Optoelectronic coordinated synthetic aperture radar moving target detection method, device and medium

Abstract

The present invention relates to an optoelectronically coordinated synthetic aperture radar moving target detection method, device, and medium. The method comprises the following steps: constructing an optical system, wherein light in the optical system passes through a reflective phase-type spatial light modulator (SLM1) and then a reflective phase-type spatial light modulator (SLM2) for frequency-domain matched filtering, and then propagates to a camera to generate multiple coarse images; performing target detection on each of the multiple coarse images in parallel to obtain a single moving target detection result, and synthesizing the single moving target detection results to obtain a final detection result. Compared with existing technologies, the present invention has the advantages of reducing the time complexity of moving target detection.

Description

Technical Field

[0001] The present invention relates to the field of radar moving target detection, and in particular to an optoelectronic coordinated synthetic aperture radar moving target detection method, device and medium. Background Art

[0002] As a core representative of active microwave remote sensing technology, Synthetic Aperture Radar (SAR) has demonstrated outstanding application value in military reconnaissance, disaster emergency response, geological exploration, and global environmental monitoring due to its all-day, all-weather imaging capabilities, as well as technical advantages such as high resolution and strong penetration. 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 moving targets of interest can be quickly screened out and only the target information is transmitted, the efficiency will be greatly improved. Therefore, the present invention focuses on the moving targets of interest and proposes an optoelectronic coordinated synthetic aperture radar moving target detection method. This method is mainly targeted at single-channel SAR systems and aims to quickly detect targets.

[0003] Early single-channel SAR moving target detection methods were mostly based on Doppler center frequency and Doppler modulation rate. In 1971, R.K. Raney proposed a Doppler frequency-based moving target detection method, which uses the shift in the Doppler center frequency caused by range velocity to distinguish moving from stationary targets. However, its limitations include being unable to detect targets with only azimuth velocity and requiring the target's spectrum to lie (or partially lie) outside the clutter spectrum. Later, A. Freeman proposed a prefilter method, but this suffers from Doppler ambiguity and blind velocity issues. Based on variations in the Doppler modulation 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, it struggles to distinguish multiple targets when they are close together. Furthermore, the RDM method requires Fourier transforms and multiple correlation operations, resulting in high computational complexity. JRFienup proposed an algorithm for detecting moving targets by focusing in 2001, but this method also has the defects of the RDM method.

[0004] In addition, researchers have applied a series of time-frequency analysis methods to moving target detection, such as the Wigner-Wille distribution, short-time Fourier transform, and fractional Fourier transform. RP Perry et al. proposed a moving target detection and imaging method based on the Keystone transform, and a series of algorithms have been developed based on this. However, both Keystone transform-based and time-frequency analysis-based methods suffer from the common drawback of high computational complexity. These algorithms are more suitable for imaging or parameter estimation of moving targets rather than detection. In 2006, M. Jahangir proposed a moving target detection method based on image shadows. However, not every moving target can be identified with a corresponding shadow, so this method has limitations. In recent years, with the rise of deep learning, a number of deep learning-based moving target detection algorithms have emerged. However, these algorithms inevitably suffer from the inherent drawbacks of deep learning methods, such as high computational resource consumption, strong data dependence, poor interpretability, and the risk of overfitting.

[0005] Existing moving target detection algorithms generally have the problem of large computational complexity. In particular, the Fourier transform algorithm has a time complexity of O(N 2 ), even using 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 coordinated synthetic aperture radar moving target detection method, device and medium in order to reduce the time complexity of moving target detection. The present invention uses a matched filter in an optical system to perform two-dimensional joint matched filtering to obtain a coarse image, and then performs target detection on the coarse image, 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:

[0008] A method for detecting moving targets using an optoelectronically coordinated synthetic aperture radar comprises the following steps:

[0009] Construct an optical system. The light in the optical system passes through the reflective phase spatial light modulator SLM1 and then passes through the reflective phase spatial light modulator SLM2 for frequency domain matched filtering. The light is then transmitted to the camera to generate multiple coarse imaging images.

[0010] Target detection is performed on each of the multiple coarse images in parallel to obtain a single moving target detection result. The single moving target detection results are combined to obtain the final detection result. The target detection process for one image is as follows:

[0011] The image is preprocessed, and line segment detection is performed on the preprocessed image to identify significant line segments. The significant line segments are merged and length-screened to obtain a single moving target detection result.

[0012] Furthermore, the specific steps of performing matched filtering in the frequency domain are:

[0013] Construct an expression for the matched filter, set M different imaging parameters, and use matched filters with different imaging parameters to obtain M coarse images of the scene, where each image corresponds to one imaging parameter.

[0014] Furthermore, the specific steps of constructing a matched filter are:

[0015] Set target edge x The speed of the axis is ;along y The speed of the axis is , and the radar moves at a constant speed Along parallel to x The direction of the axis movement, assuming The target is located at P , the radar antenna phase center is located at where represents the initial position of the target, Indicates the height of the radar's antenna phase center;

[0016] An expression for the distance between the radar and the target is constructed, and an approximate expression is obtained using the Taylor expansion formula. An expression for the echo signal is constructed based on the approximate expression, and an expression for the matched filter is obtained based on the echo signal expression.

[0017] Furthermore, the expression of the matched filter is:

[0018] ;

[0019] in, is the distance frequency variable, is the azimuthal frequency variable, is the FM slope of the transmitted linear FM signal, is the speed of light, is the carrier center frequency, is the imaging parameter.

[0020] Furthermore, the imaging parameters are

[0021] ;

[0022] in, , .

[0023] Furthermore, the specific steps for setting M different imaging parameters are:

[0024] Assume the minimum slant distance of the scene is , the maximum slope distance is , the imaging parameters satisfy:

[0025] ;

[0026] in, is the minimum value of the imaging parameter, is the maximum value of the imaging parameter, is the equivalent speed range;

[0027] Equivalent speed for:

[0028] ;

[0029] From the interval M values ​​are selected at equal intervals as M different imaging parameters.

[0030] Furthermore, the image is preprocessed, line segment detection is performed on the preprocessed image, significant line segments are identified, and significant line segments are merged and length-screened to obtain a single moving target detection result. The specific steps are as follows:

[0031] Denoise a single image, then enhance the edge information of the image using the Canny edge detection algorithm and morphological closing operation;

[0032] Perform Hough transform on the image to detect line segments, and extract significant line segments from the detected line segments through peak detection;

[0033] The significant line segments are merged and their lengths are screened to obtain the number, length and center coordinates of the line segments as the single moving target detection results.

[0034] Furthermore, the specific steps for synthesizing the single moving target detection results to obtain the final detection result are:

[0035] Analyze the line segment lengths corresponding to the same target in different single moving target detection results. If the nth imaging parameter , that is, K n If 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 the direction of arrangement increase on both sides, then the imaging parameters are closest to the accurate imaging parameters of the target and the target is retained. The arrangement direction refers to the direction in which the single moving target detection results are arranged in the order of imaging parameters from large to small or from small to large.

[0036] If the line segment length of the same target keeps increasing or decreasing in all target detection results, then the target is not within the range of interest. The line segments of the target in all moving target detection results are deleted. The number of targets left in the final target detection result is the number of detected targets. The average of the center coordinates of the line segments of the remaining targets in all target detection results is used as the imaging position of the remaining targets. The imaging positions of all remaining targets and the closest imaging parameters are used as the final detection results.

[0037] In another aspect of the present invention, an optoelectronic coordinated synthetic aperture radar moving target detection device is proposed, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the above-mentioned optoelectronic coordinated synthetic aperture radar moving target detection method.

[0038] In another aspect of the present invention, a computer-readable storage medium is provided, on which a program is stored. When the program is executed by a processor, the above-mentioned optoelectronic coordinated synthetic aperture radar moving target detection method is implemented.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention employs a two-dimensional joint matched filtering optical system for imaging. A reflective phase-type spatial light modulator (SLM) is incorporated into the optical system, which utilizes a two-dimensional joint processing scheme for range and azimuth. Compared to existing methods that process range and azimuth separately, the reflective phase-type SLM and its two-dimensional joint matched filtering method facilitate parallel optical processing. The optical system itself is two-dimensional in data processing, and the present invention utilizes this characteristic to implement a matching two-dimensional joint matched filtering, eliminating the need for additional resources. Light propagation from SLM1 to SLM2 and from SLM2 to the camera can be processed at the speed of light using a Fourier transform algorithm, increasing the Fourier transform speed and enabling high-speed imaging. During the two-dimensional joint matched filtering process, M images are obtained using filters with parameters within a certain range. Adjusting the parameter M can achieve different priorities between detection speed and accuracy, enhancing image acquisition flexibility. The present invention can achieve nanosecond imaging for coarse imaging. The complexity of executing the Fourier transform program no longer constrains the target detection algorithm, and the subsequent target detection computational complexity is only O(N). BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the present invention;

[0042] Figure 2 is the optical system diagram;

[0043] Figure 3 Flowchart of the detection algorithm used for a single image;

[0044] Figure 4 Target detection results in sea clutter background. DETAILED DESCRIPTION

[0045] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0046] Example 1:

[0047] The present invention proposes a method for detecting moving targets using an optoelectronically coordinated synthetic aperture radar. The flow chart of the method is as follows: Figure 1 As shown, the method includes the following steps:

[0048] Construct an optical system. The light in the optical system passes through the reflective phase spatial light modulator SLM1 and then passes through the reflective phase spatial light modulator SLM2 for frequency domain matched filtering. The light is then transmitted to the camera to generate multiple coarse imaging images.

[0049] Target detection is performed on each of the multiple coarse images in parallel to obtain a single moving target detection result. The single moving target detection results are combined to obtain the final detection result. The target detection process for one image is as follows:

[0050] The image is preprocessed, and line segment detection is performed on the preprocessed image to identify significant line segments. The significant line segments are merged and length-screened to obtain a single moving target detection result.

[0051] The present invention can detect multiple targets simultaneously with a time complexity of only O(N), and allows users to filter moving targets according to customized speed ranges, and can also obtain rough information on the imaging position and imaging parameters of the detected targets.

[0052] The process consists of two phases: coarse imaging and target detection, as shown in Figure 1. First, imaging parameter boundaries are dynamically defined based on the velocity range of the target of interest. Several sets of differentiated imaging parameters are selected and coarse imaging is performed on the raw SAR echoes using an optical system, resulting in several images of the entire scene. A computer then executes a target detection algorithm on these coarse imaging results. The detection results from all images are comprehensively considered to screen out targets of interest, and their number and imaging locations are determined.

[0053] Figure 2 schematically illustrates the optical system used in the coarse imaging stage. The system consists of two reflective phase-type spatial light modulators (SLM1 and SLM2) and two beamsplitters (BS1 and BS2). The camera and both SLMs are connected to a single computer (PC), enabling information exchange between optics and electronics. SLM1 loads the raw synthetic aperture radar (SAR) echo onto the laser, SLM2 performs frequency-domain matched filtering, and the camera receives the resulting image and transmits it back to the computer. The system's processing time, measured in nanoseconds, is the time required for light to propagate from SLM1 to the camera.

[0054] Depend on Figure 2 As can be seen, the optical system includes a laser, a polarizer, a camera, two SLMs, and two beam splitters (BS). The specific imaging process for a single image is as follows: laser light emitted by the laser passes through the polarizer and the first beam splitter (BS1). After reflection from SLM1, it is phase modulated, thus carrying the information of the original SAR echo. From SLM1, the light propagates to the first beam splitter (BS1) and the second (SLM2) beam splitter (BS2), and then to SLM2. This process is equivalent to a two-dimensional Fourier transform. Subsequently, frequency-domain matched filtering is performed at SLM2. From SLM2, the light is transmitted back to the second (SLM2) beam splitter (BS2). The process of propagating to the camera is equivalent to an inverse two-dimensional Fourier transform, ultimately resulting in the imaging result on the camera. The frequency-domain matched filtering formula used is derived as follows. The imaging result obtained by the camera is a coarse image, a grayscale image.

[0055] Since the synthetic aperture time is usually very short, it is assumed that the target moves in a straight line at a uniform speed during this time. x The speed of the axis is ;along y The speed of the axis is The radar moves at a constant speed Along parallel to x The direction of the axis. Assumptions The target is located at P Department ( ), the radar antenna phase center is located at The distance between the radar and the target is as shown in formula (1):

[0056] (1)

[0057] in is the azimuth time, , Using Taylor expansion formula, equation (1) can be approximated as:

[0058] (2)

[0059] Without considering the amplitude, the radar baseband signal can be expressed as:

[0060] (3)

[0061] in is the distance to time, is a constant, is the wavelength of the electromagnetic wave emitted by the radar, is the FM slope of the transmitted linear FM signal, is the speed of light. Performing a distance Fourier transform on (3) and applying the stationary phase principle yields:

[0062] (4)

[0063] in is another constant, is the carrier center frequency, is the frequency variable in the range direction. Performing the Fourier transform in the azimuth direction on (4), applying the stationary phase principle again and ignoring the first-order term of the frequency (the first-order term only affects the imaging position but not the imaging accuracy), we can obtain:

[0064] (5)

[0065] in is another constant, is the azimuth frequency variable. Therefore, the expression of the matched filter can be set as

[0066] (6)

[0067] make

[0068] (7)

[0069] but

[0070] (8)

[0071] The equivalent speed of the target is defined as

[0072] (9)

[0073] Then there is

[0074] (10)

[0075] Assume that the user only needs to know the equivalent speed The target within the interval is of interest. Assuming the radar parameters are known, the only imaging parameters that need to be determined in (8) are Assume that the minimum slant distance of the scene is , the maximum slope distance is , then

[0076] (11)

[0077] In the interval Select M values ​​at equal intervals within (including the endpoints of the interval in the selection) as The value of is used to obtain different filters and further obtain M rough imaging results of the scene. M is an integer greater than or equal to 3, which can be 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 are generated. The values ​​are arranged in order from large to small or from small to large, namely K1, K2, ... K M .

[0078] The target detection stage requires running the target detection algorithm on multiple scene images in the coarse imaging stage, and then synthesizing them to get the final result. The flowchart of the detection algorithm used for a single image is as follows Figure 3 As shown, the algorithm consists of three main stages: preprocessing, line segment detection, and postprocessing. First, a grayscale image (the coarse image) is read. In the preprocessing stage, the image is denoised using either a Gaussian filter or a non-local means filter. Edge information is then extracted using the Canny edge detection algorithm. Next, morphological closing operations are used to enhance edge continuity. In the line segment detection stage, a Hough transform is performed, limiting the angle range to 80° to 100°. This specifically detects vertical or nearly vertical line segments, thereby reducing computational overhead. Subsequently, the program uses an accumulator to identify possible line segments and extracts the most significant ones through peak detection. The postprocessing stage primarily combines and filters the line segments extracted in the previous step to prevent the same target from being detected as multiple line segments and removes very short line segments (as these are likely noise rather than point targets). These three stages determine the number of line segments, the length of each segment, and the center coordinates (which can be determined from the endpoint coordinates). Multiple images can be tested in parallel to improve overall efficiency.

[0079] When integrating the detection results of multiple images, it is necessary to cluster the detection results of different images based on 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 target is detected is less than M-2, all information about the target is deleted. In addition, by analyzing the length information of the line segments corresponding to the same target in different images, information about the target imaging parameters can be obtained. If the number of times the target is detected is less than M-2, all information about the target is deleted. n The length of the lower line segment is the shortest and the length of the line segments on both sides increases, so it is considered that Kn The closest to the target's accurate imaging parameters. If the length of the line segment is constantly increasing or decreasing, it indicates that the target's equivalent velocity is not within the range of interest, so the target is deleted. The number of targets remaining is the number of detected targets, and the average of the center coordinates of all line segments corresponding to the same target is the target's imaging position.

[0080] The present invention has conducted a verification experiment using an optical system. Gaussian noise was added to the echo containing a moving point target. The mean and standard deviation of the noise were equal to the mean of the echo amplitude. The speed of the point target in the range direction was 5m / s, and the speed in the azimuth direction was 1m / s. The optical system was used to perform coarse imaging on it, and 9 imaging results under different errors were obtained and target detection was performed. Finally, the target was successfully detected and the one closest to the real imaging parameters of the target was selected from the 9 imaging parameters. 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. The target was also successfully detected after coarse imaging using the optical system, and the detection results are as follows. Figure 4 The line segment of the fourth picture is the shortest, and the imaging parameters corresponding to this picture are also closest to the real imaging parameters.

[0081] To further test the performance of the proposed target detection method, 100 detections were performed. For each detection, an echo containing a moving point target was generated and superimposed with Gaussian noise. The mean and standard deviation of the noise were equal to the mean of the echo amplitude. The velocity and position of the point target were randomly generated. The target was imaged once using imaging parameters with random errors, and target detection was performed on the imaged results. The target was correctly detected 97 times. If each target is imaged nine times, as in coarse imaging, and a successful detection is considered successful if 7 correct detections are achieved, the probability of successful detection is approximately 99.8%.

[0082] The present invention proposes an optoelectronic coordinated synthetic aperture radar moving target detection method, which 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 position and imaging parameters. This method can reduce the computational complexity of target detection to O(N), while the complexity of existing target detection algorithms involving Fourier transform is at least O(NlogN). In addition, the method also allows the parameter M to be adjusted 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 certain situations where real-time target discovery is required, and brings inspiration to the research of optoelectronic coordinated imaging.

[0083] Example 2:

[0084] The present invention also provides a schematic structural diagram of an optoelectronic coordinated synthetic aperture radar moving target detection device corresponding to Example 1. At the hardware level, the optoelectronic coordinated synthetic aperture radar moving target detection device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include 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 Of course, in addition to software implementation, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0085] Improvements to a technology can be clearly categorized as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with technological advancements, many process flow improvements can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using physical hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can integrate a digital system onto a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly performed using software called a "logic compiler." This is similar to the software compilers used during program development. Before compilation, the original code must be written in a specific programming language, called a hardware description language (HDL). There are many types of HDL, including 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, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0086] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., 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 controllers 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 memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller purely in computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing the various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can be considered both a software module implementing the method and a structure within the hardware component.

[0087] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0088] For the convenience of description, the above device is described as being divided into various units according to their 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.

[0089] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0094] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0095] The present invention also provides a computer-readable medium for implementing the method of Example 1. Computer-readable media include permanent and non-permanent, removable and non-removable media that can implement information storage 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0097] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0098] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0099] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

Claims

1. A method for detecting moving targets using an optoelectronically coordinated synthetic aperture radar, characterized in that: The method comprises the following steps: Construct an optical system. The light in the optical system passes through the reflective phase spatial light modulator SLM1 and then passes through the reflective phase spatial light modulator SLM2 for frequency domain matched filtering. The light is then transmitted to the camera to generate multiple coarse imaging images. Target detection is performed on each of the multiple coarse images in parallel to obtain a single moving target detection result. The single moving target detection results are combined to obtain the final detection result. The target detection process for one image is as follows: The image is preprocessed, and line segment detection is performed on the preprocessed image to identify significant line segments, merge the significant line segments and filter their lengths to obtain a single moving target detection result. The specific steps of performing matched filtering in the frequency domain are as follows: Construct an expression for a matched filter, set M different actual imaging parameters, and use matched filters with different actual imaging parameters to obtain M coarse images of the scene, where each image corresponds to one actual imaging parameter. The specific steps for constructing the expression for the matched filter are as follows: Set target edge x The speed of the axis is ;along y The speed of the axis is , and the radar moves at a constant speed Along parallel to x The direction of the axis movement, assuming The target is located at P , the radar antenna phase center is located at where represents the initial position of the target, Indicates the height of the radar's antenna phase center; Construct an expression for 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 an expression for the echo signal, and based on the expression for the echo signal, obtain an expression for the matched filter. The expression for the matched filter is: ； in, is the distance frequency variable, is the azimuthal frequency variable, is the FM slope of the transmitted linear FM signal, is the speed of light, is the carrier center frequency, is an undetermined imaging parameter; the undetermined imaging parameter satisfies: ； Equivalent speed for: Then we have: in, , ; The specific steps for setting M different actual imaging parameters are: Assume the minimum slant distance of the scene is , the maximum slope distance is The minimum and maximum values ​​of the imaging parameters satisfy: ； in, is the minimum value of the imaging parameter, is the maximum value of the imaging parameter, is the equivalent speed range; From the interval M values ​​are selected at equal intervals as M different actual imaging parameters.

2. The method for detecting moving targets using an optoelectronic coordinated synthetic aperture radar according to claim 1, wherein: The specific steps for preprocessing the image, performing line segment detection on the preprocessed image, identifying significant line segments, merging the significant line segments and screening their lengths to obtain the single moving target detection result are as follows: Denoise a single image, then enhance the edge information of the image using the Canny edge detection algorithm and morphological closing operation; Perform Hough transform on the image to detect line segments, and extract significant line segments from the detected line segments through peak detection; The significant line segments are merged and their lengths are screened to obtain the number, length and center coordinates of the line segments as the single moving target detection results.

3. The method for detecting moving targets using an optoelectronic coordinated synthetic aperture radar according to claim 2, wherein: The specific steps to obtain the final detection result by integrating the single moving target detection results are: Analyze the line segment lengths corresponding to the same target in different single moving target detection results. If the nth imaging parameter , that is, K n If 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 the direction of arrangement increase on both sides, then the imaging parameters are closest to the accurate imaging parameters of the target and the target is retained. The arrangement direction refers to the direction in which the single moving target detection results are arranged in the order of imaging parameters from large to small or from small to large. If the line segment length of the same target keeps increasing or decreasing in all target detection results, then the target is not within the range of interest. The line segments of the target in all moving target detection results are deleted. The number of targets left in the final target detection result is the number of detected targets. The average of the center coordinates of the line segments of the remaining targets in all target detection results is used as the imaging position of the remaining targets. The imaging positions of all remaining targets and the closest imaging parameters are used as the final detection results.

4. An optoelectronic coordinated synthetic aperture radar moving target detection device, characterized in that: The invention comprises a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement an optoelectronic coordinated synthetic aperture radar moving target detection method according to any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that A program is stored thereon, and when the program is executed by a processor, the optoelectronic coordinated synthetic aperture radar moving target detection method described in any one of claims 1 to 3 is implemented.

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