A distributed multi-pulse detection method based on local order statistics

Abstract

The present invention discloses a distributed multi-pulse detection method based on local order statistics, comprising the following steps: initializing a distributed radar target detection system; performing square law and DMOS-CFAR processing on each radar station to obtain local statistics; maximizing the data of the Doppler channel at each radar station and calculating its probability density function; calculating a test statistic based on a likelihood ratio test using the probability density function of the maximum value of the Doppler channel data at each radar station, and transmitting the result to a fusion center via a communication link; obtaining a global optimal statistic from the local test statistic, calculating a global decision threshold based on the global optimal statistic and a false alarm rate, and finally obtaining a target detection result in a spatial resolution unit based on the threshold. The present invention significantly improves the anti-interference capability of the distributed pulse Doppler radar system and enhances the detection capability and false alarm control capability of the radar target detection algorithm under non-uniform backgrounds such as multi-false target interference and clutter edges.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar target detection, and in particular relates to a distributed multi-pulse detection method based on local order statistics. Background Art

[0002] Radar target detection refers to the process by which a radar detects a target based on the echo signal in a resolution unit of interest according to a predetermined procedure. To increase the probability of target detection, multiple spatially dispersed radars are typically used to jointly illuminate the target, forming the typical framework of a distributed radar system. In a distributed radar system, to achieve better detection results, radar stations transmit processed local test statistics via links to a fusion center for global decision-making. To prevent computational overload caused by excessive computational effort in tracking units, radar systems are typically designed with a fixed false alarm rate during the target detection phase. Because the noise level in practical applications is often unknown, existing methods for estimating the noise level of adjacent range units within a reference window have significant drawbacks.

[0003] Patent document CN118330601A discloses a "distributed radar target detection method based on Doppler channel maximum quantization." This method improves the signal-to-noise ratio and suppresses clutter through pulse accumulation. A DMCA-CFAR (unit-averaged constant false alarm) detection algorithm is used in the local statistical processing process. The noise power is estimated by averaging adjacent distance units and a threshold value is set. Although its detection performance is good under uniform background conditions, it is extremely susceptible to interference from multiple false targets and clutter edges, and its anti-interference ability is poor.

[0004] Patent publication number CN116106845A discloses a "target detection method based on square root detection and CA-CFAR." This method uses matched filtering and square root detection transformation followed by CA-CFAR (unit averaging) detection. This method significantly improves the ability to suppress the obstruction effect caused by interfering targets without incurring additional detection losses. It also has a low computational load and is easy to implement in engineering. However, this method only optimizes the CA-CFAR algorithm for multi-target interference scenarios, fails to consider clutter edge environments, and its performance in non-uniform scenarios has not been verified. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a distributed multi-pulse detection method based on local order statistics. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] The present invention provides a distributed multi-pulse detection method based on local order statistics, the method comprising:

[0007] Initialize the distributed radar target detection system; the distributed radar target detection system includes: a fusion center and M radar stations distributed in three-dimensional space, each radar station S m N pulses are emitted within a coherent processing time, and the false alarm probability of the fusion center is P fa , each radar station S m Set the same reference window number n; where M ≥ 2, N ≥ 2, n ≥ 2;

[0008] Each radar station S m The signal amplitude in the spatial resolution unit is sampled, and sorted according to the gradient and sequence value to obtain sorted data. The sorted data is processed based on the square law and DMOS-CFAR detection algorithm to obtain the local statistics t of each Doppler channel. m ;

[0009] Each radar station S m The local statistics of the Doppler channel t m Take the maximum value to get η m , through t m Cumulative distribution function of and Get η m Cumulative distribution function under the target non-existence hypothesis H0 And the cumulative distribution function under the hypothesis H1 right and Derivatives are taken separately to obtain η under two assumptions m The probability density function of and

[0010] Each radar station S m based on and Calculate the corresponding local test statistic L(η) based on the likelihood ratio test and transmit L(η) to the fusion center;

[0011] The fusion center obtains the global optimal statistic T according to the local test statistic L(η) opt ; Based on the global optimal statistic T opt and false alarm probability P fa The global decision threshold g of the distributed radar target detection system is obtained; based on the global optimal statistics T opt And the global decision threshold g is used to determine whether there is a target in the spatial resolution unit.

[0012] In one embodiment of the present invention, the false alarm probability P of the fusion center is fa =10 -4 .

[0013] In one embodiment of the present invention, each radar station S m The signal amplitude in the spatial resolution unit is sampled, and sorted according to the gradient and sequence value to obtain sorted data. The sorted data is processed based on the square law and DMOS-CFAR detection algorithm to obtain the local statistics t of each Doppler channel. m ,include:

[0014] Each radar station S m Sample the signal amplitude in the spatial resolution unit to obtain S m The output signal r within a coherent processing time m and S m The observation values ​​of the reference window are sorted by the increment and sequence value j to obtain the sorted data Y (m,j) , j=1,2,…,n; the sorting is as follows:

[0015] Y (m,1) ≤Y (m,2) ≤…≤Y (m,n) ;

[0016] Among them, Y (m,1) is the minimum value, Y (m,n) is the maximum value;

[0017] According to S m The output signal r m and sorted data Y (m,j) , use the first formula to calculate each radar station S m The local statistics of the Doppler channel t m ;

[0018] The first formula is as follows:

[0019]

[0020] Among them, t m =[t m,1 ,…,t m,i ,…,t m,N ], i=1,2,…,N.

[0021] In one embodiment of the present invention, t m Cumulative distribution function of and The expression is as follows:

[0022]

[0023] Among them, S represents the signal-to-noise ratio of the radar station, j represents the selected order value, t m,i ∈tm , i=1,2,…,N.

[0024] In one embodiment of the present invention, n m Cumulative distribution function under the target non-existence hypothesis H0 And the cumulative distribution function under the hypothesis H1 The expression is as follows:

[0025]

[0026] Among them, η m =t m,l , l is the index of the unit where the Doppler channel maximum value is located, represents t under the hypothesis H0 that the target does not exist m Middle t m,i The corresponding distribution function is represents t under the target existence hypothesis H1 m Middle t m,i The corresponding distribution function.

[0027] In one embodiment of the present invention, under two assumptions, η m The probability density function of and The expression is as follows:

[0028]

[0029] in, represents t under the hypothesis H0 that the target does not exist m Middle t m,i The corresponding probability density function is, represents t under the hypothesis H0 that the target does not exist m Middle t m,i The corresponding distribution function is represents t under the target existence hypothesis H1 m Middle t m,i The corresponding probability density function is, represents t under the target existence hypothesis H1 m Middle t m,i The corresponding distribution function.

[0030] In one embodiment of the present invention, each radar station S m based on and The corresponding local test statistic L(η) is calculated based on the likelihood ratio test, including:

[0031] Each radar station S m based on and The corresponding local test statistic L(η) is calculated using the second formula according to the likelihood ratio test;

[0032] The second formula is as follows:

[0033]

[0034] Among them, t m,i ∈t m , S represents the signal-to-noise ratio of the radar station, j represents the selected order value, and η represents the maximum value of the Doppler channel.

[0035] In one embodiment of the present invention, the global optimal statistic T opt The expression is as follows:

[0036]

[0037] Wherein, ln{·} represents the natural number logarithm operation.

[0038] In one embodiment of the present invention, based on the global optimal statistic T opt and false alarm probability P fa Obtain the global decision threshold g of the distributed radar target detection system, including:

[0039] Based on the global optimal statistic T opt and false alarm probability P fa , the global decision threshold g of the distributed radar target detection system under the assumption that the target does not exist is calculated using the third formula;

[0040] The third formula is as follows:

[0041] g=T opt (H0) -1 (1-P fa );

[0042] in,(·) -1 represents the inverse function of (·).

[0043] In one embodiment of the present invention, based on the global optimal statistic T opt And the global decision threshold g is used to determine whether there is a target in the spatial resolution unit, including:

[0044] Determine the global optimal statistic T opt ≥Whether the global decision threshold g holds:

[0045] If so, there is a target in the spatial resolution unit.

[0046] Otherwise, there is no target in the spatial resolution unit.

[0047] Beneficial effects of the present invention:

[0048] In the solution provided by the present invention, the local statistics of each radar station are processed using the DMOS-CFAR detection algorithm, thereby avoiding the problem of poor noise estimation by averaging adjacent distance units in the existing DMCA-CFAR detection algorithm under non-uniform background. By selecting appropriate sequence values, the influence of multiple false target interference and clutter edge environment is eliminated, thereby greatly improving the anti-interference capability of the distributed pulse radar system and enhancing the detection capability and false alarm control capability of the radar target detection algorithm under non-uniform backgrounds such as multiple false target interference and clutter edge. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic flow chart of a distributed multi-pulse detection method based on local order statistics provided by an embodiment of the present invention;

[0050] Figure 2 This is a curve diagram showing the variation of detection performance of different detection algorithms with signal-to-noise ratio under the interference of multiple false targets;

[0051] Figure 3 This is a curve showing the detection performance of different detection algorithms changing with the signal-to-noise ratio when the target is in a weak clutter area at the edge of the clutter;

[0052] Figure 4 The following is a curve showing the false alarm probability of different detection algorithms changing with clutter power when the detection unit is in a strong clutter area at the edge of the clutter. DETAILED DESCRIPTION

[0053] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0054] In a distributed radar system, in order to obtain better detection results, the radar station transmits the processed local test statistics to the fusion center through a link and makes a global decision. Distributed radar systems often use constant false alarm detection to process local statistics, and set a fixed false alarm rate to avoid the problem of excessive computational complexity of the tracking unit, while estimating the noise level of adjacent distance units in the reference window. However, in engineering practice, the existing technology represented by distributed multi-pulse unit average constant false alarm detection (DMCA-CFAR for short) faces detection performance loss in non-uniform scenarios such as multiple false target interference and clutter edges, and cannot meet the requirements of modern radar anti-interference capabilities. To address this pain point, an embodiment of the present invention provides a distributed multi-pulse detection method based on local order statistics, such as Figure 1 As shown, this may include:

[0055] S1, initialize the distributed radar target detection system.

[0056] Specifically, the distributed radar target detection system includes: a fusion center and M radar stations distributed in three-dimensional space, each radar station S m N pulses are emitted within a coherent processing time, and the false alarm probability of the fusion center is P fa , each radar station S m Set the same reference window number n; where M≥2, N≥2, n≥2. In the embodiment of the present invention, the false alarm probability P of the fusion center fa Can be set to P fa =10 -4 , M can be set to 3, N can be set to 4, and the reference window is the window used to estimate the noise power.

[0057] S2, each radar station S m The signal amplitude in the spatial resolution unit is sampled and sorted by gradient and sequence value to obtain sorted data. The sorted data is processed based on the square law and DMOS-CFAR detection algorithm to obtain the local statistics t of each Doppler channel. m .

[0058] For S2, this may include:

[0059] S21, each radar station S m Sample the signal amplitude in the spatial resolution unit to obtain S m The output signal r within a coherent processing time m and S m The observation values ​​of the reference window are sorted by the increment and sequence value j to obtain the sorted data Y (m,j) , j=1,2,…,n; the sorting is as follows:

[0060] Y (m,1) ≤Y (m,2) ≤…≤Y (m,n) ;

[0061] Among them, Y (m,1) is the minimum value, Y (m,n) is the maximum value, and m represents the serial number of the mth radar station.

[0062] S22, according to S m The output signal r m and sorted data Y (m,j) , use the first formula to calculate each radar station S m The local statistics of the Doppler channel t m ;

[0063] The first formula is as follows:

[0064]

[0065] Among them, t m =[t m,1 ,…,t m,i ,…,t m,N ], i=1,2,…,N.

[0066] It is understandable that the output signal r m and the local statistic t m All are vectors.

[0067] S3, each radar station S m The local statistics of the Doppler channel t m Take the maximum value to get η m , through t m Cumulative distribution function of and Get η m Cumulative distribution function under the target non-existence hypothesis H0 And the cumulative distribution function under the hypothesis H1 right and Derivatives are taken separately to obtain η under two assumptions m The probability density function of and

[0068] For S3, this may include:

[0069] S31, each radar station S m The local statistics of the Doppler channel t m Take the maximum value to get η m , through t m Cumulative distribution function of and Get η m Cumulative distribution function under the target non-existence hypothesis H0 And the cumulative distribution function under the hypothesis H1

[0070] Specifically, in the embodiment of the present invention, the fluctuation characteristics of the target amplitude can be assumed to be a Swerling I model, t m The probability density function of and The expression is as follows:

[0071]

[0072] Where n represents the total number of reference windows, S represents the signal-to-noise ratio of the radar station, j represents the selected order value, and t m,i ∈t m, i=1,2,…,N.

[0073] t m Cumulative distribution function of and The expression is as follows:

[0074]

[0075] Where S represents the signal-to-noise ratio of the radar station, and j represents the selected sequence value.

[0076] Doppler channel maximum value η m Cumulative distribution function under the target non-existence hypothesis H0 And the cumulative distribution function under the hypothesis H1 The expression is as follows:

[0077]

[0078] in, for In the Doppler channel data t m The cumulative distribution function at the maximum value, η m =t m,l , l is the index of the unit where the Doppler channel maximum value is located, represents t under the hypothesis H0 that the target does not exist m Middle t m,i The corresponding distribution function is represents t under the target existence hypothesis H1 m Middle t m,i The corresponding distribution function.

[0079] S32, yes and Derivatives are taken separately to obtain η under two assumptions m The probability density function of and

[0080] Specifically, under two assumptions, η m The probability density function of and The expression is as follows:

[0081]

[0082] in, represents t under the hypothesis H0 that the target does not exist m Middle t m,i The corresponding probability density function is, represents t under the hypothesis H0 that the target does not exist m Middle t m,iThe corresponding distribution function is represents t under the target existence hypothesis H1 m Middle t m,i The corresponding probability density function is, represents t under the target existence hypothesis H1 m Middle t m,i The corresponding distribution function.

[0083] It can be understood that the embodiment of the present invention adopts a distributed multi-pulse detection method (DMOS-CFAR) based on local order statistics for local statistics, and eliminates the interference of multiple false targets and the influence of clutter edges by selecting appropriate order values. A comparative simulation experiment is set up below to verify the anti-interference ability of the method proposed in the embodiment of the present invention.

[0084] S4, each radar station S m based on and The corresponding local test statistic L(η) is calculated based on the likelihood ratio test and transmitted to the fusion center.

[0085] For S4, this may include:

[0086] Each radar station S m based on and The corresponding local test statistic L(η) is calculated using the second formula according to the likelihood ratio test;

[0087] The second formula is as follows:

[0088]

[0089] Among them, t m,i ∈t m , S represents the signal-to-noise ratio of the radar station, j represents the selected order value, and η represents the maximum value of the Doppler channel.

[0090] S5, the fusion center obtains the global optimal statistic T according to the local test statistic L(η) opt ; Based on the global optimal statistic T opt and false alarm probability P fa The global decision threshold g of the distributed radar target detection system is obtained; based on the global optimal statistics T opt And the global decision threshold g is used to determine whether there is a target in the spatial resolution unit.

[0091] For S5, this may include:

[0092] S51, the fusion center obtains the global optimal statistic T according to the local test statistic L(η) opt .

[0093] Specifically, the fusion center calculates the global optimal statistic T based on m local test statistics L(η) opt , the global optimal statistic T opt The expression is as follows:

[0094]

[0095] Wherein, ln{·} represents the natural number logarithm operation.

[0096] It can be understood that the embodiment of the present invention designs an optimal fusion algorithm based on the Neyman-Pearson criterion in the fusion center to calculate the global optimal statistics, thereby making a global decision to determine whether the target exists.

[0097] S52, based on the global optimal statistic T opt and false alarm probability P fa Obtaining the global decision threshold g of the distributed radar target detection system may include:

[0098] Based on the global optimal statistic T opt and false alarm probability P fa , the global decision threshold g of the distributed radar target detection system under the assumption that the target does not exist is calculated using the third formula;

[0099] The third formula is as follows:

[0100] g=T opt (H0) -1 (1-P fa );

[0101] in,(·) -1 represents the inverse function of (·).

[0102] S53, based on the global optimal statistic T opt The global decision threshold g is used to determine whether a target exists in the spatial resolution unit, which may include:

[0103] Determine the global optimal statistic T opt ≥Whether the global decision threshold g holds:

[0104] If so, there is a target in the spatial resolution unit.

[0105] Otherwise, there is no target in the spatial resolution unit.

[0106] As can be understood, the embodiment of the present invention designs an optimal fusion algorithm based on the Neyman-Pearson criterion in the fusion center to calculate the global optimal statistic, thereby making a global decision to determine the presence or absence of a target. Compared with the existing DMCA-CFAR, which estimates the noise by averaging adjacent distance units, the distributed multi-pulse detection method based on local order statistics (DMOS-CFAR) provided by the embodiment of the present invention determines the threshold value by selecting the order value under the interference of multiple false targets. On the one hand, it solves the problem that traditional detection algorithms cannot estimate background noise well due to interference from false targets. On the other hand, selecting the optimal order value can effectively avoid the situation where the order value is too high, which will include false targets in the background noise power estimate and thus raise the threshold, or the order value is too low, which will ignore high-amplitude noise and thus underestimate the threshold. Simulation experiments have shown that under the condition of maintaining a certain global constant false alarm rate, the correct detection probability of DMOS-CFAR with the optimal order value is higher than that of DMCA-CFAR. Compared to existing DMCA-CFAR algorithms, which average noise estimates across adjacent range bins, the DMOS-CFAR detection algorithm selects an optimal sequence value to determine the threshold in clutter-edge environments. This prevents high-energy clutter from contaminating the detection bins and raising the threshold, while low-energy clutter from lowering the threshold. Simulations demonstrate that under optimal sequence values, DMOS-CFAR has a higher probability of correct detection than DMCA-CFAR and also outperforms DMCA-CFAR in controlling false alarms.

[0107] The embodiment of the present invention adopts the DMOS-CFAR detection algorithm to process the local statistics of each radar station respectively, avoiding the problem of poor noise estimation by averaging adjacent range units in the existing DMCA-CFAR detection algorithm under non-uniform background. By selecting appropriate sequence values, the influence of multiple false target interference and clutter edge environment is eliminated, thereby significantly improving the anti-interference capability of the distributed pulse radar system and enhancing the detection capability and false alarm control capability of the radar target detection algorithm under non-uniform backgrounds such as multiple false target interference and clutter edge.

[0108] The technical effects of the present invention are further described in detail below in conjunction with simulation experiments.

[0109] Simulation conditions:

[0110] In the distributed radar system, three radar stations and one fusion center are set, the number of pulses is 4, and the false alarm probability is set to 10 -4 The signal-to-noise ratio of each local radar station is set to S, and the total signal-to-noise ratio ranges from 0dB to 25dB. The selection order value is set to j = 6, 12, 18, and 24. Two control experiments are also conducted: Neyman-person optimal detection and DMCA-CFAR.

[0111] The simulation hardware environment is: AMD Ryzen 5 5625U with Radeon Graphics, CPU is 2.3 GHz, main frequency is 16 GB of main memory. Software environment: Windows 11 Home Chinese version, MATLAB R2023a simulation software.

[0112] Simulation content:

[0113] a. Multiple false target interference

[0114] The false target interference is set to be located at the 90th, 95th, 105th and 110th distance units, with an interference-to-noise ratio of 10dB, and the real target is located at the 100th distance unit, with a signal-to-noise ratio of 14dB. Figure 2 The curve of detection performance of different detection algorithms with signal-to-noise ratio changes under the interference of multiple false targets is shown in Figure 2. Figure 2 As can be seen from the figure, when the order value j is 24 (the maximum order statistic), the detection performance is the lowest. This is because the interference amplitude of the false target is large. After sorting, the interference target has a higher probability of becoming the maximum value of the order statistic. At this time, if it is still used as the estimate of the background noise power, it will greatly increase the detection threshold, resulting in a loss of detection performance. When j = 12, 18, from a statistical perspective, the interference target is likely to not affect the noise power estimate. That is, j = 18 has a higher threshold value than j = 12, effectively controlling false alarms and achieving better detection performance. j = 6 has worse detection performance than j = 12, 18, but better than j = 24. Under the interference of multiple false targets, the detection performance of DMCA-CFAR is worse than that of DMOS-CFAR when j = 12, 18, which proves the superiority of the proposed DMOS-CFAR algorithm in multi-target scenarios. Under the condition of maintaining a constant false alarm rate, by selecting appropriate sequence values ​​(such as j = 12, 18), the correct detection probability of DMOS-CFAR is significantly higher than that of DMCA-CFAR.

[0115] b. Clutter edge environment

[0116] The ratio of the number of reference cells in the low-energy clutter area to the high-energy clutter area is set to 2:1. Figure 3 The curve of detection performance of different detection algorithms changing with signal-to-noise ratio when the target is in the weak clutter area at the edge of the clutter. Figure 3As can be seen from the figure, the detection performance when j = 18 is lower than that when j = 12. This is because after sorting, the high-energy clutter is located in the order statistics of 2n / 3 (the ratio of the number of reference units in the low-energy clutter area to the high-energy clutter area is 2:1) and above. j = 18 is a reference unit contaminated by high-energy clutter. If it is still used as an estimate of the background noise power, the detection threshold will be raised, resulting in a loss of detection performance. If the order value j is 24, the contamination is more serious, and the detection performance loss is the greatest. When j = 6, from a statistical point of view, the high-energy clutter is unlikely to affect the noise power estimation. Therefore, it is consistent with the simulation results of the uniform background, that is, the detection performance of j = 6 is worse than that of j = 12, but better than that of j = 24. It is worth noting that in the clutter edge environment, the detection performance of DMCA-CFAR is worse than that of DMOS-CFAR when j = 6, 12, and 18. This proves the superiority of the proposed DMOS-CFAR algorithm in the clutter edge environment. Under the condition of maintaining a constant false alarm rate, by selecting appropriate sequence values ​​(j = 6, 12, 18), the correct detection probability of DMOS-CFAR is significantly higher than that of DMCA-CFAR. Figure 4 The curve of false alarm probability of different detection algorithms changing with clutter power when the detection unit is a strong clutter area at the edge of the clutter. Figure 4 It can be seen that the false alarm control ability of DMOS-CFAR is significantly better than that of DMCA-CFAR.

[0117] In summary, the simulation results show that the local DMOS-CFAR detection algorithm proposed in this paper can greatly enhance the anti-interference capability of the radar system in multiple false target interference and clutter edge environments, improve the target detection probability and false alarm control capability, and expand ideas for the application of distributed radar systems in non-uniform backgrounds.

[0118] The distributed multi-pulse detection method (DMOS-CFAR) based on local order statistics proposed in the embodiment of the present invention eliminates the interference of multiple false targets and the influence of clutter edges by selecting appropriate order values, thereby improving the anti-interference ability of the distributed pulse Doppler radar system in non-uniform backgrounds such as multiple false target interference and clutter edges. The embodiment of the present invention greatly expands the application of target detection algorithms of distributed radar systems in non-uniform backgrounds, and provides a new idea for signal-level fusion detection methods, that is, in the face of complex interference and clutter scenarios, DMOS-CFAR or improved methods based on it can be tried to suppress clutter and interference, thereby improving the robustness of the radar system. In addition to multiple false targets and clutter edges, the appropriate selection of order values ​​can also effectively suppress or eliminate certain processed atmospheric noise, which means that the present invention greatly enriches the application scenarios of radar systems on the sea and in the air.

[0119] It should be noted that, in the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A distributed multi-pulse detection method based on local order statistics, characterized in that: include: Initialize the distributed radar target detection system; The distributed radar target detection system includes: a fusion center and distributed in three-dimensional space radar stations, each radar station Emitted within a coherent processing time pulses, the false alarm probability of the fusion center is , each radar station Set the same number of reference windows ;in, , , ; Each radar station The signal amplitude in the spatial resolution unit is sampled, sorted by gradient and sequence value to obtain sorted data, and the sorted data is processed based on the square law and DMOS-CFAR detection algorithm to obtain the local statistics of each Doppler channel. ,include: Each radar station The signal amplitude in the spatial resolution unit is sampled to obtain The output signal within a coherent processing time and The observations of the reference window; the obtained observations are sorted into increments and order values Sort to get sorted data , ; The sorting is as follows: ； in, is the minimum value, is the maximum value; according to Output signal and sorted data , use the first formula to calculate each radar station The local statistics of the Doppler channel ; The first formula is as follows: ； in, , ; Each radar station Local statistics of the Doppler channel Take the maximum value to get ,pass Cumulative distribution function of and get No assumptions exist on the target Cumulative distribution function under and target existence assumption Cumulative distribution function under ;right and Derivatives are taken separately to obtain the following two assumptions: The probability density function of and ; Each radar station based on and , calculate the corresponding local test statistic based on the likelihood ratio test ,Will Transmit to fusion center; The fusion center is based on the local test statistic Get the global optimal statistics Based on the global optimal statistics and false alarm probability Get the global decision threshold of the distributed radar target detection system ; Based on the global optimal statistics and global decision threshold Determine whether there is a target in the spatial resolution unit.

2. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: The false alarm probability of the fusion center .

3. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: described Cumulative distribution function of and The expression is as follows: ； in, represents the signal-to-noise ratio of the radar station, Indicates the selected order value, , .

4. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: described No assumptions exist on the target Cumulative distribution function under and target existence assumption Cumulative distribution function under The expression is as follows: ； in, , is the index of the unit where the maximum value of the Doppler channel is located, Indicates that there is no hypothesis on the target Next middle The corresponding distribution function is Indicates that there is an assumption on the target Next middle The corresponding distribution function.

5. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: Under the two assumptions The probability density function of and The expression is as follows: ； in, Indicates that there is no hypothesis on the target Next middle The corresponding probability density function is, Indicates that there is no hypothesis on the target Next middle The corresponding distribution function is Indicates that there is an assumption on the target Next middle The corresponding probability density function is, Indicates that there is an assumption on the target Next middle The corresponding distribution function.

6. The distributed multi-pulse detection method based on local order statistics according to claim 5, characterized in that: Each radar station based on and , calculate the corresponding local test statistic based on the likelihood ratio test ,include: Each radar station based on and , according to the likelihood ratio test, the corresponding local test statistic is calculated using the second formula ; The second formula is as follows: ； in, , represents the signal-to-noise ratio of the radar station, Indicates the selected order value, Indicates the maximum value of the Doppler channel.

7. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: The global optimal statistic The expression is as follows: ； in, Represents the natural number logarithm operation.

8. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: The global optimal statistics and false alarm probability Get the global decision threshold of the distributed radar target detection system ,include: Based on the global optimal statistics and false alarm probability , the third formula is used to calculate the distributed radar target detection system under the assumption that the target does not exist The global decision threshold under ; The third formula is as follows: ； in, express The inverse function of .

9. The distributed multi-pulse detection method based on local order statistics according to claim 1, characterized in that: Based on the global optimal statistics and global decision threshold Determine whether there is a target in the spatial resolution unit, including: Determining the global optimal statistic Global decision threshold Is it established? If so, there is a target in the spatial resolution unit. Otherwise, there is no target in the spatial resolution unit.

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