Detection method for moving targets with specific velocities using high-temporal-phase SAR echo data

Abstract

This invention discloses a method for detecting moving targets at a specific velocity in high-temporal-phase SAR echo data. The method includes: dividing the high-temporal-phase SAR echo data to be detected into multiple sub-data sets according to a predetermined specific velocity and in chronological order; performing imaging processing on each sub-data set to obtain multiple sub-images; performing high-dimensional mapping on a sequential image composed of each sub-image to obtain a sequentially mapped image; determining a binary feature map of the sequentially mapped image based on the kernel distance of each pixel in the sequentially mapped image; and performing morphological closing operations on the binary feature map to obtain the detection result of the moving target at the specific velocity. This application can accurately detect moving targets at a specific velocity in SAR echo data, achieving directional detection.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, and in particular to a method for detecting moving targets at a specific velocity using high-temporal-phase SAR echo data. Background Technology

[0002] Synthetic Aperture Radar (SAR), as an all-weather, all-day long-range remote sensing technology, has wide applications in traffic control, environmental monitoring, and military reconnaissance. High-temporal-phase SAR imagery, as a new imaging modality, further improves temporal resolution, giving it a stronger capability in moving target detection.

[0003] Currently, existing SAR image moving target detection methods mainly utilize the Doppler characteristics and spatiotemporal joint distribution characteristics of moving targets to separate moving targets from background interference. While these methods can suppress clutter and detect moving targets to some extent, they cannot perform precise screening for moving targets at specific velocities, resulting in the presence of unwanted interfering moving targets in the detection results. Summary of the Invention

[0004] This invention provides a method for detecting moving targets at specific velocities in high-temporal-phase SAR echo data, which can accurately detect moving targets at specific velocities in SAR echo data and achieve directional detection. The technical solution is as follows:

[0005] On the one hand, a method for detecting moving targets at specific velocities in high-temporal-phase SAR echo data is provided, the method comprising:

[0006] Based on a predetermined specific velocity, the high-temporal-phase SAR echo data to be detected is divided into multiple sub-data according to the time sequence;

[0007] Each of the sub-data is subjected to imaging processing to obtain multiple sub-images;

[0008] The sequential image composed of each of the sub-images is mapped in a high dimension to obtain the sequential mapped image;

[0009] Based on the kernel distance of each pixel in the sequential mapping image, the binary feature map of the sequential mapping image is determined;

[0010] A morphological closing operation is performed on the binary feature map to obtain the detection result of the moving target at the specific velocity.

[0011] On the other hand, a device for detecting moving targets at specific velocities in high-temporal-phase SAR echo data is provided, the device comprising:

[0012] The segmentation unit is used to divide the high-temporal-phase SAR echo data to be detected into multiple sub-data based on a predetermined specific velocity and in chronological order.

[0013] An imaging unit is used to perform imaging processing on each of the sub-data to obtain multiple sub-images;

[0014] A mapping unit is used to perform high-dimensional mapping on the sequential image composed of each of the sub-images to obtain a sequentially mapped image;

[0015] The feature map determination unit is used to determine the binary feature map of the sequential mapping image based on the kernel distance of each pixel in the sequential mapping image.

[0016] The result determination unit is used to perform morphological closing operations on the binary feature map to obtain the detection result of the moving target at the specific velocity.

[0017] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the method for detecting moving targets at specific velocities in high temporal phase SAR echo data described above.

[0018] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the steps of the method for detecting moving targets at a specific velocity in high-temporal-phase SAR echo data described above are implemented.

[0019] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method for detecting moving targets at a specific velocity in high-temporal-phase SAR echo data as described above.

[0020] This invention provides a method for detecting moving targets at a specific velocity in high-temporal-phase SAR echo data. First, high-temporal-phase SAR echo data is used as input. Then, the echo data is divided into several sub-data sets according to a specific velocity, making the temporal domain characteristics of each sub-data set more obvious. Thus, when a sequential image composed of these sub-images is mapped in high dimension, the high-dimensional mapping result of the kernel function can be maximized, eliminating interference from other moving targets, clutter, and noise. Finally, by mapping the sequentially mapped image into a binary feature map and performing morphological closing operations on the binary feature map, moving targets at a specific velocity can be detected, while other moving targets and noise are eliminated, improving the accuracy of the detection results. Therefore, this application can accurately detect moving targets at a specific velocity in SAR echo data, achieving directional detection. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for detecting moving targets at specific velocities in high-temporal-phase SAR echo data according to an embodiment of the present invention;

[0023] Figure 2 This is a structural diagram of a device for detecting moving targets at specific velocities in high-temporal-phase SAR echo data according to an embodiment of the present invention;

[0024] Figure 3 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of moving targets and their velocities contained in high temporal phase SAR echo data provided in an embodiment of the present invention;

[0026] Figures 5-7 The methods described in this application and different specific speed pairs are respectively used. Figure 4 A schematic diagram showing the results of detecting moving targets. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] As mentioned earlier, the inventors discovered the following shortcomings in existing SAR moving target detection methods: On the one hand, while traditional methods can suppress clutter and detect moving targets to some extent, they are difficult to accurately screen for moving targets with specific velocities. On the other hand, deep learning methods rely too heavily on large-scale labeled data and consume significant computational resources, making it difficult to meet the real-time requirements of high-temporal-phase SAR for detecting moving targets with specific velocities.

[0029] Based on the above problems, the inventors proposed that by utilizing the high temporal resolution of high-temporal-resolution SAR echo data and changing the temporal domain characteristics of a moving target at a specific velocity, the high-dimensional mapping result of the kernel function can be maximized, and interference from other moving targets, clutter, and noise can be eliminated to achieve directional detection.

[0030] The following describes the specific implementation of the above concept.

[0031] Please refer to Figure 1 This invention provides a method for detecting moving targets at specific velocities in high-temporal-phase SAR echo data, the method comprising:

[0032] Step 100: Based on a predetermined specific velocity, the high-temporal-phase SAR echo data to be detected is divided into multiple sub-data in chronological order;

[0033] Step 102: Perform imaging processing on each sub-data to obtain multiple sub-images;

[0034] Step 104: Perform high-dimensional mapping on the sequential image composed of each sub-image to obtain the sequentially mapped image;

[0035] Step 106: Determine the binary feature map of the sequential mapping image based on the kernel distance of each pixel in the sequential mapping image;

[0036] Step 108: Perform morphological closing operation on the binary feature map to obtain the detection result of the moving target at a specific speed.

[0037] In this embodiment of the invention, high-temporal-phase SAR echo data is first used as input. Then, the echo data is divided into several sub-data based on a specific velocity, making the temporal domain characteristics of each sub-data more obvious. Thus, when the sequential image composed of the sub-images is mapped in high dimension, the high-dimensional mapping result of the kernel function can be maximized, eliminating interference from other moving targets, clutter, and noise. Finally, by mapping the sequentially mapped image into a binary feature map and performing morphological closing operations on the binary feature map, moving targets at a specific velocity can be detected, while other moving targets and noise are eliminated, improving the accuracy of the detection results. Therefore, this application can accurately detect moving targets at a specific velocity in SAR echo data, achieving directional detection.

[0038] The following description Figure 1 The execution method for each step is shown.

[0039] First, for step 100, based on a predetermined specific velocity, the high-temporal-phase SAR echo data to be detected is divided into multiple sub-data in chronological order.

[0040] In this step, the specific speed is a speed range, which can be determined according to the user's needs, such as 60-80 km / h, 120-130 km / h, etc. Since different moving targets have different speeds, different speeds can roughly characterize different target features. For example, the speed range of a car is significantly different from that of a high-speed train or an airplane.

[0041] In some implementations, the number of sub-data is determined based on a specific velocity, the satellite velocity acquiring the echo data, the Doppler modulation slope of the echo data, and the synthetic aperture time.

[0042] In this embodiment, the number of sub-data segments is matched with the velocity of the moving target, which allows the high-dimensional mapping of subsequent sequential images to obtain larger mapping values, thereby facilitating the differentiation of noise.

[0043] Therefore, the following formula is preferred for determining the number of segments, i.e. the number of sub-data:

[0044]

[0045] In the formula, n is the number of sub-data; V t For a specific speed; V s Satellite velocity for acquiring echo data; K a T represents the Doppler frequency modulation slope of the echo data. a The time for synthesizing the aperture is denoted as .

[0046] For step 102, each sub-data is processed for imaging to obtain multiple sub-images.

[0047] In this step, the purpose of imaging processing is to convert the echo data into image data that can be used for subsequent processing. Users can use imaging processing methods such as Range-Doppler algorithm (RDA), Chirp Scaling algorithm (CSA), wavenumber domain algorithm (ω-K algorithm), etc. Of course, other methods can also be used, and this application is not limited to them.

[0048] For step 104, the sequential image composed of each sub-image is subjected to high-dimensional mapping to obtain the sequential mapped image.

[0049] In this step, high-dimensional mapping can facilitate the extraction of features from moving targets.

[0050] In some implementations, the following kernel function can be used to perform high-dimensional mapping on the sequential image to obtain the sequentially mapped image:

[0051]

[0052] In the formula, ker p For kernel function; x q For elements in the previous time series segment; y q η represents the element in the subsequent time series segment; L represents the correlation modulation coefficient; W represents the total length of the time series; I represents the sliding window length; p represents the position of the high-dimensional mapping result along the time series; L-2W-I+1 represents the total length of the time series after mapping.

[0053] In the above mapping process, according to the mapping relationship, for the amplitude time series of each pixel, two sliding windows of length L and interval I are set from the starting position, and the amplitude segments within the window are sorted by intensity. The sliding windows are traversed temporally with a preset step size to calculate the similarity between two amplitude segments within the window. This process is repeated until all images are traversed, resulting in a sequentially mapped image. Through the above calculation, a sequential image of length L can be mapped to a sequentially mapped image of length L-2W-I+1, thus facilitating the extraction of features from moving targets.

[0054] For step 106, based on the kernel distance of each pixel in the sequentially mapped image, the binary feature map of the sequentially mapped image is determined, including:

[0055] Iterate through each cell in the sequentially mapped image, and for each cell visited, perform the following:

[0056] Normalize the kernel distance time series corresponding to the current pixel to obtain the normalized kernel distance time series; determine whether the maximum value in the normalized kernel distance time series is not less than a predetermined hard threshold; if yes, set the value of the current pixel to 1; otherwise, set the value of the current pixel to 0.

[0057] This process is repeated until every pixel has been traversed, resulting in a binary feature map of the sequentially mapped image.

[0058] In the above calculation process, pixels with values ​​less than the hard threshold are considered negative, meaning that no moving target was detected in that pixel; pixels with values ​​greater than or equal to the hard threshold are considered positive, meaning that a moving target was detected in that pixel. A binary feature image is obtained by traversing all pixels.

[0059] In some implementations, the kernel distance time series corresponding to each pixel is normalized using the following formula:

[0060]

[0061] In the formula, D i '(0,1,…,L-2W-I+1) represents the normalized kernel distance time series of the i-th pixel, where i = 1, 2, …, K, and K is the total number of pixels in the mapped image; D i (0,1,…,L-2W-I+1) represents the kernel distance time series of the i-th pixel before normalization; μ pi Let σ be the expected value of the kernel distance time series of the i-th pixel; pi Let be the standard deviation of the distance from the kernel of the i-th pixel to the time series.

[0062] In some implementations, the value of each cell is determined using the following decision formula:

[0063]

[0064] In the formula, thr is the hard threshold; C0 represents the negative class, i.e., the pixel value is 0; C1 represents the positive class, i.e., the pixel value is 1.

[0065] Finally, for step 108, the morphological closing operation is performed on the binary feature map using the following formula:

[0066]

[0067] In the formula, b is a binary image; t is a template function; · represents the closing operation; Indicates an etching operation; This indicates an expansion operation.

[0068] The detection image of a moving target at a specific speed obtained in this step can be used for subsequent applications such as target tracking and trajectory extraction.

[0069] To demonstrate the effectiveness of the method described in this application, the inventors used... Figure 4 The high-temporal SAR echo data containing five moving targets with different velocities is shown as input. The method of this application is used to perform three types of filtering and detection on the moving targets with different velocities in the data. The detection results are as follows:

[0070] (1) When the inventor sets the specific speed to 20-40 m / s, the test results are as follows: Figure 5 As shown in the figure, the method of this application can not only screen out targets with speeds in the range of 20-40 m / s, but also eliminate targets with speeds of 60 m / s and 80 m / s, thus meeting the detection requirements.

[0071] (2) When the inventor sets a specific speed to 60-80 m / s, the test results are as follows: Figure 6 As shown in the figure, the method of this application can not only screen out targets with speeds in the range of 60-80 m / s, but also eliminate targets with speeds of 20 m / s, 27 m / s and 40 m / s, thus meeting the detection requirements.

[0072] (3) When the inventor sets a specific speed to 20-80 m / s, the test results are as follows: Figure 7 As shown in the figure, the method of this application can filter out all targets within this speed range, thus meeting the detection requirements.

[0073] The results above show that by adjusting specific velocities, echo segmentation numbers, and subsequent processing parameters, low-speed, high-speed, or all moving targets can be screened out, respectively. Therefore, the method described in this application can accurately detect moving targets at specific velocities in SAR echo data, achieving directional detection.

[0074] like Figure 2, Figure 3 As shown, this invention provides a device for detecting moving targets at specific velocities in high-temporal-phase SAR echo data. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, such as... Figure 2 The diagram shown is a hardware architecture diagram of a computing device housing a detection device for moving targets at specific velocities in high-temporal-phase SAR echo data, as provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the computing device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0075] Please refer to Figure 3 This application provides a device for detecting moving targets at specific velocities in high-temporal-phase SAR echo data, comprising:

[0076] The segmentation unit 300 is used to segment the high-temporal-phase SAR echo data to be detected into multiple sub-data based on a predetermined specific velocity and in chronological order.

[0077] Imaging unit 302 is used to perform imaging processing on each sub-data to obtain multiple sub-images;

[0078] The mapping unit 304 is used to perform high-dimensional mapping on the sequential image composed of each sub-image to obtain a sequentially mapped image.

[0079] The feature map determination unit 306 is used to determine the binary feature map of the sequential mapping image based on the kernel distance of each pixel in the sequential mapping image.

[0080] The result determination unit 308 is used to perform morphological closing operations on the binary feature map to obtain the detection result of a moving target at a specific speed.

[0081] In some implementations, the number of sub-data is determined based on a specific velocity, the satellite velocity acquiring the echo data, the Doppler modulation slope of the echo data, and the synthetic aperture time.

[0082] In some implementations, the sequential image is mapped to a higher dimension using the following kernel function to obtain the sequentially mapped image:

[0083]

[0084] In the formula, ker p For kernel function; x qFor elements in the previous time series segment; y q η represents the element in the subsequent time series segment; L represents the correlation modulation coefficient; W represents the total length of the time series; I represents the sliding window length; p represents the position of the high-dimensional mapping result along the time series; L-2W-I+1 represents the total length of the time series after mapping.

[0085] In some implementations, the feature map determination unit 306 is used to perform the following operations:

[0086] Iterate through each cell in the sequentially mapped image, and for each cell visited, perform the following:

[0087] Normalize the kernel distance time series corresponding to the current pixel to obtain the normalized kernel distance time series; determine whether the maximum value in the normalized kernel distance time series is not less than a predetermined hard threshold; if yes, set the value of the current pixel to 1; otherwise, set the value of the current pixel to 0.

[0088] This process is repeated until every pixel has been traversed, resulting in a binary feature map of the sequentially mapped image.

[0089] In some implementations, the kernel distance time series corresponding to each pixel is normalized using the following formula:

[0090]

[0091] In the formula, D i '(0,1,…,L-2W-I+1) represents the normalized kernel distance time series of the i-th pixel, where i = 1, 2, …, K, and K is the total number of pixels in the mapped image; D i (0,1,…,L-2W-I+1) represents the kernel distance time series of the i-th pixel before normalization; μ pi Let σ be the expected value of the kernel distance time series of the i-th pixel; pi Let be the standard deviation of the distance from the kernel of the i-th pixel to the time series.

[0092] In some implementations, the morphological closing operation on the binary feature map is performed using the following formula:

[0093]

[0094] In the formula, b is a binary image; t is a template function; · represents the closing operation; Indicates an etching operation; This indicates an expansion operation.

[0095] It should be noted that the detection device for moving targets at specific velocities in high-temporal-phase SAR echo data provided in the above embodiments is only an example illustrating the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the detection device for moving targets at specific velocities in high-temporal-phase SAR echo data and the method embodiment for detecting moving targets at specific velocities in high-temporal-phase SAR echo data are based on the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0096] Embodiments of this application also provide a computer device, please refer to... Figure 3 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the method for detecting moving targets at specific speeds in high temporal phase SAR echo data provided in the above-described method embodiments.

[0097] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the method for detecting moving targets at specific speeds in high-temporal-phase SAR echo data provided in the above-described method embodiments.

[0098] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform the detection method for a specific velocity moving target in high temporal phase SAR echo data as described in any of the above embodiments.

[0099] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0100] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0101] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for detecting a specific velocity moving target from high temporal SAR echo data, characterized in that, The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: wherein The method comprises: p is a kernel function; x q is an element in the preceding time series segment; y q is an element in the following time series segment; The method comprises: is a correlation modulation coefficient; L is a total length of the time series; W is a sliding window length; I is a sliding window interval; p denotes a high-dimensional mapping result along a time series position; L - 2W - I + 1 is a total length of the mapped time series.

2. The method of claim 1, wherein, The method comprises:

3. The method of claim 1, wherein, The method comprises: The method comprises: The method comprises: The method comprises:

4. The method of claim 3, wherein, The method comprises: In the formula, For the first i Time series of kernel distances after pixel normalization i =1, 2, ..., K, where K is the total number of pixels in the mapped image; For the first i Time series of kernel distances before pixel normalization; For the first i The expectation of the distance time series from the kernel of each pixel; For the first i The standard deviation of the distance between the kernel of each pixel and the time series.

5. The method of claim 1, wherein, The method comprises: wherein b is a binary image; t is a template function; denotes a closing operation; denotes an erosion operation; denotes a dilation operation.

6. An apparatus for detecting a specific velocity moving target from high temporal phase SAR echo data, characterized in that, The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises:

7. A computer device, comprising: The method comprises:

8. A computer-readable storage medium, characterized in that, The method comprises:

9. 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