A near-field synthetic aperture radar data analysis and imaging processing method and device

Abstract

The application discloses a near-field synthetic aperture radar data analysis and imaging processing method and device, comprising: acquiring radar data and importing imaging frame speed data; generating a speed curve diagram based on the imaging frame speed data; and generating partial data images for part of the imaging frame speed data; performing error detection on the quality of the partial data images generated by the imaging frame speed data based on the speed curve diagram; when there is an error, correcting and fitting the current frame speed data, generating a single-frame synthetic aperture radar image based on the corrected speed data, and saving the single-frame synthetic aperture radar image; and continuously performing synthetic aperture radar imaging based on the fitted speed parameters, and synthesizing a high-frame-rate video stream. The application realizes systematic, real-time and visual SAR imaging by processing and analyzing original SAR data, modifying radar imaging configuration in real time, and simplifying the debugging steps of the influence of different parameters on imaging quality.

Description

Technical Field

[0001] This invention relates to the field of computer information processing technology, and in particular to a near-field synthetic aperture radar data analysis and imaging processing method, device, smart terminal, and storage medium. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an active Earth observation system that can be mounted on platforms such as aircraft, satellites, and automobiles. It can conduct all-weather, 24 / 7 Earth observations and has a certain degree of ground penetration capability. Due to its high resolution, which does not decrease with increasing distance, SAR is widely used in military and civilian fields. However, spaceborne platforms have limitations such as wide coverage areas, relatively fixed satellite orbits, and the inability to easily change the observation area. Airborne platforms have high requirements for takeoff and landing sites, while vehicle-mounted and UAV-borne platforms offer high flexibility, small size, and good maneuverability. Therefore, research on near-field SAR imaging has significant practical implications.

[0003] Traditional methods for near-field SAR (synthetic aperture radar) imaging employ different imaging algorithms to process and analyze the raw SAR data. Depending on the image quality, the estimated velocity data is filtered, corrected, and fitted to obtain a high-precision SAR image. In addition, radar imaging modes include frontal, side-view, and oblique-view, and the radar can be placed in front of, behind, to the left, or to the right of a vehicle. When the imaging mode or radar position changes, the data must be re-imported for analysis and processing. Different data reading paths will also result in different imaging results.

[0004] In existing technologies, near-field SAR (synthetic aperture radar) imaging platforms have multiple imaging algorithms, requiring direct modification of parameters from the code each time. The rationality of the correction data is judged based on the imaging quality, resulting in low operability of SAR imaging. The more data analyzed and processed, the longer the analysis and processing time becomes. In addition, the data path changes depending on the placement of the radar on a car or drone, leading to longer imaging time and lower imaging efficiency.

[0005] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a near-field synthetic aperture radar data analysis and imaging processing method, device, smart terminal and storage medium to address the above-mentioned deficiencies of the prior art. This application processes and analyzes the raw SAR data, modifies the radar imaging configuration in real time, simplifies the debugging steps of different parameters affecting the imaging quality, and realizes systematic, real-time and visualized SAR imaging.

[0007] The technical solution adopted by this invention to solve the problem is as follows:

[0008] A near-field synthetic aperture radar data analysis and imaging processing method, wherein the method includes:

[0009] Acquire radar data for imaging;

[0010] The acquired radar data is processed and analyzed, and the imaging frame velocity data corresponding to the radar data is imported; based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; and a portion of the imaging frame velocity data in the imaging frame velocity data is imaged to generate a partial data image.

[0011] Error detection is performed on the image quality of a portion of the data generated from the velocity data of the imaging frame based on the velocity curve;

[0012] If the image quality of some data generated from the current imaging frame velocity data has errors, the system controls the correction and fitting of the current frame velocity data, generates a single-frame synthetic aperture radar image based on the corrected velocity data and saves it, and saves the corrected and fitted velocity parameters in real time.

[0013] Continuous synthetic aperture radar imaging is performed based on the fitted velocity parameters, and a high frame rate video stream is synthesized.

[0014] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the step of acquiring radar data for imaging includes:

[0015] Acquire radar data for imaging;

[0016] Read the initial parameters of radar imaging, including sampling rate, frequency modulation frequency, repetition rate, number of sampling points, carrier frequency, oblique angle, range range, beamwidth, number of accumulation points, number of step points, enhancement factor, color space, and radar placement position.

[0017] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the steps of processing and analyzing the acquired radar data, importing imaging frame velocity data corresponding to the radar data, generating a velocity curve based on the imported imaging frame velocity data corresponding to the radar data, and imaging a portion of the imaging frame velocity data to generate a partial data image include:

[0018] The acquired radar data is processed and analyzed, and the imaging frame rate data from the initial parameters of the radar data is loaded.

[0019] Based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated;

[0020] Extract a portion of the imaging frame velocity data from the imaging frame velocity data, and then image the portion of the imaging frame velocity data to generate a partial data image.

[0021] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the step of generating a velocity curve based on the imported imaging frame velocity data corresponding to the radar data includes:

[0022] Based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is fitted as sample data points, and a velocity curve graph is generated.

[0023] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the step of error detection of the image quality of a portion of the imaging frame velocity data generated based on the velocity curve includes:

[0024] Obtain partial data images generated from imaging based on partial imaging frame velocity data:

[0025] The data image is controlled to be compared with the corresponding frame optical image at the position of the corresponding frame image in the velocity curve to determine whether there is an error;

[0026] If the position of the partial data image in the corresponding frame image of the velocity curve is deviated when compared with the corresponding frame optical image, it is determined that the quality of the partial data image generated from the velocity data of the current imaging frame has an error.

[0027] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the steps of controlling the correction of the current frame velocity data when the quality of some data images generated from the current imaging frame velocity data is incorrect, generating a single-frame synthetic aperture radar image based on the corrected velocity data and saving it, and saving the corrected and fitted velocity parameters in real time include:

[0028] If the image quality of some data generated from the current imaging frame rate data has errors, then the current frame rate data is corrected and fitted.

[0029] The error portion is filled by interpolation. The velocity curve is fitted using the Interp interpolation function or the spline interpolation function, and the corrected and fitted velocity parameters are saved in real time.

[0030] A single-frame synthetic aperture radar image is generated and saved based on the corrected velocity data.

[0031] The near-field synthetic aperture radar data analysis and imaging processing method, wherein the step of performing continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesizing a high frame rate video stream includes:

[0032] Based on the fitted velocity parameters, the BP algorithm or 2D-FFT algorithm is used to realize continuous synthetic aperture radar imaging, and then the images are synthesized into a high frame rate video stream and output.

[0033] A near-field synthetic aperture radar data analysis and imaging processing device, wherein the device comprises:

[0034] The acquisition module is used to acquire radar data for imaging.

[0035] The import and generation module is used to process and analyze the acquired radar data, import the imaging frame velocity data corresponding to the radar data, generate a velocity curve based on the imported imaging frame velocity data corresponding to the radar data, and generate partial data images by imaging a portion of the imaging frame velocity data.

[0036] The error detection module is used to detect errors in the image quality of a portion of the imaging frame velocity data based on the velocity curve.

[0037] The correction and fitting module is used to control the correction and fitting of the current frame velocity data when there are errors in the quality of some data images generated from the current imaging frame velocity data. Based on the corrected velocity data, a single-frame synthetic aperture radar image is generated and saved, and the corrected and fitted velocity parameters are saved in real time.

[0038] The imaging control module is used to perform continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesize high frame rate video streams.

[0039] A smart terminal includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising the method for performing any one of the methods.

[0040] A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform any of the methods described herein.

[0041] The beneficial effects of this invention are as follows: This invention provides a near-field synthetic aperture radar (SAR) data analysis and imaging processing method, apparatus, smart terminal, and storage medium. The method involves: acquiring radar data for imaging; processing and analyzing the acquired radar data, importing imaging frame velocity data corresponding to the radar data; generating a velocity curve based on the imported imaging frame velocity data; imaging a portion of the imaging frame velocity data to generate a partial data image; performing error detection on the quality of the partial data image generated from the imaging frame velocity data based on the velocity curve; if there is an error in the quality of the partial data image generated from the current imaging frame velocity data, controlling the correction and fitting of the current frame velocity data, generating and saving a single-frame SAR image based on the corrected velocity data, and saving the corrected and fitted velocity parameters in real time; performing continuous SAR imaging based on the fitted velocity parameters, and synthesizing a high frame rate video stream. Compared with traditional simulation software for analyzing and processing acquired data, this invention employs a near-field synthetic aperture radar data analysis and imaging processing method that can modify radar imaging configuration in real time, simplify the debugging steps for different parameters affecting imaging quality, and achieve systematic, real-time, and visualized SAR imaging. Attached Figure Description

[0042] 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 only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the near-field synthetic aperture radar data analysis and imaging processing method provided in an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the near-field synthetic aperture radar data analysis and imaging processing method provided in specific application embodiment 2 of the present invention.

[0045] Figure 3 This is a schematic diagram of the near-field synthetic aperture radar data analysis and imaging processing method provided in specific application embodiment 3 of the present invention.

[0046] Figure 4 This is a schematic diagram of the process of correcting the current frame rate data using the near-field synthetic aperture radar data analysis and imaging processing method provided in embodiment 4 of the present invention.

[0047] Figure 5This is a schematic diagram of the setting speed fine-tuning text box for the near-field synthetic aperture radar data analysis and imaging processing method provided in embodiment 4 of the present invention.

[0048] Figure 6 This is a schematic diagram of the first imaging process of the near-field synthetic aperture radar data analysis and imaging processing method provided in specific application embodiment 4 of the present invention.

[0049] Figure 7 This is a schematic diagram of the second imaging process of the near-field synthetic aperture radar data analysis and imaging processing method provided in specific application embodiment 4 of the present invention.

[0050] Figure 8 This is a schematic diagram of the near-field synthetic aperture radar data analysis and imaging processing device provided in an embodiment of the present invention.

[0051] Figure 9 This is a block diagram illustrating the internal structure of a smart terminal provided in an embodiment of the present invention.

[0052] Figure 10 The near-field synthetic aperture radar data analysis and imaging processing method provided in this embodiment of the invention includes a generation speed curve and a schematic diagram of the generated partial data image.

[0053] Figure 11 This invention provides an embodiment of the near-field synthetic aperture radar data analysis and imaging processing method, which compares the position of the corresponding frame image in the velocity curve with the corresponding frame optical image to determine the error.

[0054] Figure 12 An optical schematic diagram of the current frame SAR image of the near-field synthetic aperture radar data analysis and imaging processing method provided in this embodiment of the invention.

[0055] Figure 13 The corrected image of the near-field synthetic aperture radar data analysis and imaging processing method provided in this embodiment of the invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0057] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0058] Near-field refers to vehicle-mounted and drone-mounted applications, which differ from space-based applications and related algorithms.

[0059] Existing near-field synthetic aperture radar (SAR) data analysis and imaging processing suffers from low operability due to the existence of multiple imaging algorithms on SAR imaging platforms. Each time, parameters need to be directly modified from the code, and the rationality of the correction data needs to be judged based on the imaging quality. The more data analyzed and processed, the longer the analysis and processing time becomes. In addition, the data path changes depending on the placement of the radar on a car or drone, which leads to longer imaging time and lower imaging efficiency.

[0060] To address the problems of existing technologies, this invention provides a near-field synthetic aperture radar data analysis and imaging processing method.

[0061] Exemplary methods

[0062] like Figure 1 As shown in the illustration, this embodiment of the invention provides a near-field synthetic aperture radar (SAR) data analysis and imaging processing method, which can be applied to smart terminals. In this embodiment, the method includes the following steps:

[0063] Step S100: Acquire radar data for imaging;

[0064] In this embodiment of the invention, radar data for imaging is acquired in real time to facilitate the timeliness of imaging: wherein, the radar data includes radar echo data and initial parameter data for radar imaging.

[0065] The initial parameters of the radar imaging read include sampling rate, frequency modulation frequency, repetition rate, number of sampling points, carrier frequency, oblique angle, range, beamwidth, number of accumulation points, number of step points, enhancement factor, color space, radar placement position, and imaging frame rate data.

[0066] Step S200: Process and analyze the acquired radar data, import the imaging frame velocity data corresponding to the radar data; generate a velocity curve based on the imported imaging frame velocity data corresponding to the radar data; and generate partial data images by imaging a portion of the imaging frame velocity data.

[0067] In this step, the acquired radar data is processed and analyzed to extract the corresponding imaging frame velocity data. This imaging frame velocity data refers to the velocity data of the imaging frames, i.e., the frame rate of the imaging video frames. Then, based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; and partial data images are generated from a portion of the imaging frame velocity data.

[0068] Specifically, the acquired radar data is processed and analyzed, and the imaging frame velocity data in the initial parameters of the radar data is loaded; based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; wherein, the generation of the velocity curve specifically involves fitting a velocity curve using the imported imaging frame velocity data corresponding to the radar data as sample data points, that is, fitting a velocity curve using the frame rate of the imaging video frame as sample data points, and generating a velocity curve.

[0069] Then, partial imaging frame velocity data is extracted from the imaging frame velocity data, and this partial imaging frame velocity data is used to generate a partial data image. In this embodiment of the invention, partial imaging frame velocity data is extracted from the imaging frame velocity data as sample data, and a partial data image is generated to facilitate image quality comparison.

[0070] like Figure 10 As shown, Figure 10 The bottom left shows radar-related parameters, the top left shows a velocity curve (imaging frame velocity data, blue represents estimated velocity, and orange represents filtered velocity), and the right side shows a SAR image generated based on the imaging frame velocity data, which is a partial data image generated by imaging a portion of the imaging frame velocity data.

[0071] Step S300: Perform error detection on the image quality of a portion of the data generated from the velocity data of the imaging frame based on the velocity curve;

[0072] In this step, error detection is performed on the image quality of a portion of the generated image data based on the velocity curve. In this embodiment of the invention, the velocity data of the imaging frames corresponding to the radar data are used as sample data points to fit a velocity curve, which is a smooth velocity curve for image frame quality reference, used to avoid jitter and deviations in the generated image from the video frames.

[0073] In this step, specifically, a partial data image generated by imaging based on partial imaging frame velocity data is obtained: the partial data image is controlled to be compared and displayed with the corresponding frame optical image at the position of the corresponding frame image in the velocity curve to determine whether there is an error;

[0074] like Figure 11 and Figure 12 As shown, based on the optical image corresponding to the current frame SAR image, it can be seen that the double-step imaging is curved and an error occurs. By fine-tuning the current frame number, a corrected SAR image is obtained.

[0075] If a portion of the data image deviates from the corresponding frame image in the velocity curve when compared with the corresponding frame optical image, it is determined that the quality of the portion of the data image generated from the current imaging frame velocity data has an error. For example, if the current imaging frame velocity data velocity is 4.5, and the generated portion of the data image shows a deviation (e.g., slight distortion) from the corresponding frame image in the velocity curve when compared with the corresponding frame optical image, it is determined that the quality of the portion of the data image generated from the current imaging frame velocity data has an error.

[0076] Step S400: When there is an error in the quality of some data images generated from the current imaging frame velocity data, the current frame velocity data is corrected and fitted. A single-frame synthetic aperture radar image is generated and saved based on the corrected velocity data, and the corrected and fitted velocity parameters are saved in real time.

[0077] In this embodiment of the invention, when there is an error in the quality of some data images generated from the current imaging frame velocity data, the current frame velocity data is corrected, a single-frame synthetic aperture radar image is generated and saved based on the corrected velocity data, and the corrected and fitted velocity parameters are saved in real time.

[0078] Specifically, when the image quality of some data generated from the current imaging frame velocity data contains errors, the system controls the correction and fitting of the current frame velocity data; interpolation is performed to complete the error portion, and the velocity curve is fitted using the Interp interpolation function or the spline interpolation function, and the corrected and fitted velocity parameters are saved in real time; a single-frame synthetic aperture radar image is generated based on the corrected velocity data and saved. For example... Figure 13 As shown, Figure 13 This is the corrected image.

[0079] The interpolation function used in this embodiment of the invention addresses the issue that directly plotting a graph from some points may result in an unattractive image with jagged, scattered points. To achieve a smooth curve and remove these less-than-ideal points, an interpolation function is needed. This invention employs the Interp or spline interpolation function to fit the velocity curve and promptly correct the current imaging frame velocity data. This helps to avoid jitter and deviations in the generated image from video frames.

[0080] The `Interp` interpolation function, `interp()`, is a function that performs linear interpolation on one-dimensional and multi-dimensional arrays. It linearly interpolates the values ​​in a given set of x and y points and returns the function value at the given point. The `spline` function—a cubic spline interpolation function—is used for interpolation: y i =spline(x,y,x) i In the formula, x and y are the vectors of the interpolation points, x i Let y be the x-coordinate of the point to be found. iThis represents the ordinate value of the point to be calculated. Its purpose is to obtain the function value through cubic spline interpolation.

[0081] For example, if the current imaging frame rate is 3.5, and the generated image has deviations such as slight distortion, then the system will correct and fit the current frame rate data; interpolation will be used to complete the error portion. Fine-tuning can be performed in units of 0.1 or 0.01 to directly fit the portion of the image corresponding to the velocity curve, and the corrected and fitted velocity parameters will be saved in real time.

[0082] Step S500: Perform continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesize a high frame rate video stream.

[0083] In this step, continuous synthetic aperture radar (SAR) imaging is performed based on the fitted velocity parameters, and a high frame rate video stream is synthesized. Specifically, based on the fitted velocity parameters, the BP algorithm or 2D-FFT (two-dimensional fast Fourier transform) algorithm is used to achieve continuous SAR imaging, and then the images are synthesized into a high frame rate video stream and output.

[0084] The BP algorithm uses the squared network error objective function and employs gradient descent to calculate the minimum value of the objective function.

[0085] The Backpropagation (BP) algorithm consists of two processes: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample enters the network from the input layer, passes through the hidden layers, and is propagated layer by layer to the output layer. If the actual output of the output layer differs from the expected output (the mentor signal), backward propagation begins; if the actual output of the output layer matches the expected output (the mentor signal), the learning algorithm terminates. During backward propagation, the output error (the difference between the expected and actual output) is calculated by propagating it back along the original path, through the hidden layers, and up to the input layer. During this backpropagation, the error is distributed to each unit in each layer, obtaining the error signal for each unit, which is then used to adjust the weights of each unit. This calculation process is accomplished using gradient descent, continuously adjusting the weights and thresholds of neurons in each layer to minimize the error signal.

[0086] Thus, this invention performs continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesizes a high frame rate video stream, which can obtain more stable synthetic aperture radar images and video streams, and has fast imaging efficiency and good image stability.

[0087] As can be seen from the above, this application processes and analyzes the raw SAR data, modifies the radar imaging configuration in real time, simplifies the debugging steps for the impact of different parameters on imaging quality, and realizes systematic, real-time and visualized SAR imaging.

[0088] The following detailed description of a near-field synthetic aperture radar data analysis and imaging processing method of the present invention is provided through specific application examples:

[0089] Example 2

[0090] like Figure 2 As shown in the specific application embodiment of the present invention, a near-field synthetic aperture radar data analysis and imaging processing method includes the following steps:

[0091] Step S11: Locate the radar data folder to obtain the radar data files:

[0092] Embodiment 2 of the present invention processes and verifies image quality and performs parameter correction on radar data acquired from the front end. In this step, the folder address where the radar data file is located is found in the file system based on the target file name of the radar data.

[0093] Step S12: Determine whether the radar data target file has been found. If yes, proceed to step S13; otherwise, proceed to step S20.

[0094] This step determines whether the collected radar data target file has been found. If the radar data target file is not found, the process will exit. If the radar data target file is found, step S13 will be executed.

[0095] Step S13, Open Folder: Open the target folder.

[0096] In this embodiment of the invention, the target radar data file is located. Once the target radar data file is found, it is opened and the initial parameters of the radar imaging are read, including sampling rate, frequency modulation frequency, repetition rate, number of sampling points, carrier frequency, oblique angle, range range, beamwidth, number of accumulation points, number of step points, enhancement coefficient, color space, and radar placement position. In addition, data, image, speed, and video file markers are set. Then, the process proceeds to step S14.

[0097] In this embodiment of the invention, indicator lights for data, images, speed, and video files are set to check whether data has been transmitted. The indicator lights are red by default, and turn green if data is read in.

[0098] Step S14: Perform speed correction and fine-tune the speed:

[0099] This step involves extracting the imaging frame velocity data from the acquired radar data files, estimating the velocity of the imaging frame velocity data, filtering, correcting, and fitting the velocity estimation data, displaying the current frame of the dataset in the imaging coordinate area, and displaying the position of the current frame image on the velocity curve; updating the velocity files and storing all velocity files.

[0100] In this embodiment of the invention, the radar data can be selected from data collected by inertial navigation, etc. If the SAR (Synthetic Aperture Radar) image error is large when a certain frame is selected, the velocity of that frame needs to be fine-tuned. The number of frames selected will result in the number of velocity files, i.e., velocity points. The number of frames adjusted will result in different corrected velocity files. Then, a curve is fitted based on these points. This fitted velocity file will change with the different corrected velocities.

[0101] Step S15, SAR (Synthetic Aperture Radar) Imaging:

[0102] Based on the previously corrected parameters, SAR (Synthetic Aperture Radar) imaging is performed. In this embodiment of the invention, the imaging area is gridded, and the radar echo data, velocity data and initial parameters are analyzed and processed. The BP algorithm or 2D-FFT (Two-Dimensional Fast Fourier Transform) algorithm is used to realize continuous SAR (Synthetic Aperture Radar) imaging, and the images are generated into video files.

[0103] The BP algorithm uses the squared network error objective function and employs gradient descent to calculate the minimum value of the objective function.

[0104] The Backpropagation (BP) algorithm consists of two processes: forward propagation of the signal and backward propagation of the error. During forward propagation, the input sample enters the network from the input layer, passes through the hidden layers, and is propagated layer by layer to the output layer. If the actual output of the output layer differs from the expected output (the mentor signal), backward propagation begins; if the actual output of the output layer matches the expected output (the mentor signal), the learning algorithm terminates. During backward propagation, the output error (the difference between the expected and actual output) is calculated by propagating it back along the original path, through the hidden layers, and up to the input layer. During this backpropagation, the error is distributed to each unit in each layer, obtaining the error signal for each unit, which is then used to adjust the weights of each unit. This calculation process is accomplished using gradient descent, continuously adjusting the weights and thresholds of neurons in each layer to minimize the error signal.

[0105] Step S16: Save SAR (Synthetic Aperture Radar) images: Set the frame selection interval to select and save a portion of the SAR images as needed;

[0106] Step S17: Close the folder: Close the target folder.

[0107] Step S20: Exit.

[0108] Example 3

[0109] Furthermore, to achieve better imaging quality, the SAR (Synthetic Aperture Radar) image quality is verified before SAR (Synthetic Aperture Radar) image imaging.

[0110] like Figure 3 As shown, Embodiment 3 of the present invention provides a near-field synthetic aperture radar data analysis and imaging processing method, including the following steps:

[0111] Step S21: Locate the radar data folder: Based on the radar data target file name, locate the folder address where the file is located in the file system.

[0112] Step S22: Determine whether the radar data target file is found. If yes, proceed to step S24; otherwise, proceed to step S23. That is, if the target file is not found, exit; if the file is found, execute step S24.

[0113] Step S24: Open the folder: Open the radar data target folder and read the initial parameters of radar imaging from the Readme file, including sampling rate, frequency modulation frequency, repetition frequency, number of sampling points, carrier frequency, oblique angle, range range, beamwidth, number of accumulation points, number of step points, enhancement factor, color space, and radar placement position. Additionally, you can modify the text boxes for each parameter for fine-tuning. Also, set the indicator lights for data, image, velocity, and video files.

[0114] Step S25, Velocity Fine-tuning: Filter, correct, and fit the velocity estimation data; display the current frame of the dataset in the imaging coordinate area; display the position of the current frame image on the velocity curve; update the velocity file; and store all velocity files.

[0115] Step S26: Verify SAR imaging quality: Compare the standard optical video data and SAR image data generated in advance in the experimental scenario, and proceed to step S27.

[0116] Step S27: Determine if there is an error. If yes, return to step S25; otherwise, proceed to step S28.

[0117] The standard optical video data and SAR image data generated in advance in the experimental scenario will be compared. If there is a large error, the process will return to step S25.

[0118] The optical video is generated in advance in the experimental scene to verify the correctness of SAR imaging. By comparing the numerous targets in the optical video, such as trees, people, bicycles, school buildings and streetlights, it is determined whether the corresponding targets in each frame of SAR image have problems such as geometric distortion, low resolution and overlay. If the error is too large (severe distortion and bending, etc.), the process returns to step S25 to continue parameter correction.

[0119] Step S28, SAR (Synthetic Aperture Radar) Imaging: The imaging area is gridded, and the radar echo data, velocity data and initial parameters are analyzed and processed. Continuous SAR imaging is achieved using the BP algorithm or 2D-FFT algorithm, and the images are generated into video files.

[0120] Step S29: Save SAR (Synthetic Aperture Radar) images: Set the frame selection interval to select a portion of SAR images to save as needed;

[0121] Step S30: Close the folder: Close the target folder.

[0122] Example 4

[0123] Further technical solutions: A more detailed description of the speed correction steps is provided.

[0124] Specific implementation: Load existing speed data, move the frame number range slider or modify the current frame number text box to fine-tune the speed in real time.

[0125] For example, a convolutional neural network can be used to estimate the initial velocity data, or the velocity data can be directly collected using an inertial navigation system to obtain existing velocity data; there are no specific requirements for the existing velocity data. If there is no velocity data, it is necessary to adjust frame by frame to achieve high-quality imaging, which is time-consuming, labor-intensive, and inefficient. If velocity data is already available, only fine-tuning of the unsatisfactory data is required.

[0126] In this specific embodiment, the steps for correcting the current frame rate data are further detailed as follows: Figure 4 As shown, the speed correction step includes:

[0127] C21: Load existing speed data;

[0128] For example, import the imaging frame rate data corresponding to the radar data;

[0129] C22: Move the frame range slider or modify the frame number text box to a specific frame to display the SAR image of that frame at the current velocity;

[0130] For example, by controlling the slider to move the frame number range or modifying the frame number text box to a specific frame, the SAR image of that frame at the current velocity can be displayed through the preview interface.

[0131] C23: Adjust the current velocity within a certain range based on the SAR imaging quality, and display SAR image data in real time;

[0132] If the velocity of the imaging frame corresponding to the radar data is too high or too low, the SAR image will produce geometric distortion. For example, if the velocity is currently 3.5, the SAR image will have slight distortion. This distortion can be fine-tuned in units of 0.1 or 0.01 to obtain a high-resolution SAR image.

[0133] C24: Fine-tuning unsatisfactory speed data;

[0134] A speed fine-tuning text box has been set up. Modifying the speed of the current frame will cause the SAR image to change in real time. At this point, the rationality of the speed fine-tuning can be judged based on the SAR image quality. For example... Figure 5 As shown, this is a schematic diagram of the speed fine-tuning text box.

[0135] C25: Perform interpolation to complete the fine-tuned speed;

[0136] Interpolation completion uses the Interp or spline interpolation function to fit the velocity curve, which means fitting the sample data points created by the previous fine-tuning into a velocity curve.

[0137] C26: Updates the speed file, storing all speed data.

[0138] Further technical solutions: The present invention provides a more detailed description of the SAR (Synthetic Aperture Radar) imaging steps as follows:

[0139] Specifically: Regarding the SAR (Synthetic Aperture Radar) imaging steps: The imaging area is gridded, radar echo data, velocity data and initial parameters are analyzed and processed, and continuous SAR imaging is achieved using the BP algorithm or 2D-FFT algorithm. That is, after the parameters are set, continuous imaging can be achieved, and then the images are generated into video files.

[0140] Once all parameters are calibrated, simply click the imaging button. It will automatically and continuously capture images and save them as image files, and will display the remaining time for imaging to complete, somewhat similar to the run button in simulation software.

[0141] Specifically, the present invention is primarily applicable to near-field scenarios, meaning that the BP algorithm and the instantaneous SAR imaging algorithm (2D-FFT) are more suitable for vehicle-mounted and UAV-based platforms than airborne or spaceborne platforms. In addition, the 2D-FFT algorithm is groundbreaking.

[0142] The Backpropagation (BP) algorithm is a typical example of a precise time-domain processing algorithm. It calculates the round-trip time delay between all target points within the imaging area and the SAR platform, reconstructs the scene by compensating for phase differences caused by distance point-by-point, and then coherently superimposes the corresponding time-domain echoes along the aperture direction to achieve energy focusing of the image. The BP algorithm is universal, requires no range curvature correction, does not require any approximations, and produces clear images.

[0143] The instantaneous imaging algorithm employs sub-aperture imaging, dividing the broadband echo signal into multiple sub-echo datasets. The long aperture formed by the slow time azimuth dimension is then divided to obtain time-domain sub-aperture echoes. Since the sub-aperture corresponds to a sufficiently short time, it is called instantaneous imaging. This involves dividing the actual sub-aperture beam into several smaller sub-beams. The angle between the center of each sub-beam and the SAR's motion velocity vector differs, resulting in different radial velocities relative to the SAR. These differences in radial velocities form the Doppler frequency difference between the sub-beam echoes.

[0144] FFT is performed on the difference frequency signal to obtain the range-dimensional pulse compression signal, i.e., range-Doppler data. However, its corresponding range-angle information cannot be matched with the actual physical scene. Therefore, coordinate transformation is required to convert the range-Doppler data into range-azimuth dimension data.

[0145] Instantaneous SAR imaging (2D-FFT) has a shorter imaging time than BP imaging, but its imaging resolution is slightly lower due to the limitation of sub-aperture synthesis time, while BP imaging is a precise time-domain imaging.

[0146] Specifically, such as Figure 6 As shown, the specific steps involved in SAR (Synthetic Aperture Radar) imaging include:

[0147] E21: Perform Decirp processing on the echo data to obtain high-resolution range image (HRRP);

[0148] This involves de-modulating the echo data to obtain a high-resolution range profile (HRRP). An HRRP is the vector sum of the projections of the complex echoes from target scattering points acquired using broadband radar signals onto the radar ray. It provides information on the distribution of target scattering points along the range direction. Its key feature is that by emitting a high-frequency signal of a specific wavelength, the time and location of the reflection imaging are used to obtain a high-resolution range profile. It possesses important structural features of the target and is highly valuable for target identification and classification.

[0149] E22: Imaging grid parameter settings;

[0150] E23: Calculate the round-trip time delay between all target points within the imaging area and the SAR (Synthetic Aperture Radar) platform, and reconstruct the scene by compensating for the phase difference caused by the distance point by point;

[0151] E24: By coherently superimposing the corresponding time-domain echo signals along the aperture direction, a continuous image of the focused scene area can be obtained.

[0152] Furthermore, such as Figure 7 As shown, specific embodiment 3:

[0153] E31: Perform two FFTs on the echo data to obtain the range-Doppler data;

[0154] E32: Interpolation is used to transform the coordinates, converting the range-Doppler data to range-azimuth dimension data, to obtain the image of the imaging scene area.

[0155] As can be seen from the above, compared with traditional simulation software for analyzing and processing acquired data, this invention uses near-field synthetic aperture radar data analysis and imaging processing software, which can modify radar imaging configuration in real time, simplify the debugging steps for the influence of different parameters on imaging quality, and realize systematic, real-time and visualized SAR imaging.

[0156] Exemplary device

[0157] like Figure 8 As shown in the figure, an embodiment of the present invention provides a near-field synthetic aperture radar data analysis and imaging processing device, the device comprising:

[0158] Acquisition module 510 is used to acquire radar data for imaging;

[0159] The import and generation module 520 is used to process and analyze the acquired radar data, import the imaging frame velocity data corresponding to the radar data, generate a velocity curve based on the imported imaging frame velocity data corresponding to the radar data, and generate partial data images by imaging a portion of the imaging frame velocity data.

[0160] Error detection module 530 is used to detect errors in the image quality of a portion of the image generated from the velocity data of the imaging frame based on the velocity curve.

[0161] The correction and fitting module 540 is used to control the correction and fitting of the current frame velocity data when there is an error in the quality of some data images generated from the current imaging frame velocity data, generate a single frame synthetic aperture radar image based on the corrected velocity data and save it, and save the corrected and fitted velocity parameters in real time.

[0162] The imaging control module 550 is used for continuous synthetic aperture radar imaging based on the fitted velocity parameters and for synthesizing high frame rate video streams.

[0163] Based on the above embodiments, the present invention also provides a smart terminal, the principle block diagram of which can be as follows: Figure 9As shown, the intelligent terminal includes a processor, memory, network interface, display screen, and radar module connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a near-field synthetic aperture radar data analysis and imaging processing method. The display screen can be a liquid crystal display (LCD) or an e-ink display, and the radar module is pre-installed within the intelligent terminal.

[0164] Those skilled in the art will understand that Figure 9 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the smart terminal to which the present invention is applied. A specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0165] In one embodiment, a smart terminal is provided, including a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations:

[0166] Acquire radar data for imaging;

[0167] The acquired radar data is processed and analyzed, and the imaging frame velocity data corresponding to the radar data is imported; based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; and a portion of the imaging frame velocity data in the imaging frame velocity data is imaged to generate a partial data image.

[0168] Error detection is performed on the image quality of a portion of the data generated from the velocity data of the imaging frame based on the velocity curve;

[0169] If the image quality of some data generated from the current imaging frame velocity data has errors, the system controls the correction and fitting of the current frame velocity data, generates a single-frame synthetic aperture radar image based on the corrected velocity data and saves it, and saves the corrected and fitted velocity parameters in real time.

[0170] Continuous synthetic aperture radar imaging is performed based on the fitted velocity parameters, and a high frame rate video stream is synthesized.

[0171] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0172] In summary, this invention discloses a near-field synthetic aperture radar (SAR) data analysis and imaging processing method, apparatus, smart terminal, and storage medium. The method includes: acquiring radar data for imaging; processing and analyzing the acquired radar data, and importing imaging frame velocity data corresponding to the radar data; generating a velocity curve based on the imported imaging frame velocity data corresponding to the radar data; imaging a portion of the imaging frame velocity data in the imaging frame velocity data to generate a partial data image; performing error detection on the quality of the partial data image generated from the imaging frame velocity data based on the velocity curve; if there is an error in the quality of the partial data image generated from the current imaging frame velocity data, controlling the correction and fitting of the current frame velocity data, generating a single-frame SAR image based on the corrected velocity data and saving it, and saving the corrected and fitted velocity parameters in real time; performing continuous SAR imaging based on the fitted velocity parameters, and synthesizing a high frame rate video stream. Compared with traditional simulation software for analyzing and processing acquired data, this invention employs a near-field synthetic aperture radar data analysis and imaging processing method that can modify radar imaging configuration in real time, simplify the debugging steps for different parameters affecting imaging quality, and achieve systematic, real-time, and visualized SAR imaging.

[0173] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A near-field synthetic aperture radar data analysis and imaging processing method, characterized in that, The method includes: Acquire radar data for imaging; The acquired radar data is processed and analyzed, and the imaging frame velocity data corresponding to the radar data is imported; based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; and a portion of the imaging frame velocity data in the imaging frame velocity data is imaged to generate a partial data image. Error detection is performed on the image quality of a portion of the data generated from the velocity data of the imaging frame based on the velocity curve; The step of error detection for the partial data image quality generated based on the velocity curve of the imaging frame velocity data includes: Obtain partial data images generated from imaging based on partial imaging frame velocity data: The data image is controlled to be compared with the corresponding frame optical image at the position of the corresponding frame image in the velocity curve to determine whether there is an error; If the position of the partial data image in the corresponding frame image of the velocity curve is deviated when compared with the corresponding frame optical image, it is determined that the quality of the partial data image generated from the velocity data of the current imaging frame has an error. If the image quality of some data generated from the current imaging frame velocity data has errors, the system controls the correction and fitting of the current frame velocity data, generates a single-frame synthetic aperture radar image based on the corrected velocity data and saves it, and saves the corrected and fitted velocity parameters in real time. The steps of controlling the correction of the current frame velocity data when there are errors in the quality of some data images generated from the current imaging frame velocity data, generating and saving a single-frame synthetic aperture radar image based on the corrected velocity data, and saving the corrected and fitted velocity parameters in real time include: If the image quality of some data generated from the current imaging frame rate data has errors, then the current frame rate data is corrected and fitted. The error portion is filled by interpolation. The velocity curve is fitted using the Interp interpolation function or the spline interpolation function, and the corrected and fitted velocity parameters are saved in real time. Generate and save a single-frame synthetic aperture radar image based on the corrected velocity data; Continuous synthetic aperture radar imaging is performed based on the fitted velocity parameters, and a high frame rate video stream is synthesized.

2. The near-field synthetic aperture radar data analysis and imaging processing method according to claim 1, characterized in that, The steps for acquiring radar data for imaging include: Acquire radar data for imaging; Read the initial parameters of radar imaging, including sampling rate, frequency modulation frequency, repetition rate, number of sampling points, carrier frequency, oblique angle, range range, beamwidth, number of accumulation points, number of step points, enhancement factor, color space, and radar placement position.

3. The near-field synthetic aperture radar data analysis and imaging processing method according to claim 1, characterized in that, The acquired radar data is processed and analyzed, and imaging frame velocity data corresponding to the radar data is imported; based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated. The steps for generating partial data images from partial imaging frame velocity data include: The acquired radar data is processed and analyzed, and the imaging frame rate data from the initial parameters of the radar data is loaded. Based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is generated; Extract a portion of the imaging frame velocity data from the imaging frame velocity data, and then image the portion of the imaging frame velocity data to generate a partial data image.

4. The near-field synthetic aperture radar data analysis and imaging processing method according to claim 3, characterized in that, The step of generating a velocity curve based on the imported imaging frame velocity data corresponding to the radar data includes: Based on the imported imaging frame velocity data corresponding to the radar data, a velocity curve is fitted as sample data points, and a velocity curve graph is generated.

5. The near-field synthetic aperture radar data analysis and imaging processing method according to claim 1, characterized in that, The steps of performing continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesizing a high frame rate video stream include: Based on the fitted velocity parameters, the BP algorithm or 2D-FFT algorithm is used to realize continuous synthetic aperture radar imaging, and then the images are synthesized into a high frame rate video stream and output.

6. A near-field synthetic aperture radar data analysis and imaging processing device, characterized in that, The near-field synthetic aperture radar data analysis and imaging processing device is used to implement the near-field synthetic aperture radar data analysis and imaging processing method as described in any one of claims 1-5, the device comprising: The acquisition module is used to acquire radar data for imaging. The import and generation module is used to process and analyze the acquired radar data, import the imaging frame velocity data corresponding to the radar data, generate a velocity curve based on the imported imaging frame velocity data corresponding to the radar data, and generate partial data images by imaging a portion of the imaging frame velocity data. The error detection module is used to detect errors in the image quality of a portion of the imaging frame velocity data based on the velocity curve. The correction and fitting module is used to control the correction and fitting of the current frame velocity data when there are errors in the quality of some data images generated from the current imaging frame velocity data. Based on the corrected velocity data, a single-frame synthetic aperture radar image is generated and saved, and the corrected and fitted velocity parameters are saved in real time. The imaging control module is used to perform continuous synthetic aperture radar imaging based on the fitted velocity parameters and synthesize high frame rate video streams.

7. A smart terminal, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, wherein the one or more programs include methods for performing any one of claims 1-5.

8. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-5.

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