Target detection method and device under strong clutter background and electronic equipment

Abstract

The invention discloses a target detection method and device under a strong clutter background and electronic equipment, and belongs to the technical field of target detection. The method comprises the following steps: acquiring echo data of a moving platform radar, and performing pulse compression processing to obtain data after pulse compression; mTD processing is carried out on the data after pulse compression, and a two-dimensional data matrix of the RD spectrum is obtained; based on the distribution characteristics of clutters on the RD spectrum, data of different distance dimensions in the two-dimensional data matrix of the RD spectrum are subjected to standardization processing, and a standardized data matrix is obtained; constructing a connected domain for the standardized data matrix; on the basis of the area of each connected domain, clutter regions in the RD spectrum are determined, the clutter regions are removed, and a data matrix containing targets and noise is obtained; and based on the data matrix containing the target and the noise, target agglomeration is carried out to realize target detection. According to the method, target detection can be realized in a scene with high clutter intensity and serious distance dimension and Doppler dimension expansion.

Description

Technical Field

[0001] The present invention belongs to the technical field of target detection, and particularly relates to a target detection method, device and electronic device under a strong clutter background. Background Art

[0002] The phased array radar on a moving platform has the advantages of large detectable range, strong mobility, and the ability to detect low-altitude targets, and plays an increasingly important role in the current increasingly complex war environment. When the radar dives to pursue a target, it will receive three parts of signals, namely noise, environmental clutter, and the target. Among them, the environmental clutter has a great impact on the detection performance of the radar system. In the overlooking search state of the radar, the clutter intensity is large, and the relative movement between the moving platform and the ground clutter unit in different azimuth dimensions will cause the received clutter signal to show Doppler dimension expansion in the RD (Range Doppler) spectrum, and the radar beam irradiating different elevation dimensions on the ground will cause the clutter signal to show range dimension expansion in the RD spectrum. Due to the serious broadening of the clutter signal in the Doppler dimension and range dimension of the RD spectrum, especially the range dimension broadening is serious, it poses a huge challenge to the radar to achieve target detection. Therefore, researching a target detection algorithm suitable for the clutter background has very important engineering implementation value.

[0003] The traditional radar target detection methods mainly include the moving target indication method and the sliding window mean method. Among them, the moving target indication method is only applicable to the situation where there is no relative movement between the clutter and the moving platform, and the clutter is distributed at the position where the Doppler frequency is 0, and it is not applicable to the target detection in the scenario where the moving platform dives to pursue a target and encounters ground clutter. The sliding window mean method selects the surrounding window in each unit in the RD spectrum, calculates its mean value and then compares it with the noise. If the mean value is less than the noise, it is determined that the unit is neither a target nor clutter, and the window continues to slide to judge the next unit; if the mean value is greater than the noise, it is initially judged as a target, and if the sliding window mean value is greater than the noise at multiple consecutive units, it is determined as clutter. However, this method is only applicable to the RD spectrum with weak clutter intensity, and not serious expansion in the range dimension and velocity dimension. When it is necessary to detect a target in the RD spectrum with large clutter intensity and serious expansion in the Doppler dimension and range dimension, due to the serious expansion of the clutter, it affects the amplitude of the RD spectrum unit, and then seriously affects the sliding window mean value, ultimately resulting in the failure of target detection. In addition, when the number of points in the RD spectrum is too large, the operation time of the sliding window mean method will also increase exponentially, making it difficult to meet the real-time requirement of engineering implementation.

[0004] In summary, the existing algorithms are restricted by the position of the clutter in the Doppler dimension of the RD spectrum, and cannot achieve target detection in the scenario with large clutter intensity and relatively serious expansion in the range dimension and Doppler dimension; and the algorithms are time-consuming and it is difficult to meet the real-time requirement of engineering implementation. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention provides a target detection method, device and electronic device under a strong clutter background. The technical problems to be solved by the present invention are achieved through the following technical solutions: In a first aspect, the present invention proposes a target detection method under a strong clutter background, including: Step 1: Obtain the echo data of the moving platform radar and perform pulse compression processing to obtain the pulse-compressed data containing clutter, targets, and noise; Step 2: Perform MTD processing on the pulse-compressed data to obtain a two-dimensional data matrix of the RD spectrum; Step 3: Based on the distribution characteristics of clutter on the RD spectrum, perform standardization processing on the data in different range dimensions of the two-dimensional data matrix of the RD spectrum to obtain a standardized data matrix; Step 4: Construct connected regions for the standardized data matrix; Step 5: Based on the area of each connected region, determine the clutter region in the RD spectrum and remove the clutter region to obtain a data matrix containing targets and noise; Step 6: Based on the data matrix containing targets and noise, perform target aggregation to achieve target detection.

[0006] In a second aspect, the present invention proposes a target detection device under a strong clutter background for implementing the method proposed in the first aspect of the present invention. The device includes: A pulse compression module for obtaining the echo data of the moving platform radar and performing pulse compression processing to obtain the pulse-compressed data containing clutter, targets, and noise; An MTD module for performing MTD processing on the pulse-compressed data to obtain a two-dimensional data matrix of the RD spectrum; A standardization module for performing standardization processing on the data in different range dimensions of the two-dimensional data matrix of the RD spectrum based on the distribution characteristics of clutter on the RD spectrum to obtain a standardized data matrix; A connection module for constructing connected regions for the standardized data matrix; A clutter removal module for determining the clutter region in the RD spectrum based on the area of each connected region and removing the clutter region to obtain a data matrix containing targets and noise; A target aggregation module for performing target aggregation based on the data matrix containing targets and noise to achieve target detection.

[0007] In a third aspect, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the method steps provided in the first aspect of the present invention.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method steps provided in the first aspect of the present invention are implemented.

[0009] Advantages of the present invention: A target detection method under a strong clutter background provided by the present invention first performs pulse compression processing and MTD processing on the acquired radar echo data to obtain a two-dimensional data matrix of the RD spectrum; then performs normalization processing on the data in different range dimensions of the two-dimensional data matrix of the RD spectrum, and constructs a connected domain for the normalized data matrix; then determines the clutter region in the RD spectrum according to the area of each connected domain, and eliminates the clutter region to obtain a data matrix containing the target and noise; finally, based on the data matrix containing the target and noise, target condensation is performed to achieve target detection. This method uses the characteristic of severe range dimension expansion of clutter for range dimension normalization to distinguish clutter and targets, so as to perform target detection. When there is relative motion between the clutter and the radar, it is not affected by the position of the clutter in the Doppler dimension of the RD spectrum, and the clutter can still be eliminated in the RD spectrum to find the target. It is applicable to target detection in scenarios with strong clutter, severe range dimension and Doppler dimension expansion; and the algorithm is simple, easy to understand, has short time consumption, is easy to implement in engineering, and can meet the engineering real-time requirements.

[0010] The present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0011] Figure 1 is a schematic flowchart of a target detection method under a strong clutter background provided by an embodiment of the present invention; Figure 2 is a schematic flowchart of another target detection method under a strong clutter background provided by an embodiment of the present invention; Figure 3 is a range-Doppler three-dimensional diagram of a certain moving platform diving to pursue a target provided by an embodiment of the present invention; Figure 4 is a result diagram of normalizing the two-dimensional data matrix of the RD spectrum obtained by MTD processing column by column provided by an embodiment of the present invention; Figure 5 is a structural block diagram of a target detection device under a strong clutter background provided by an embodiment of the present invention. Detailed Embodiments

[0012] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] The first aspect of the present invention provides a target detection method under a strong clutter background. Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a target detection method under a strong clutter background provided by an embodiment of the present invention. The method mainly includes the following steps: Step 1: Obtain the echo data of the moving platform radar and perform pulse compression processing to obtain the data after pulse compression containing clutter, target, and noise.

[0014] Specifically, first, receive the echo signal within one coherent accumulation frame of the moving platform radar; among them, one frame of data includes 64 pulses, each pulse collects 1024 points, for a total of point data.

[0015] Then, perform pulse compression processing on the received radar echo signal to obtain the data after pulse compression containing clutter, target, and noise. Among them, the formula for pulse compression is: ; In the formula, represents the pulse compression result, represents the radar echo data, represents the for Fourier transform, represents the radar transmit signal, represents the for Fourier transform and conjugate transpose, represents the inverse Fourier transform.

[0016] Step 2: Perform MTD processing on the data after pulse compression to obtain a two-dimensional data matrix of the RD spectrum.

[0017] The MTD (Moving Target Detection) algorithm is a radar signal processing technology for detecting moving targets. This method identifies the motion state of targets by analyzing the Doppler shift and related characteristics in the radar echo signal. First, the received echo signal is subjected to a fast Fourier transform (FFT) in the Doppler dimension to separate target signals with different velocities. Then, by analyzing the FFT results, the radial velocity of each target is calculated. For the specific implementation process of the MTD algorithm, reference can be made to existing related technologies, and this embodiment will not be described in detail here.

[0018] In this embodiment, after performing MTD processing on the pulse-compressed data, a two-dimensional data matrix is obtained. Each data point in this matrix corresponds to an amplitude, and the corresponding three-dimensional graph is the RD spectrum.

[0019] It can be understood that since in step 1, the echo signals within one coherent accumulation frame obtained include point data, after MTD processing, the size of the obtained two-dimensional data matrix is also ; where 64 represents 64 velocity dimensions, corresponding to the rows of the matrix, that is, one row represents one velocity dimension; 1024 represents 1024 distance dimensions, corresponding to the columns of the matrix, that is, one column represents one distance dimension.

[0020] Step 3: Based on the distribution characteristics of clutter on the RD spectrum, perform normalization processing on the data of different distance dimensions in the two-dimensional data matrix of the RD spectrum to obtain a normalized data matrix.

[0021] Specifically, based on the distribution characteristic that clutter is more severely broadened in the distance dimension of the RD spectrum, in this embodiment, data normalization processing is performed on the amplitudes of 64 point units in the same distance dimension of the two-dimensional data matrix obtained in step 2, and this processing operation is performed separately for each distance dimension to obtain the final normalization result. Specifically as follows: First, calculate the average value and standard deviation of the data of each distance dimension in the two-dimensional data matrix of the RD spectrum. The calculation formulas are: ; ; In the formula, represents the average value of the data of one distance dimension, that is, the average value of the data in the same column, represents the number of velocity dimensions. In this embodiment, it is 64, that is, the number of rows of the two-dimensional data matrix of the RD spectrum, represents the th data point of one distance dimension, represents the standard deviation of the data of one distance dimension; Then, based on the average value and the standard deviation, the data in the corresponding distance dimension is standardized to obtain a standardized data matrix; the formula for the standardization process is: ; In the formula, represents the data point after standardization. This data shows the degree of deviation of the data point relative to the mean value in units of the standard deviation.

[0022] In this embodiment, the above standardization operation is performed on 64 data points in the same column of the two-dimensional data matrix obtained in step 2. The same operation process is performed on different columns, and each data point is converted to its position relative to the mean value of the data set and scaled according to the standard deviation to adjust data with different ranges and scales to a unified scale, thereby eliminating the influence of the dimension between different data features and reducing the influence of strong clutter with too high amplitude on target detection.

[0023] It can be understood that since the amplitudes in the same distance dimension after the standardization transformation are in the same dimension, the influence of the expansion of clutter in the Doppler dimension on target detection can be suppressed.

[0024] Step 4: Construct a connected domain for the standardized data matrix.

[0025] First, the standardized data matrix obtained in step 3 is grouped by rows. Each data point of each pixel is compared with a first preset threshold. If the data point of the current pixel is less than the first preset threshold, the pixel is marked as 0; otherwise, the pixel is marked as 1. If the number of continuously marked 0 pixels in a certain row is less than the preset quantity, all the continuously marked 0 pixels are modified to 1, and finally a first marking matrix, denoted as matrix_A, is formed.

[0026] Optionally, as an implementation method, since the amplitude of the target in the RD spectrum gradually increases as the radar gets closer to the target, in this embodiment, some adaptive adjustments can be made when setting the first preset threshold. For example, when the initial scene radar is 6000 m away from the target, the first preset threshold is set to the mean value of the standardized data matrix (the mean value is 0 at this time). As the radar gets closer to the target, the threshold should be appropriately increased.

[0027] It should be noted that in the process of forming the first marking matrix, in this embodiment, all independent pixel points with the number of continuously marked 0 pixels in each row less than the preset quantity (for example, it can be 2 or 3) are modified to 1 to facilitate connection with the pixels marked as 1.

[0028] It can be understood that the pixels marked as 1 can be tentatively determined as targets, while the pixels marked as 0 can be directly excluded.

[0029] Then, pixel connection is performed on the first marking matrix based on 4-connectivity to form several connected regions.

[0030] Specifically, for the pixels marked as 1, when the pixels adjacent to it in the four directions of up, down, left, and right are 1, they are regarded as connected to this pixel, so that the pixels marked as 1 in the first marking matrix are divided into several connected regions.

[0031] Step 5: Based on the area of each connected region, determine the clutter region in the RD spectrum, and eliminate the clutter region to obtain a data matrix containing targets and noise.

[0032] First, calculate the area of each connected region marked as 1 and compare it with the second preset threshold: if it exceeds the second preset threshold, then determine this connected region as the clutter region in the RD spectrum.

[0033] Optionally, in this embodiment, the area of each connected region can be understood as the number of pixels in this connected region. When it is less than the second preset threshold, it is initially determined as a target or noise. If the area of this connected region is greater than the second preset threshold, it can be considered that this region is a clutter region.

[0034] It should be noted that as the radar gets closer and closer to the target, the target will occupy more and more pixel points in the RD spectrum. Therefore, when setting the second preset threshold in this embodiment, some adaptive adjustments can be made.

[0035] Optionally, as an implementation method, the initial scene radar is 6000m away from the target, and the second preset threshold is 20; as the radar gets closer and closer to the target and the target occupies more and more pixel points in the RD spectrum, this threshold can be appropriately increased.

[0036] Then, modify the labels of all connected regions determined as clutter regions to 0 to eliminate the clutter region and obtain a data matrix containing only targets and noise, denoted as the second marking matrix, represented as matrix_B.

[0037] Step 6: Based on the data matrix containing targets and noise, perform target aggregation to achieve target detection.

[0038] Specifically, multiply the second marking matrix obtained in step 5 by the two-dimensional data matrix of the RD spectrum obtained in step 2. Denote the resulting matrix as matrix_C. Perform target condensation on this matrix and observe whether the target can be detected. If the target can be detected, considering that noise may become a false target, determine the one with the largest amplitude as the target. If there are multiple targets, several targets with the largest amplitudes can be determined as targets according to the specific situation, and output the speed and distance information of the targets.

[0039] It should be noted that for the specific implementation process of target condensation, reference can be made to the existing related technologies, and this embodiment will not elaborate on it here.

[0040] Further, please refer to Figure 2 , Figure 2 which is a schematic flowchart of another target detection method under strong clutter background provided by the embodiment of the present invention. In this embodiment, the method further includes: if no target information is detected in step 6, modify the radar transmission waveform parameters and return to step 1 to re-perform target detection.

[0041] Specifically, due to the characteristic of clutter in spectrum broadening, target information may be submerged in the clutter, and step 6 may not be able to detect the target information. At this time, the frequency of the radar transmission waveform can be reduced, thereby reducing the maximum unambiguous speed and further improving the resolution in the Doppler dimension. By improving the resolution in the Doppler dimension, the broadened part of the clutter in the Doppler dimension can be made more refined, separating the target from the position units where the strong clutter broadens in the Doppler dimension. Then, obtain the radar echo data of the next frame of the moving platform and perform the data processing flow from step 1 to step 6, so as to achieve target detection under strong clutter background.

[0042] A target detection method under strong clutter background provided by the present invention first performs pulse compression processing and MTD processing on the obtained radar echo data to obtain a two-dimensional data matrix of the RD spectrum; then performs standardization processing on the data in different range dimensions of the two-dimensional data matrix of the RD spectrum, and constructs connected domains for the standardized data matrix; then determines the clutter regions in the RD spectrum according to the area of each connected domain, and eliminates the clutter regions to obtain a data matrix containing only targets and noise; finally, based on the data matrix containing only targets and noise, perform target condensation to achieve target detection. This method uses the characteristic that the clutter range dimension expands severely to perform range dimension standardization to distinguish clutter and targets, so as to perform target detection. When there is relative motion between the clutter and the radar, it can ignore the position of the clutter in the Doppler dimension of the RD spectrum and still eliminate the clutter in the RD spectrum to find the target. It is applicable to target detection in scenarios where the clutter intensity is large and the range dimension and Doppler dimension expand relatively severely; and the algorithm is simple and easy to understand, with small time consumption and easy to implement in engineering, and can meet the engineering real-time requirements.

[0043] The following is a verification and description of a target detection method proposed by the present invention under a strong clutter background through simulation experiments.

[0044] Please refer to Figure 3 , Figure 3 , which is the range-Doppler 3D map of a moving platform diving and pursuing a target provided by an embodiment of the present invention. Among them, multiple targets have been marked in Figure 3 , Figure 3 . In the velocity dimension coordinate of , the interval from 20 to 35 is clutter. It can be observed that the clutter expands severely in the range dimension and Doppler dimension, especially in the range dimension, spanning the entire maximum unambiguous range.

[0045] Figure 4 is the result map after normalizing the two-dimensional data matrix of the RD spectrum obtained by MTD processing column by column according to an embodiment of the present invention. From the coordinate amplitude, it can be seen that after the normalization processing in the range dimension, the amplitudes of the target and clutter are adjusted to a unified scale, the clutter amplitude decreases, and the target amplitude increases. Thus, a connected domain can be constructed from the normalization result to remove the clutter and achieve target detection.

[0046] Thus, the effectiveness of the present invention is verified.

[0047] Based on the same inventive concept, the second aspect of the present invention also provides a target detection device under a strong clutter background.

[0048] Please refer to Figure 5 , Figure 5 , which is the structural block diagram of a target detection device under a strong clutter background provided by an embodiment of the present invention. The device includes: A pulse compression module, configured to obtain the echo data of the moving platform radar and perform pulse compression processing to obtain the pulse-compressed data containing clutter, targets, and noise; An MTD module, configured to perform MTD processing on the pulse-compressed data to obtain a two-dimensional data matrix of the RD spectrum; A normalization module, configured to perform normalization processing on the data in different range dimensions of the two-dimensional data matrix of the RD spectrum based on the distribution characteristics of the clutter on the RD spectrum to obtain a normalized data matrix; A connection module, configured to construct a connected domain for the normalized data matrix; A clutter removal module, configured to determine the clutter region in the RD spectrum based on the area of each connected domain and remove the clutter region to obtain a data matrix containing targets and noise; A target aggregation module, configured to perform target aggregation based on the data matrix containing targets and noise to achieve target detection.

[0049] Based on the same inventive concept, a third aspect of the present invention further provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus; wherein, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The memory is used to store computer programs. When the processor is used to execute the programs stored on the memory, it realizes the method steps provided in the first aspect of the present invention.

[0050] Based on the same inventive concept, a fourth aspect of the present invention further proposes a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it realizes the method steps provided in the first aspect of the present invention.

[0051] It should be noted that for the device, electronic device, and storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiments.

[0052] The device, electronic device, and storage medium of the embodiments of the present invention are respectively the device, electronic device, and storage medium applying the above-mentioned target detection method under strong clutter background. Then, all embodiments of the above-mentioned target detection method under strong clutter background are applicable to the device, electronic device, and storage medium, and can achieve the same or similar beneficial effects.

[0053] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0054] Although the present application is described in combination with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and implement other changes of the disclosed embodiments by viewing the drawings, the disclosed content, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality of situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0055] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, an apparatus (device), or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects, which are collectively referred to herein as "modules" or "systems". Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as part of the hardware, and can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0056] The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for detecting a target under a strong clutter background, characterized in that: include: Step 1: Acquire the echo data of the moving platform radar and perform pulse compression processing to obtain pulse compressed data containing clutter, target and noise; Step 2: performing MTD processing on the pulse compressed data to obtain a two-dimensional data matrix of the RD spectrum; Step 3: Based on the distribution characteristics of clutter on the RD spectrum, the data of different distance dimensions in the two-dimensional data matrix of the RD spectrum are standardized to obtain a standardized data matrix; Step 4: constructing a connected domain for the standardized data matrix; Step 5: Based on the area of ​​each connected domain, determine the clutter region in the RD spectrum, and remove the clutter region to obtain a data matrix containing the target and noise; Step 6: Based on the data matrix containing the target and noise, target aggregation is performed to achieve target detection.

2. The target detection method under strong clutter background according to claim 1, characterized in that: In step 1, the formula for pulse compression processing is: ； In the formula, represents the pulse compression result, Represents radar echo data, Express Perform Fourier transform, Indicates that the radar transmits a signal. Express Perform Fourier transform and take conjugate transpose, represents the inverse Fourier transform.

3. The target detection method under strong clutter background according to claim 1, characterized in that: Step 3 includes: The mean value and standard deviation of the data in each distance dimension in the two-dimensional data matrix of the RD spectrum are calculated using the following formula: ； ； In the formula, Represents the average value of data in a distance dimension, represents the number of velocity dimensions, that is, the number of rows of the two-dimensional data matrix of the RD spectrum, Represents the first data points, Represents the standard deviation of the data in a distance dimension; Based on the mean value and standard deviation, the data of the corresponding distance dimension is standardized to obtain a standardized data matrix; wherein the formula for the standardization is: ； In the formula, Represents data points Normalized data.

4. The target detection method under strong clutter background according to claim 1, characterized in that: Step 4 includes: The standardized data matrix is ​​grouped by rows, and the data point of each pixel is compared with a first preset threshold value respectively; if the data point of the current pixel is less than the first preset threshold value, the pixel is marked as 0; otherwise, the pixel is marked as 1; if the number of pixels continuously marked as 0 in a row is less than a preset number, the pixels continuously marked as 0 are modified to 1, and finally a first marking matrix is ​​formed; The pixels of the first marking matrix are connected based on 4-connectivity to form a plurality of connected domains.

5. The target detection method under strong clutter background according to claim 4, characterized in that: Step 5 includes: Calculate the area of ​​each connected domain marked as 1, and compare it with a second preset threshold: if it exceeds the second preset threshold, determine that the connected domain is a clutter region in the RD spectrum; The marks of all connected domains determined as clutter areas are changed to 0 to eliminate the clutter areas, and a data matrix containing targets and noise is obtained, which is recorded as the second marking matrix.

6. The target detection method under strong clutter background according to claim 5, characterized in that: Step 6 includes: The second label matrix is ​​multiplied with the two-dimensional data matrix of the RD spectrum, and target aggregation is performed on the matrix obtained after the multiplication to obtain target information, thereby realizing target detection.

7. The target detection method under strong clutter background according to claim 1, characterized in that: Also includes: If the target information is not detected in step 6, the radar transmission waveform parameters are modified and the process returns to step 1 to perform target detection again.

8. A target detection device under strong clutter background, used to implement the method according to any one of claims 1 to 7, characterized in that: The device comprises: The pulse compression module is used to obtain the echo data of the moving platform radar and perform pulse compression processing to obtain pulse compressed data containing clutter, targets and noise; An MTD module is used to perform MTD processing on the pulse compressed data to obtain a two-dimensional data matrix of the RD spectrum; A standardization module, used for standardizing data of different distance dimensions in the two-dimensional data matrix of the RD spectrum based on the distribution characteristics of clutter on the RD spectrum to obtain a standardized data matrix; A connectivity module, used for constructing a connectivity domain for the standardized data matrix; A clutter removal module is used to determine the clutter area in the RD spectrum based on the area of ​​each connected domain, and remove the clutter area to obtain a data matrix containing the target and noise; The target aggregation module is used to perform target aggregation based on the data matrix containing the target and the noise to achieve target detection.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the method steps described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method steps described in any one of claims 1 to 7 can be implemented.

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