacks and flexible spectrum utilization, making it suitable for rapid deployment and large-scale surveillance of UAV platforms. However, its echoes are usually affected by the modulation structure of the illumination source, propagation multipath, platform motion, and non-stationary clutter, exhibiting significant correlation and non-Gaussian characteristics. This makes it difficult for classical adaptive detectors based on the assumption of independent and identically distributed Gaussian noise to obtain stable gain.

[0003] Cyclic stationary signal detection can utilize the inherent periodic statistical characteristics of the illumination source and echo to achieve noise suppression gain under conditions where noise and clutter are insensitive to the cyclic frequency, and is considered an important approach for passive detection of weak targets. However, in UAV-borne scenarios, due to factors such as limited observation windows, insufficient sampling data, time-frequency synchronization errors between platforms, and unreliable link reporting, the estimation deviation of cyclic statistics increases. Furthermore, when multiple UAVs cooperate, a trade-off must be made between communication overhead and detection performance. Existing methods often suffer from problems such as strong reliance on priors, insufficient robustness to non-ideal factors, and complex engineering implementation.

[0004] Therefore, it is necessary to propose a weak cyclic stationary signal detection method suitable for UAV-borne passive radar networks to achieve stable and reliable target detection under conditions of low signal-to-noise ratio, strong clutter interference, and non-ideal coordination. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a weak cyclic stationary signal detection method based on an unmanned aerial vehicle (UAV)-borne passive radar network, which addresses the reconnaissance challenges in low signal-to-noise ratio environments and improves the reliability and accuracy of reconnaissance systems.

[0006] The technical solution adopted in this invention is: a method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network, the specific steps of which are as follows:

[0007] S1. Establish models for the active radar transmitting signals and the UAV passive radar receiving signals;

[0008] S2. Based on step S1, a binary hypothesis model based on cyclic stationary generalized likelihood ratio detection is established using the block cyclic matrix approximation.

[0009] S3. Based on the binary hypothesis model established in step S2, establish a cyclically stationary generalized likelihood ratio detection statistic.

[0010] S4. Based on step S3, establish a Bayesian minimum risk fusion model, fuse the detection results of all UAVs, and obtain the final system detection results.

[0011] Furthermore, step S1 is specifically as follows:

[0012] First, set the active radar transmission signal. The expression is as follows:

[0013] (1);

[0014] in, Representing a time series, Indicates the frequency modulation slope. Represents a pulse sequence. Indicates the number of pulses. Indicates the signal carrier frequency. Indicates the pulse width. Indicates the pulse repetition period. Indicates amplitude, The rectangle function is represented by the following expression:

[0015] (2);

[0016] Then the first The target echo signal received by the drone The expression is as follows:

[0017] (3);

[0018] in, , Indicates the total number of drones. This indicates that the received signal has reached the drone. The amplitude attenuation, This indicates the latency of the corresponding drone.

[0019] Furthermore, step S2 is specifically as follows:

[0020] Within each detection cycle, each drone performs multiple detection operations. Each detection operation corresponds to a duration of... The detection window, in which Indicates the cycle period. This indicates the number of repetitions in that cycle. The total number of detection windows is denoted as... , After discretization, Indicates the first The drone in the first Echo sampling sequence within each detection window.

[0021] Then the first The drone in the first Within a detection window, the binary hypothesis model The expression is as follows:

[0022] (4);

[0023] in, Represents a discrete time series. Indicates the first The drone in the first Noise within each detection window Indicates the first The drone in the first Multipath interference within each detection window.

[0024] Then define a signal vector. Its specific expression is as follows:

[0025] (5);

[0026] in, Represents the set of complex numbers. This represents the matrix transpose operation; similarly, the received signal is defined. ,noise and interference signals The expressions for their respective signal vectors are as follows:

[0027] (6);

[0028] The binary hypothesis model is then updated to the following expression:

[0029] (7);

[0030] exist In the first cycle, the first The expression for the observed signal at each UAV location is as follows:

[0031] (8);

[0032] in, .

[0033] Define the covariance matrix The definition of , Then, utilizing the cyclic stationarity property, we obtain the following expression:

[0034] (9);

[0035] in, Expressing expectations, This indicates the conjugate transpose. Indicates in the assumption The expectations below Indicates time delay. This represents the cycle period. The derived expression is as follows:

[0036] (10);

[0037] The binary hypothesis is then updated to the following expression:

[0038] (11);

[0039] in, Indicates a Gaussian distribution. Represents a zero matrix.

[0040] The signal form is transformed to meet the requirements of two-dimensional discrete Fourier transform processing, resulting in the following transformed signal expression:

[0041] (12);

[0042] Then, approximation is performed using a block cyclic matrix, i.e. The specific expression for the block cyclic matrix is ​​as follows:

[0043] (13);

[0044] Introduce two more transformation matrices and The transformations of the covariance matrix of the two matrices are expressed as follows:

[0045] (14);

[0046] in, , .matrix After diagonalization, a diagonal matrix is ​​obtained. , This represents the eigenvalues ​​after diagonalization. The specific process expression is as follows:

[0047] (15);

[0048] in, Represents the Kronecker product. A diagonal matrix. Dimension is It is divided into diagonal pieces Each sub-block corresponds to a space-time block. from Extract from, the expression is as follows:

[0049] (16);

[0050] in, .

[0051] Then, through inverse Fourier transform, we obtain... The expression is as follows:

[0052] (17);

[0053] Then, all spatial covariance sub-blocks are reassembled to reconstruct the block diagonal covariance matrix. To make its structure correspond to the original structure, the expression is as follows:

[0054] (18);

[0055] in, This represents a block diagonal matrix obtained by concatenating matrices in a block diagonal format. Similarly, we obtain the matrix... .

[0056] The observation vector of the final detection system The expression is as follows:

[0057] (19);

[0058] in, The binary hypothesis model based on cyclostationary generalized likelihood ratio detection is expressed as follows:

[0059] (20);

[0060] Furthermore, step S3 is specifically as follows:

[0061] First, the observation vector Divided into Section, i.e. Then the maximum likelihood estimate The expression is as follows:

[0062] (twenty one);

[0063] Setting the Cyclic Stationary-Generalized Likelihood Ratio Detection Statistic The expression is defined as follows:

[0064] (twenty two);

[0065] in, Let represent the likelihood function. Then, according to statistical signal processing theory, after matrix operations, the covariance matrix estimation expression is as follows:

[0066] (twenty three);

[0067] (twenty four);

[0068] in, Represents the first of the matrix Each block, Represents the identity matrix. Detection statistic. The simplified expression is as follows:

[0069] (25);

[0070] in, The decision expression is as follows:

[0071] (26);

[0072] in, Indicates drone The decision threshold. Then the detection probability. The expression is as follows:

[0073] (27);

[0074] in, This indicates a probability calculation. Indicates the local signal-to-noise ratio. Indicates the number of samples tested. Indicates the noise variance. This represents the standard Q-function. And the false alarm probability... The expression is as follows:

[0075] (28);

[0076] Furthermore, step S4 is specifically as follows:

[0077] At the fusion center, each UAV transmits its local detection results to the fusion center, where equivalent false alarm probability and equivalent detection probability analysis are performed. (UAV) Equivalent detection probability and equivalent false alarm probability The expressions are as follows:

[0078] (29);

[0079] in, This represents the signal-to-noise ratio of the transmission channel. At the fusion center, a Bayesian minimum risk method is used to fuse the local detection results of all UAVs to obtain the final detection result. The expression is as follows:

[0080] (30);

[0081] in, Indicates a hypothesis The prior probability, This indicates the partial detection results of the drone. Indicates a hypothesis The joint probability, Let represent the marginal probability. Then the expression for the decision criterion at the fusion center is as follows:

[0082] (31);

[0083] in, Indicates a hypothesis The prior probability, Indicates a hypothesis The joint probability; then the equivalent detection probability and equivalent false alarm probability Substituting into equation (31), we obtain the following expression:

[0084] (32);

[0085] in, Indicates that there is The drone detected the target's presence. Indicates the decision threshold. If If the probability is positive, the target is considered to exist; otherwise, it is considered not to exist. Global detection probability. The expression is as follows:

[0086] (33);

[0087] in, Represents the integral variable. Indicates non-central parameters, Represents the Bessel function. This represents the degrees of freedom of the chi-square distribution.

[0088] Finally, signal detection is achieved by combining local detection with global fusion, thereby improving the probability of target detection.

[0089] The beneficial effects of this invention are as follows: First, the method of this invention establishes models of the active radar transmitted signal and the UAV passive radar received signal. Then, using the block cyclic matrix approximation, it establishes a binary hypothesis model based on cyclic stationary generalized likelihood ratio (GPR) detection. Utilizing the cyclic stationary characteristics of the target echo, it constructs a GPR detection statistic under colored noise background and obtains the local detection results for each UAV. Finally, at the fusion center, it fuses the local detection results of each UAV based on the Bayesian minimum risk criterion, outputting a global detection decision, forming a detection process of cyclic stationary GPR test-Bayesian minimum risk fusion. This method utilizes the cyclic stationary characteristics of the target echo to construct the detection statistic, which can improve the global detection accuracy and robustness under low signal-to-noise ratio and complex colored noise conditions, and is applicable to multi-UAV cooperative passive radar detection scenarios. Attached Figure Description

[0090] Figure 1 This is a flowchart of a weak cyclic stationary signal detection method based on an unmanned aerial vehicle (UAV)-borne passive radar network according to the present invention.

[0091] Figure 2 This is a comparison chart of the detection performance of local detectors in embodiments of the present invention.

[0092] Figure 3 This is a comparison chart of the detection performance of fused and non-fused devices in an embodiment of the present invention. Detailed Implementation

[0093] The method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0094] like Figure 1 The flowchart of a weak cyclic stationary signal detection method based on an unmanned aerial vehicle (UAV)-borne passive radar network is shown below. The specific steps are as follows:

[0095] S1. Establish models for the active radar transmitting signals and the UAV passive radar receiving signals;

[0096] S2. Based on step S1, a binary hypothesis model based on cyclic stationary generalized likelihood ratio detection is established using the block cyclic matrix approximation.

[0097] S3. Based on the binary hypothesis model established in step S2, establish a cyclically stationary generalized likelihood ratio detection statistic.

[0098] S4. Based on step S3, establish a Bayesian minimum risk fusion model, fuse the detection results of all UAVs, and obtain the final system detection results.

[0099] In this embodiment, step S1 is specifically as follows:

[0100] First, set the active radar transmission signal. The expression is as follows:

[0101] (1);

[0102] in, Representing a time series, Indicates the frequency modulation slope. Represents a pulse sequence. Indicates the number of pulses. Indicates the signal carrier frequency. Indicates the pulse width. Indicates the pulse repetition period. Indicates amplitude, The rectangle function is represented by the following expression:

[0103] (2);

[0104] Then the first The target echo signal received by the drone The expression is as follows:

[0105] (3);

[0106] in, , Indicates the total number of drones. This indicates that the received signal has reached the drone. The amplitude attenuation, This indicates the latency of the corresponding drone.

[0107] In this embodiment, step S2 is specifically as follows:

[0108] Within each detection cycle, each drone performs multiple detection operations. Each detection operation corresponds to a duration of... The detection window, in which Indicates the cycle period. This indicates the number of repetitions in that cycle. The total number of detection windows is denoted as... ,in After discretization, Indicates the first The drone in the first Echo sampling sequence within each detection window.

[0109] Then the first The drone in the first Within a detection window, the binary hypothesis model The expression is as follows:

[0110] (4);

[0111] in, Represents a discrete time series. Indicates the first The drone in the first Noise within each detection window Indicates the first The drone in the first Multipath interference within each detection window.

[0112] Then define a signal vector. Its specific expression is as follows:

[0113] (5);

[0114] in, Represents the set of complex numbers. This represents the matrix transpose operation; similarly, the received signal is defined. ,noise and interference signals The expressions for their respective signal vectors are as follows:

[0115] (6);

[0116] The binary hypothesis model is then updated to the following expression:

[0117] (7);

[0118] exist In the first cycle, the first The expression for the observed signal at each UAV location is as follows:

[0119] (8);

[0120] in, .

[0121] Define the covariance matrix The definition of , Then, utilizing the cyclic stationarity property, we obtain the following expression:

[0122] (9);

[0123] in, Expressing expectations, This indicates the conjugate transpose. Indicates in the assumption The expectations below Indicates time delay. This represents the cycle period. The derived expression is as follows:

[0124] (10);

[0125] The binary hypothesis is then updated to the following expression:

[0126] (11);

[0127] in, Indicates a Gaussian distribution. Represents a zero matrix.

[0128] The signal form is transformed to meet the requirements of two-dimensional discrete Fourier transform processing, resulting in the following transformed signal expression:

[0129] (12);

[0130] Then, approximation is performed using a block cyclic matrix, i.e. The specific expression for the block cyclic matrix is ​​as follows:

[0131] (13);

[0132] Introduce two more transformation matrices and The transformations of the covariance matrix of the two matrices are expressed as follows:

[0133] (14);

[0134] in, , .matrix After diagonalization, a diagonal matrix is ​​obtained. , This represents the eigenvalues ​​after diagonalization. The specific process expression is as follows:

[0135] (15);

[0136] in, Represents the Kronecker product. A diagonal matrix. Dimension is It is divided into diagonal pieces Each sub-block corresponds to a space-time block. from Extract from, the expression is as follows:

[0137] (16);

[0138] in, .

[0139] Then, through inverse Fourier transform, we obtain... The expression is as follows:

[0140] (17);

[0141] Then, all spatial covariance sub-blocks are reassembled to reconstruct the block diagonal covariance matrix. To make its structure correspond to the original structure, the expression is as follows:

[0142] (18);

[0143] in, This represents a block diagonal matrix obtained by concatenating matrices in a block diagonal format. Similarly, we obtain the matrix... .

[0144] The observation vector of the final detection system The expression is as follows:

[0145] (19);

[0146] in, The binary hypothesis model based on cyclostationary generalized likelihood ratio detection is expressed as follows:

[0147] (20);

[0148] In this embodiment, step S3 is specifically as follows:

[0149] First, the observation vector Divided into Section, i.e. Then the maximum likelihood estimate The expression is as follows:

[0150] (twenty one);

[0151] Setting the Cyclic Stationary-Generalized Likelihood Ratio Detection Statistic The expression is defined as follows:

[0152] (twenty two);

[0153] in, Let represent the likelihood function. Then, according to statistical signal processing theory, after matrix operations, the covariance matrix estimation expression is as follows:

[0154] (twenty three);

[0155] (twenty four);

[0156] in, Represents the first of the matrix Each block, Represents the identity matrix. Detection statistic. The simplified expression is as follows:

[0157] (25);

[0158] in, The decision expression is as follows:

[0159] (26);

[0160] in, Indicates drone The decision threshold. Then the detection probability. The expression is as follows:

[0161] (27);

[0162] in, This indicates a probability calculation. Indicates the local signal-to-noise ratio. Indicates the number of samples tested. Indicates the noise variance. This represents the standard Q-function. And the false alarm probability... The expression is as follows:

[0163] (28);

[0164] In this embodiment, step S4 is specifically as follows:

[0165] At the fusion center, each UAV transmits its local detection results. During transmission, due to factors such as channel errors, the detection probability and false alarm probability observed at the fusion center will change. Therefore, equivalent false alarm probability and equivalent detection probability are used for analysis. (UAV) Equivalent detection probability and equivalent false alarm probability The expressions are as follows:

[0166] (29);

[0167] in, This represents the signal-to-noise ratio of the transmission channel. At the fusion center, a Bayesian minimum risk method is used to fuse the local detection results of all UAVs to obtain the final detection result. The expression is as follows:

[0168] (30);

[0169] in, Indicates a hypothesis The prior probability, This indicates the partial detection results of the drone. Indicates a hypothesis The joint probability, Let represent the marginal probability. Then the expression for the decision criterion at the fusion center is as follows:

[0170] (31);

[0171] in, Indicates a hypothesis The prior probability, Indicates a hypothesis The joint probability; then the equivalent detection probability and equivalent false alarm probability Substituting into equation (31), we obtain the following expression:

[0172] (32);

[0173] in, Indicates that there is The drone detected the target's presence. Indicates the decision threshold. If If the probability is positive, the target is considered to exist; otherwise, it is considered not to exist. Global detection probability. The expression is as follows:

[0174] (33);

[0175] in, Represents the integral variable. Indicates non-central parameters, Represents the Bessel function. This represents the degrees of freedom of the chi-square distribution.

[0176] Finally, signal detection is achieved by combining local detection with global fusion, thereby improving the probability of target detection.

[0177] This embodiment also includes further simulation verification and analysis, such as... Figure 2 As shown, a comparison of local detector performance is performed. CGLRT-BMRF, the detector proposed in this invention, demonstrates significantly superior detection performance compared to other detectors. For example... Figure 3 As shown, comparing the unfused detection results with the fused detection results of 10 drones, it can be found that the detection performance of the fused results is significantly better than that of the unfused results.

[0178] In summary, the method of this invention utilizes the cyclostationary characteristics of target echoes to construct detection statistics, enabling reliable detection of weak targets in low signal-to-noise ratio, strong clutter, and interference environments, while improving the detection probability under controllable false alarms. This method has low dependence on target parameters and noise models, and only requires uploading low-dimensional statistics or local decision results to complete collaborative fusion, thereby reducing communication overhead and improving the engineering applicability and robustness of UAV-borne passive radar networks under non-ideal conditions such as maneuvering and synchronization errors.

[0179] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network, the specific steps of which are as follows: S1. Establish models for the active radar transmitting signals and the UAV passive radar receiving signals; S2. Based on step S1, a binary hypothesis model based on cyclic stationary generalized likelihood ratio detection is established using the block cyclic matrix approximation. S3. Based on the binary hypothesis model established in step S2, establish a cyclically stationary generalized likelihood ratio detection statistic. S4. Based on step S3, establish a Bayesian minimum risk fusion model, fuse the detection results of all UAVs, and obtain the final system detection results.

2. The method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network according to claim 1, characterized in that, The specific steps of S1 are as follows: First, set the active radar transmission signal. The expression is as follows: （1）； in, Representing a time series, Indicates the frequency modulation slope. Represents a pulse sequence. Indicates the number of pulses. Indicates the signal carrier frequency. Indicates the pulse width. Indicates the pulse repetition period. Indicates amplitude, The rectangle function is represented by the following expression: （2）； Then the first The target echo signal received by the drone The expression is as follows: （3）； in, , Indicates the total number of drones. This indicates that the received signal has reached the drone. The amplitude attenuation, This indicates the latency of the corresponding drone.

3. The method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network according to claim 2, characterized in that, Step S2 is as follows: Within each detection cycle, each drone performs multiple detection operations; each detection operation corresponds to a duration of... The detection window, in which Indicates the cycle period. This indicates the number of repetitions in that cycle; the total number of detection windows is denoted as... , After discretization, Indicates the first The drone in the first Echo sampling sequence within each detection window; Then the first The drone in the first Within a detection window, the binary hypothesis model The expression is as follows: （4）； in, Represents a discrete time series. Indicates the first The drone in the first Noise within each detection window Indicates the first The drone in the first Multipath interference within each detection window; Then define a signal vector. Its specific expression is as follows: （5）； in, Represents the set of complex numbers. This represents the matrix transpose operation; similarly, the received signal is defined. ,noise and interference signals The expressions for their respective signal vectors are as follows: （6）； The binary hypothesis model is then updated to the following expression: （7）； exist In the first cycle, the first The expression for the observed signal at each UAV location is as follows: （8）； in, ; Set the covariance matrix The definition of , Then, utilizing the cyclic stationarity property, we obtain the following expression: （9）； in, Expressing expectations, This indicates the conjugate transpose. Indicates in the assumption The expectations below Indicates time delay. Let represent the cycle period; then the derived expression is as follows: （10）； The binary hypothesis is then updated to the following expression: （11）； in, Indicates a Gaussian distribution. Represents a 0 matrix; The signal form is transformed to meet the requirements of two-dimensional discrete Fourier transform processing, resulting in the following transformed signal expression: （12）； Then, approximation is performed using a block cyclic matrix, i.e. The specific expression for the block cyclic matrix is ​​as follows: （13）； Introduce two more transformation matrices and The transformations of the covariance matrix of the two matrices are expressed as follows: （14）； in, , ;matrix After diagonalization, a diagonal matrix is ​​obtained. , This represents the eigenvalues ​​after diagonalization; the specific process expression is as follows: （15）； in, Represents the Kronecker product; a diagonal matrix Dimension is It is divided into diagonal pieces Each sub-block corresponds to a space-time block. from Extract from, the expression is as follows: （16）； in, ; Then, through inverse Fourier transform, we obtain... The expression is as follows: （17）； Then, all spatial covariance sub-blocks are reassembled to reconstruct the block diagonal covariance matrix. To make its structure correspond to the original structure, the expression is as follows: （18）； in, This represents a block diagonal matrix obtained by concatenating matrices in block diagonal form; similarly, we obtain the matrix... ; The observation vector of the final detection system The expression is as follows: （19）； in, The binary hypothesis model based on cyclic stationary generalized likelihood ratio detection is expressed as follows: （20）。 4. The method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network according to claim 3, characterized in that, Step S3 is as follows: First, the observation vector Divided into Section, i.e. Then the maximum likelihood estimate The expression is as follows: （21）； Setting the Cyclic Stationary-Generalized Likelihood Ratio Detection Statistic The expression is defined as follows: （22）； in, Let represent the likelihood function. Then, according to statistical signal processing theory, after matrix operations, the covariance matrix estimation expression is as follows: （23）； （24）； in, Represents the first of the matrix Each block, Represents the identity matrix; detection statistic The simplified expression is as follows: （25）； in, The decision expression is as follows: （26）； in, Indicates drone The decision threshold; then the detection probability. The expression is as follows: （27）； in, This indicates a probability calculation. Indicates the local signal-to-noise ratio. Indicates the number of samples tested. Indicates the noise variance. This represents the standard Q-function; and the false alarm probability... The expression is as follows: （28）。 5. The method for detecting weak cyclic stationary signals based on an unmanned aerial vehicle (UAV)-borne passive radar network according to claim 4, characterized in that, Step S4 is as follows: At the fusion center, each UAV transmits its local detection results to the fusion center, where equivalent false alarm probability and equivalent detection probability analysis are performed; UAV Equivalent detection probability and equivalent false alarm probability The expressions are as follows: （29）； in, This represents the signal-to-noise ratio of the transmission channel. At the fusion center, a Bayesian minimum risk method is used to fuse the local detection results of all UAVs to obtain the final detection result. The expression is as follows: （30）； in, Indicates a hypothesis The prior probability, This indicates the partial detection results of the drone. Indicates a hypothesis The joint probability, Let represent the marginal probability; then the expression for the decision criterion at the fusion center is as follows: （31）； in, Indicates a hypothesis The prior probability, Indicates a hypothesis The joint probability; then the equivalent detection probability and equivalent false alarm probability Substituting into equation (31), we obtain the following expression: （32）； in, Indicates that there is The drone detected the target's presence. Indicates the decision threshold; if If the probability is positive, the target is considered to exist; otherwise, it is considered not to exist. (Global detection probability) The expression is as follows: （33）； in, Represents the integral variable. Indicates non-central parameters, Represents the Bessel function. Indicates the degrees of freedom of the chi-square distribution; Finally, signal detection is achieved by combining local detection with global fusion, thereby improving the probability of target detection.

Images