Photoelectric cooperative synthetic aperture radar moving target detection method and device, and medium

Abstract

The invention relates to a photoelectric cooperative synthetic aperture radar moving target detection method and device and a medium, and the method comprises the following steps: constructing an optical system, enabling light in the optical system to pass through a reflective phase type spatial light modulator SLM1, then to pass through a reflective phase type spatial light modulator SLM2 for frequency domain matched filtering, and then to be transmitted to a camera; generating a plurality of coarse imaging images; and performing target detection on each image of the plurality of coarse imaging images in parallel to obtain a single moving target detection result, and integrating the single moving target detection results to obtain a final detection result. Compared with the prior art, the method has the advantages of reducing the time complexity of moving target detection and the like.

Description

Technical Field

[0001] The present invention relates to the field of radar moving target detection, and more particularly to a method, apparatus, and medium for detecting moving targets by an optoelectronic collaborative synthetic aperture radar. Background Art

[0002] Synthetic Aperture Radar (SAR), as a core representative of active microwave remote sensing technology, with its all-weather and all-time imaging capabilities, as well as technical advantages such as high resolution and strong penetrability, has demonstrated excellent application value in military reconnaissance, disaster emergency response, geological exploration, and global environmental monitoring. The raw data generated by the SAR system is not only large in volume but also difficult to compress, which poses a severe challenge to real-time data transmission. It is worth noting that in specific application scenarios such as traffic monitoring and military reconnaissance, moving targets often carry key information. If the interesting moving targets can be quickly screened out and only the information of the targets is transmitted, the efficiency will be greatly improved. Therefore, the present invention focuses on the interesting moving targets and proposes a method for detecting moving targets by an optoelectronic collaborative synthetic aperture radar. This method mainly aims at a single-channel SAR system and is designed to quickly detect targets.

[0003] Most of the early single-channel SAR moving target detection methods were based on the Doppler center frequency and the Doppler frequency rate. In 1971, R. K. Raney proposed a moving target detection method based on the Doppler frequency, which uses the offset of the Doppler center frequency caused by the range velocity to distinguish moving targets from stationary targets. Its drawback is that it cannot detect targets with only azimuth velocity, and it requires the spectrum of the target to be located (or partially located) outside the clutter spectrum. Later, A. Freeman proposed the prefilter method, but there are problems of Doppler ambiguity and blind speed. Based on the change of the Doppler frequency rate, J. R. Moreira and W. Keydel proposed the Reflection Displacement Method (RDM). However, the RDM method also has some limitations: First, it cannot effectively detect targets with only range velocity; second, in the case where multiple targets are close in distance, it is difficult to distinguish each target. In addition, the RDM method requires Fourier transform and multiple correlation operations, and the computational complexity is relatively high. J.R. Fienup proposed an algorithm for detecting moving targets by focusing in 2001, but this method also has the same drawbacks as the RDM method.

[0004] In addition, scholars have also applied a series of time-frequency analysis methods to the detection of moving targets, such as the Wigner-Ville Distribution, short-time Fourier transform, fractional Fourier transform, etc. R. P. Perry et al. proposed a method for detecting and imaging moving targets based on the Keystone transform, and a series of algorithms have been developed on this basis. However, both the method based on the Keystone transform and the method based on time-frequency analysis generally have the drawback of large computational complexity. These algorithms are more suitable for imaging or parameter estimation of moving targets rather than detection. In 2006, M. Jahangir proposed a method for detecting moving targets based on image-domain shadows, but not every moving target can find a corresponding shadow, so this method has limitations. In recent years, with the upsurge of deep learning, some moving target detection algorithms based on deep learning have emerged one after another. However, these algorithms inevitably have the inherent defects of deep learning methods, such as large consumption of computing resources, strong data dependence, poor interpretability, and the risk of overfitting, etc.

[0005] Existing moving target detection algorithms generally have the pain point of large computational complexity. Especially the steps of the Fourier transform algorithm, whose time complexity is O(N 2 ), and even using the fast Fourier transform can only reduce the complexity to O(NlogN), and the detection of moving targets is still slow. Summary of the Invention

[0006] The purpose of the present invention is to provide an optoelectronic collaborative synthetic aperture radar moving target detection method, device and medium for reducing the time complexity of moving target detection. The present invention performs two-dimensional joint matched filtering by using a matched filter in an optical system to obtain a coarsely imaged image, and then performs target detection on the coarsely imaged picture, which can reduce the computational complexity of target detection to O(N).

[0007] The purpose of the present invention can be achieved by the following technical solutions: An optoelectronic collaborative synthetic aperture radar moving target detection method, the method comprising the following steps: Construct an optical system, the light in the optical system passes through the reflective phase-type spatial light modulator SLM1 and then passes through the reflective phase-type spatial light modulator SLM2 for frequency-domain matched filtering, and then propagates to the camera to generate multiple coarsely imaged images; Perform target detection on each of the multiple coarsely imaged images in parallel to obtain a single moving target detection result, and synthesize the single moving target detection results to obtain the final detection result, wherein the target detection process for one image is: Preprocess the image. After preprocessing, perform line segment detection on the image to identify significant line segments. Then, merge and screen the lengths of the significant line segments to obtain the single-frame moving target detection result.

[0008] Further, the specific steps for performing matched filtering in the frequency domain are as follows: Construct the expression of the matched filter, set M different imaging parameters, and use the matched filters with different imaging parameters to obtain the rough imaging images of M scenes. Each image corresponds to one imaging parameter.

[0009] Further, the specific steps for constructing the matched filter are as follows: Assume the velocity of the target along the x axis is ; the velocity along the y axis is , and the radar moves at a constant velocity along the direction parallel to the x axis. Assume that at the moving target is located at P , and the antenna phase center of the radar is located at , where represents the initial position of the target, represents the height of the antenna phase center of the radar; Construct the expression of the distance between the radar and the target, and use the Taylor expansion formula to obtain an approximate expression. Based on the approximate expression, construct the echo signal expression, and based on the echo signal expression, obtain the expression of the matched filter.

[0010] Further, the expression of the matched filter is: ; where, is the range frequency variable, is the azimuth frequency variable, is the chirp rate of the transmitted chirp signal, is the speed of light, is the carrier center frequency, is the imaging parameter.

[0011] Further, the imaging parameter is ; where, , .

[0012] Further, the specific steps for setting M different imaging parameters are as follows: Assume the minimum slant range of the scene is , the maximum slant range is , and the imaging parameter satisfies: ; Among them, is the minimum value of the imaging parameter, is the maximum value of the imaging parameter, is the equivalent velocity interval; Equivalent velocity is: ; Select M values at equal intervals from the interval as M different imaging parameters.

[0013] Furthermore, the specific steps for preprocessing the image, detecting line segments on the preprocessed image, identifying significant line segments, and merging and length screening the significant line segments to obtain the single-frame moving target detection result are as follows: Denoise the single-frame image, and then enhance the edge information of the image through the Canny edge detection algorithm and morphological closing operation; Perform the Hough transform on the image to achieve line segment detection, and extract significant line segments through peak detection among the detected line segments; Merge and length screen the significant line segments to obtain the number, length, and central coordinates of the line segments as the single-frame moving target detection result.

[0014] Furthermore, the specific steps for obtaining the final detection result by synthesizing the single-frame moving target detection results are as follows: Analyze the line segment lengths corresponding to the same target in different single-frame moving target detection results. If in the nth imaging parameter , that is, K n the line segment length in the corresponding target detection result is the shortest and the line segment lengths in the target detection results in the arrangement directions on both sides increase, then this imaging parameter is closest to the accurate imaging parameter of this target, and this target is retained. Among them, the arrangement direction refers to the direction formed by arranging the single-frame moving target detection results in descending or ascending order of the imaging parameter; If the line segment lengths of the same target are continuously increasing or decreasing in all target detection results, then this target is not within the range of interest, delete the line segments of this target in all moving target detection results, and the number of targets remaining in the final target detection result is the number of detected targets. The mean value of the central coordinates of the line segments of the remaining targets in all target detection results is used as the imaging position of the remaining targets, and the imaging positions of all the remaining targets and the closest imaging parameters are used as the final detection result.

[0015] On the other hand, the present invention also provides a synthetic aperture radar moving target detection device with optoelectronic collaboration, including a memory and one or more processors. An executable code is stored in the memory. When the one or more processors execute the executable code, it is used to implement the above-mentioned synthetic aperture radar moving target detection method with optoelectronic collaboration.

[0016] On the other hand, the present invention also provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements the above-mentioned synthetic aperture radar moving target detection method with optoelectronic collaboration.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention performs imaging by setting up an optical system for two-dimensional joint matched filtering. A reflective phase-type spatial light modulator is set in the optical system. The reflective phase-type spatial light modulator adopts a two-dimensional joint processing scheme in the range direction and azimuth direction. Compared with the existing method of separate processing in the range direction and azimuth direction, the reflective phase-type spatial light modulator and its two-dimensional joint matched filtering method in the present invention facilitate optical parallel processing. The optical system itself is two-dimensional processing in data processing. The present invention utilizes this characteristic to adopt a matching two-dimensional joint matched filtering without consuming additional resources. The propagation of light from SLM1 to SLM2 and from SLM2 to the camera can perform the Fourier transform algorithm at the speed of light, improving the speed of the Fourier transform and achieving high-speed imaging. During the two-dimensional joint matched filtering process, M images under filters with certain interval parameters are obtained. Adjusting the parameter M can achieve different emphases between the detection speed and accuracy, improving the flexibility of obtaining images. The present invention can achieve nanosecond-level imaging in rough imaging. The complexity of the program executing the Fourier transform is no longer a constraint on the target detection algorithm. The subsequent operation complexity of target detection is only O(N). BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of the present invention; Figure 2 is an optical system diagram; Figure 3 is a flowchart of the detection algorithm adopted for a single picture; Figure 4 is the target detection result under a sea clutter background. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and the detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0020] Embodiment 1: The present invention proposes a method for detecting moving targets in a synthetic aperture radar with optoelectronic collaboration. The flowchart of the method is as Figure 1 shown. The method includes the following steps: Construct an optical system. The light in the optical system passes through the reflective phase-type spatial light modulator SLM1 and then through the reflective phase-type spatial light modulator SLM2 for frequency-domain matched filtering, and then propagates to the camera to generate multiple rough imaging images; Perform target detection on each of the multiple rough imaging images in parallel to obtain the detection results of single moving targets, and synthesize the detection results of single moving targets to obtain the final detection result. The target detection process for one image is as follows: Preprocess the image, perform line segment detection on the preprocessed image, identify significant line segments, merge and screen the lengths of the significant line segments to obtain the detection results of single moving targets.

[0021] The present invention can detect multiple targets simultaneously, with a time complexity of only O(N), and allows users to screen moving targets according to a custom speed range. It can also obtain rough information on the imaging positions and imaging parameters of the detected targets.

[0022] It is divided into two stages: rough imaging and target detection. The schematic diagram is shown in Figure 1. First, dynamically delimit the imaging parameter boundaries based on the speed range of the target of interest, select several groups of differentiated imaging parameters, and use the optical system to perform rough imaging on the SAR raw echo to obtain several imaging maps of the entire scene. Subsequently, the computer executes the target detection algorithm on the multiple rough imaging results, and comprehensively considers the detection results of all pictures to screen out the targets of interest, and simultaneously obtains the number and imaging positions of these targets.

[0023] The schematic diagram of the optical system used in the rough imaging stage is shown in Figure 2. The main body of this system consists of two reflective phase-type spatial light modulators (SLM1 and SLM2) and two beam splitters (BS1 and BS2). The camera and the two SLMs are both connected to the same computer PC, thus realizing the information interaction between optics and electronics. Among them, SLM1 is used to load the synthetic aperture radar SAR raw echo onto the laser, SLM2 is used to achieve frequency-domain matched filtering, and the camera is used to receive the imaging result and transmit it back to the computer. The processing time of this system is the time required for light to propagate from SLM1 to the camera, which is of the order of nanoseconds.

[0024] By Figure 2It can be seen that the optical system includes a laser, a polarizer, a camera, two SLMs, and two beam splitters BS. The specific imaging process of a single picture is as follows: The laser beam emitted by the laser passes through the polarizer and the first beam splitter BS1, and then undergoes phase modulation after being reflected by SLM1, thereby carrying the information of the SAR raw echo. The light propagates from SLM1 to the first beam splitter BS1 and the second SLM2 beam splitter BS2, and then propagates to SLM2. This process is equivalent to performing a two-dimensional Fourier transform. Subsequently, frequency-domain matched filtering is completed at SLM2. The process of propagating back from SLM2 to the second SLM2 beam splitter BS2 and then to the camera is equivalent to performing an inverse two-dimensional Fourier transform, and finally the imaging result is obtained on the camera. The derivation of the adopted frequency-domain matched filtering formula is as follows. The imaging result obtained by the camera is the rough imaging image, which is a grayscale image.

[0025] Since the synthetic aperture time is usually very short, it is assumed that the target moves in a uniform straight line during this period. The target moves along the x axis at a speed of ; and along the y axis at a speed of . The radar moves at a constant speed along a direction parallel to the x axis. Let . Assume that at time, the moving target is located at P ([[]] ), and the antenna phase center of the radar is located at . Then the distance between the radar and the target is shown in Equation (1): (1) where is the azimuth time, , . Using the Taylor expansion formula, Equation (1) can be approximated as: (2) When not considering the amplitude, the radar baseband signal can be expressed as: (3) where is the range time, is a constant, is the wavelength of the electromagnetic wave emitted by the radar, is the frequency modulation slope of the linearly frequency-modulated signal emitted, is the speed of light. Performing a range Fourier transform on (3) and applying the stationary phase principle gives: (4) where is another constant, is the carrier center frequency, is the range - direction frequency variable. Performing azimuth - direction Fourier transform on (4), and applying the stationary - phase principle again and ignoring the first - order term of frequency (the first - order term only affects the imaging position but not the imaging accuracy), we can obtain: (5) where is another constant, is the azimuth - direction frequency variable. Therefore, the expression of the matched filter can be set as (6) Let (7) Then (8) Define the equivalent velocity of the target as (9) Then there is (10) Assume that the user is only interested in the targets with equivalent velocity in the interval. Assuming that the radar parameters are known, then the imaging parameter to be determined in (8) is only . Assume that the minimum slant range of the scene is , and the maximum slant range is , then there is (11) Select M values (including the interval endpoints) at equal intervals within the interval as the values of to obtain different filters and further obtain the rough imaging results of M scenes. M is an integer greater than or equal to 3, which can be specifically selected according to needs. The larger M is, the higher the detection accuracy and the more accurate the estimation of imaging parameters, but the longer the time consumption. Therefore, a total of M values are generated, and they are arranged in descending or ascending order as K1, K2, …… K M .

[0026] In the target - detection stage, the target - detection algorithm needs to be run on the multiple scene images in the rough - imaging stage respectively, and then the final result is obtained comprehensively. The flowchart of the detection algorithm for a single image is as shown in Figure 3As shown in the figure, it is mainly divided into three stages: preprocessing, line segment detection, and postprocessing. First, the grayscale image, that is, the rough imaging image, is read. In the preprocessing stage, the image is first denoised, and a Gaussian filter or a non-local means filter can be selected. Then, the edge information in the image is extracted through the Canny edge detection algorithm. Next, morphological closing operation is used to enhance the coherence of the edges. In the line segment detection stage, first, the Hough transform is performed, and the angle range is limited to 80° to 100° to specifically detect vertical or nearly vertical line segments, which also reduces the computational amount by the way. Subsequently, the program uses the accumulator method to identify possible line segments and extracts the most significant line segments through peak detection. The postprocessing stage mainly merges and filters the lengths of the line segments extracted in the previous step to prevent the image of the same target from being detected as multiple line segments and to delete too short line segments (because too short line segments are very likely to be interference rather than the image of point targets). After these three stages, the number of line segments, the length of each line segment, and the central coordinates (which can be obtained from the endpoint coordinates) can be obtained. Multiple pictures can be detected in parallel to improve the overall efficiency.

[0027] When integrating the detection results of multiple pictures, it is necessary to cluster the detection results of different pictures according to the position information of the line segments so as to associate the information of the same target and obtain the total number of times the target is detected. If the number of times a certain target is detected is less than M - 2, all the information of this target is deleted. In addition, by analyzing the length information of the line segments corresponding to the same target in different pictures, information about the imaging parameters of the target can be obtained. If at a certain imaging parameter K n the line segment length is the shortest and the line segment lengths increase on both sides, then it is considered that K n is closest to the accurate imaging parameter of the target. If the line segment length has been increasing or decreasing, it means that the equivalent speed of this target is not within the range of interest, so this target should be deleted. The number of the finally remaining targets is the number of detected targets, and the mean value of the central coordinates of all the line segments corresponding to the same target is the imaging position of this target.

[0028] The present invention has carried out verification experiments using an optical system. Gaussian noise with a mean and standard deviation equal to the mean of the echo amplitude was added to the echo containing a moving point target. The range velocity of the point target was 5 m / s, and the azimuth velocity was 1 m / s. The optical system was used for rough imaging, and imaging results with 9 different errors were obtained and target detection was performed. Finally, the target was successfully detected, and the one closest to the true imaging parameter of the target was selected from 9 imaging parameters. Also, the same moving point target as above was added to the original echo containing sea clutter, and the backscattering coefficient of the point target was set to half of the mean of the echo amplitude. After rough imaging using the optical system, the target was also successfully detected, and the detection results are as Figure 4As shown. Among them, the line segment in the fourth picture is the shortest, and the imaging parameters corresponding to this picture are also the closest to the real imaging parameters.

[0029] To further test the performance of the proposed moving target detection method, 100 detections were carried out. Each detection generated an echo containing a moving point target and superimposed Gaussian noise. The mean and standard deviation of the noise were both equal to the mean of the echo amplitude. The speed and position of the point target were randomly generated. It was imaged once using the imaging parameters with random errors, and then target detection was performed on the imaging result. The number of times the target was correctly detected was 97 times. If each target was imaged 9 times like in rough imaging and it was considered that the detection was successful when the number of correct detections reached 7 times, then the probability of successful detection was approximately 99.8%.

[0030] The present invention proposes a method for detecting moving targets of an optoelectronic collaborative synthetic aperture radar. This method combines the high speed of optical processing and the flexibility of electronic processing, and can quickly detect moving targets within a custom speed range and obtain a rough estimate of their imaging positions and imaging parameters. This method can reduce the computational complexity of target detection to O(N), while the existing target detection algorithms involving Fourier transform have a complexity of at least O(NlogN). In addition, this method also allows the adjustment of parameter M to achieve different emphases between detection speed and accuracy. This method provides a new solution for SAR moving target detection, can play a role in some occasions with high requirements for real-time target discovery, and brings inspiration to the research of optoelectronic collaborative imaging.

[0031] Embodiment 2: The present invention also provides a schematic structural diagram of an optoelectronic collaborative synthetic aperture radar moving target detection device corresponding to Embodiment 1. At the hardware level, this optoelectronic collaborative synthetic aperture radar moving target detection device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 described method. Of course, in addition to the software implementation method, the present invention does not exclude other implementation methods, such as logical devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logical unit, and can also be hardware or logical devices.

[0032] Improvements to a technology can be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. The designer can program on their own to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a hardware description language (HDL), and there is not just one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0033] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0034] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0035] For the convenience of description, the above devices are described by dividing them into various units according to functions. Of course, when implementing the present invention, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0036] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0037] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block of the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 or means for implementing the functions specified in a block or plurality of blocks.

[0038] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 or means for implementing the functions specified in a block or plurality of blocks.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one flow or a plurality of flows and / or blocks Figure 1 or means for implementing the functions specified in a block or plurality of blocks.

[0040] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0041] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0042] The present invention also provides a computer-readable medium for implementing the method of Embodiment 1. The computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0043] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0044] Those skilled in the art should understand that the embodiments of the present invention may be provided as a method, a system or a computer program product. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0046] Each embodiment in the present invention is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiment.

Claims

1. An optoelectronic collaborative synthetic aperture radar moving target detection method, characterized in that, The method comprises the following steps: Construct an optical system. The light rays in the optical system pass through the reflective phase-type spatial light modulator SLM1 and then through the reflective phase-type spatial light modulator SLM2 for frequency-domain matched filtering, and then propagate to the camera to generate multiple rough imaging images; Perform object detection on each of the multiple rough imaging images in parallel to obtain a single moving object detection result, and synthesize the single moving object detection results to obtain the final detection result. The object detection process for one image is as follows: Preprocess the image. After preprocessing, perform line segment detection on the image, identify significant line segments, merge and screen the lengths of the significant line segments to obtain the single moving object detection result.

2. The method for detecting moving targets of a synthetic aperture radar with optoelectronic collaboration according to claim 1, wherein The specific steps for performing frequency-domain matched filtering are as follows: Construct the expression of the matched filter, set M different imaging parameters, and use the matched filters with different imaging parameters to obtain M rough imaging images of the scene, with each image corresponding to one imaging parameter.

3. The synthetic aperture radar moving target detection method with optoelectronic cooperation according to claim 2, wherein, The specific steps for constructing the expression of the matched filter are as follows: Let the velocity of the target along the x axis be ; and the velocity along the y axis be . And the radar moves at a constant velocity along a direction parallel to the x axis. Assume that at time the moving target is located at P , and the antenna phase center of the radar is located at , where represents the initial position of the target, and represents the height of the antenna phase center of the radar; Construct the expression of the distance between the radar and the target, and use the Taylor expansion formula to obtain an approximation. Based on the approximation, construct the echo signal expression, and based on the echo signal expression, obtain the expression of the matched filter.

4. A method for detecting moving targets of a synthetic aperture radar with optoelectronic cooperation according to claim 3, characterized in that, The expression of the matched filter is: ； Among them, is the range - direction frequency variable, is the azimuth - direction frequency variable, is the frequency modulation slope of the transmitted chirp signal, is the speed of light, is the carrier center frequency, is the imaging parameter.

5. The method for detecting moving targets of a synthetic aperture radar with optoelectronic collaboration according to claim 4, wherein The imaging parameter is ； Among them, , .

6. The method for detecting moving targets of a synthetic aperture radar with optoelectronic cooperation according to claim 5, wherein The specific steps for setting M different imaging parameters are as follows: Let the minimum slant range of the scene be , and the maximum slant range be . The imaging parameters satisfy: ； Among them, is the minimum value of the imaging parameter, is the maximum value of the imaging parameter, is the equivalent speed range; Equivalent speed is as follows: ； Select M values at equal intervals from the interval as M different imaging parameters.

7. A method for detecting moving targets of a synthetic aperture radar with optoelectronic cooperation according to claim 1, characterized in that, The specific steps for preprocessing the image, performing line segment detection on the preprocessed image, identifying significant line segments, merging and screening the lengths of the significant line segments to obtain the single moving object detection result are as follows: Denoise a single image, and then enhance the edge information of the image through the Canny edge detection algorithm and morphological closing operation; Perform Hough transform on the image to achieve line segment detection, and extract significant line segments through peak detection among the detected line segments; Merge and screen the lengths of the significant line segments to obtain the number, length, and center coordinates of the line segments as the single moving object detection result.

8. A method for detecting moving targets of a synthetic aperture radar with optoelectronic collaboration according to claim 7, characterized in that, The specific steps for synthesizing the single moving object detection results to obtain the final detection result are as follows: Analyze the line segment lengths corresponding to the same target in the moving target detection results of different single images. If in the nth imaging parameter , that is, K n the line segment length in the corresponding target detection result is the shortest and the line segment lengths in the target detection results arranged in both directions increase, then this imaging parameter is closest to the accurate imaging parameter of this target, and this target is retained. Among them, the arrangement direction refers to the direction formed by arranging the moving target detection results of a single image in descending or ascending order of imaging parameters; If the line segment length of the same target has been increasing or decreasing in all object detection results, then the target is not within the range of interest. Delete the line segments of the target in all moving object detection results. The number of targets remaining in the final object detection result is the number of detected targets. The mean of the center coordinates of the line segments of the remaining targets in all object detection results is used as the imaging position of the remaining target. The imaging positions of all remaining targets and the closest imaging parameters are used as the final detection result.

9. An optoelectronic collaborative synthetic aperture radar moving target detection device, characterized in that, It includes a memory and one or more processors. Executable code is stored in the memory. When the one or more processors execute the executable code, it is used to implement an optoelectronic collaborative synthetic aperture radar moving target detection method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, A program is stored thereon. When the program is executed by a processor, it implements the optoelectronic collaborative synthetic aperture radar moving target detection method according to any one of claims 1-8.

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