Polar coordinate imaging algorithm for synthetic aperture radar forward oblique imaging

Abstract

The present invention relates to the field of radar imaging technology, and discloses a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, including: obtaining electromagnetic signals when radar waves are reflected when they arrive at a detection target and flight path data of the radar waves, performing data preprocessing, and establishing echo data. Convert the rectangular coordinate system of the echo data into a polar coordinate system, and perform grid processing. Remove the Doppler effect in the echo data after grid processing based on the range Doppler algorithm, and determine the azimuth of the echo data based on the polar coordinate fast Fourier transform. Divide the echo data after the Doppler effect is removed into several sub-areas based on a pre-configured viewing angle for block processing, and reconstruct into an image to be generated. Perform image filtering enhancement and eliminate artifacts on the image to be generated, and convert the image to be generated into a standard image format for output. The present invention can effectively improve the accuracy and reliability of radar imaging through signal processing and image reconstruction technology.

Description

Technical Field

[0001] The present invention relates to the field of radar imaging technology, and in particular to a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm. Background Art

[0002] Synthetic aperture radar is a remote sensing technology widely used in ground monitoring, target detection, terrain mapping and other fields. SAR technology uses the signal reflected by radar waves, combined with the relative motion information of the moving platform, to obtain high-resolution images through synthetic virtual apertures.

[0003] However, although SAR technology has been widely used in many fields, the current SAR image generation process still faces a series of technical difficulties, especially in signal processing, image reconstruction and image quality improvement. First, the signal reflected by the radar wave is usually affected by many factors, including atmosphere, terrain and weather, which may cause signal distortion and affect the accuracy of the final image. Especially when the mobile platform is imaging, the movement of the platform and the relative movement of the target will cause the Doppler effect, which will change the frequency of the echo signal and cause image distortion. Therefore, how to effectively remove the Doppler effect and restore the authenticity of the original signal is an important challenge to improve the quality of SAR images. Secondly, the spatial distribution characteristics of SAR images and the non-uniformity of data make data processing more complicated. Due to different imaging areas, the propagation path of the radar wave reflection signal may be different, which requires special attention to the spatial distribution of the data when generating images to ensure the correct reconstruction of the signal.

[0004] Therefore, it is urgent to invent a synthetic aperture radar imaging technology to solve the problem of low imaging accuracy and quality caused by the Doppler effect, signal distortion and non-uniformity of data spatial distribution in the existing synthetic aperture radar imaging technology. Summary of the invention

[0005] In view of this, the present invention proposes a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, aiming to solve the problem of low imaging accuracy and quality caused by the Doppler effect, signal distortion and non-uniformity of data spatial distribution in the prior art synthetic aperture radar imaging.

[0006] The present invention proposes a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, comprising:

[0007] Acquire electromagnetic signals when radar waves are reflected when reaching the detection target and the flight path data of radar waves;

[0008] Performing data preprocessing on the electromagnetic signal and the flight path data, and establishing echo data;

[0009] Acquire a rectangular coordinate system of the echo data, convert the rectangular coordinate system into a polar coordinate system, and perform grid processing on the echo data of the polar coordinate system;

[0010] Removing the Doppler effect in the echo data after gridding based on a range Doppler algorithm, and determining the azimuth of the echo data based on a polar coordinate fast Fourier transform;

[0011] Based on a pre-configured viewing angle, the echo data after the Doppler effect is removed is divided into a plurality of sub-regions for block processing, and an image to be generated is reconstructed according to the plurality of sub-regions after the block processing;

[0012] Perform image filtering and enhancement on the image to be generated, eliminate artifacts in the image to be generated after image filtering and enhancement, and convert the image to be generated into a standard image format for output.

[0013] Further, the electromagnetic signal and the flight path data are preprocessed and the echo data is established, including:

[0014] Acquire noise spectrum characteristics in the electromagnetic signal, adjust filtering parameters according to the noise spectrum characteristics, and remove environmental noise in the electromagnetic signal based on the filtering parameters;

[0015] Acquire the position information, attitude information and speed information of the transmitter in the flight path data, perform transmitter position fitting according to the position information, attitude information and speed information, and determine the propagation time offset of the electromagnetic signal according to the position distance between the transmitter and the detection target after fitting;

[0016] The beam parameters of the radar wave are obtained, and the distance and angle information in the electromagnetic signal are corrected according to the relative position between the beam parameters and the detection target.

[0017] Furthermore, obtaining the noise spectrum characteristics in the electromagnetic signal, adjusting the filtering parameters according to the noise spectrum characteristics, and removing the environmental noise in the electromagnetic signal based on the filtering parameters includes:

[0018] Converting the electromagnetic signal into a plurality of discrete signals, and acquiring any of the discrete signals as a sample signal of the electromagnetic signal;

[0019] Based on the fast Fourier transform, the sample signal is converted from the time domain to the frequency domain, the spectrum characteristics are extracted, and the amplitude and distribution characteristics of the noise at each frequency are obtained according to the spectrum characteristics;

[0020] Acquire a power spectrum density of the sample signal based on the frequency spectrum feature, and acquire a frequency division ratio and intensity of the noise according to the power spectrum density;

[0021] The signal power of the sample signal is obtained according to the power spectrum density, the noise power of the noise is obtained according to the frequency division ratio and intensity of the noise, and the frequency band in the filtering parameter is determined according to the relationship between the signal power and the noise power: wherein,

[0022] When the signal power is greater than the noise power, determining the cutoff frequency in the filter parameters to be a narrowband frequency band, and removing the environmental noise in the electromagnetic signal based on the filter parameters after the frequency band is adjusted to the narrowband frequency band;

[0023] When the signal power is less than the noise power, the cutoff frequency in the filter parameter is determined to be a broadband frequency band, and the environmental noise in the electromagnetic signal is removed based on the filter parameter after the frequency band is adjusted to the broadband frequency band.

[0024] Further, performing transmitter position fitting according to the position information, attitude information and speed information, and determining the propagation time offset of the electromagnetic signal according to the position distance between the transmitter and the detection target after fitting, includes:

[0025] Based on the position information, attitude information, speed information and formula, the fitting position of the transmitting end is obtained, wherein the formula is as follows:

[0026] ;

[0027] Where: p fit is the fitting position of the transmitter, A is the design matrix between the position information, attitude information and speed information, A T is the transposed matrix of the design matrix, and p is the error value in the transmitter position information;

[0028] The propagation time offset of the electromagnetic signal is determined according to the fitting position of the transmitting end, the position of the detection target and the propagation speed of the electromagnetic wave:

[0029] ;

[0030] Where Δt is the propagation time offset, p target is the position of the detection target, and c is the propagation speed of the electromagnetic wave.

[0031] Furthermore, obtaining the beam parameters of the radar wave and correcting the distance and angle information in the electromagnetic signal according to the relative position between the beam parameters and the detection target includes:

[0032] Acquire the beam width and the main lobe direction in the beam parameters, and determine the beam direction of the radar wave according to the beam width and the main lobe direction;

[0033] Determining the actual direction of the radar wave according to the beam direction and the attitude information of the transmitting end;

[0034] Acquire a relative position vector between the transmitting end and the detecting target according to the fitting position of the transmitting end and the position of the detecting target;

[0035] Acquire a beam main lobe deviation angle according to the main lobe direction, calculate the distance deviation caused by the actual pointing of the radar wave according to the beam main lobe deviation angle, and determine the distance difference between the relative position vector and the distance deviation as the corrected distance in the electromagnetic signal;

[0036] According to the actual pointing direction and the relative position vector, the offset angle is determined:

[0037] ;

[0038] Wherein, b is the pointing vector of the actual pointing, Δp is the relative position vector, θ offset is the offset angle;

[0039] According to the angle difference between the angle information in the electromagnetic signal and the offset angle, the angle difference is determined as the corrected angle information in the electromagnetic signal.

[0040] Furthermore, obtaining a rectangular coordinate system of the echo data, converting the rectangular coordinate system into a polar coordinate system, and gridding the echo data of the polar coordinate system includes:

[0041] Acquire the position data of the echo data in a spatial rectangular coordinate system, and convert the position data into a radial distance, azimuth angle, and elevation angle between the detection target and the transmitting end in a polar coordinate system:

[0042] ;

[0043] Wherein, x, y, z are the coordinates of the position data respectively, r is the radial distance between the detection target and the transmitting end, θ is the azimuth angle between the detection target and the transmitting end, and ϕ is the pitch angle between the detection target and the transmitting end;

[0044] Divide the radial distance, azimuth and elevation angle between the detection target and the transmitting end in the polar coordinate system into discrete grid points according to a preset step size;

[0045] Acquire the reflection intensity in the echo data, and distribute the reflection intensity to each of the discrete grid points according to the relationship between the reflection intensity and the radial distance, azimuth and elevation angle between the detection target and the transmitting end, wherein:

[0046] If the discrete grid point does not contain the reflection intensity, a weighted average intensity between the reflection intensities in two adjacent discrete grid points is obtained, and the reflection intensity of the discrete grid point is determined according to the weighted average intensity.

[0047] Further, when removing the Doppler effect in the echo data after the gridding process based on the range Doppler algorithm and determining the azimuth of the echo data based on the polar coordinate fast Fourier transform, it includes:

[0048] Obtaining the relative radial velocity between the detection target and the transmitting end in the reflection intensity of each discrete grid point;

[0049] Based on the range Doppler algorithm, the frequency distribution of the echo data is analyzed in the time-frequency domain, and the Doppler shift component is extracted:

[0050] ;

[0051] Among them, f d is the Doppler frequency shift component, v r is the relative radial velocity between the detection target and the transmitting end, and λ is the wavelength of the radar wave;

[0052] removing the Doppler effect from the echo data based on phase compensation according to the relative radial velocity and the Doppler frequency shift component;

[0053] The echo data with the Doppler effect removed is used to extract the frequency domain signal based on polar coordinate fast Fourier transform:

[0054] ;

[0055] Among them, F (r,θ) is the frequency domain signal, f (r,θ) is the echo data in the polar coordinate system, u is the frequency component in the radial direction, v is the frequency component in the angular direction, and j is the imaginary unit in Fourier transform;

[0056] The power spectrum density of the frequency domain signal is obtained, and based on the power spectrum density of the frequency domain signal, the energy point of each discrete grid point azimuth is obtained, and the energy point with the largest energy value among the energy points of each discrete grid point azimuth is used as the azimuth of the echo data.

[0057] Furthermore, the echo data after the Doppler effect is removed is divided into a plurality of sub-regions for block processing based on a pre-configured viewing angle, and the image to be generated is reconstructed according to the plurality of sub-regions after the block processing, including:

[0058] Acquire the scanning angle and imaging pixel requirement of the transmitting end, and establish a preset segmentation distance according to the relationship between the scanning angle and the imaging pixel requirement;

[0059] Dividing the echo data after gridding processing into a plurality of sub-areas according to the segmentation distance;

[0060] Acquire the echo data block in the sub-region, convert the echo data block from time domain data to frequency domain data based on Fourier transform, and extract the spectrum information in the sub-region;

[0061] According to the radar wavelength, the operating frequency of the radar wave, the position information and attitude information of the transmitter, the position information and attitude information of the receiver, the flight path data and the polar coordinate system of the detection target, and the beam width and main lobe direction of the radar wave in the echo data, the frequency domain data is converted into the detection target image based on the inverse Fourier transform:

[0062] ;

[0063] Among them, I (r,θ,ϕ) is the target image, S (f,θ,ϕ) is the frequency domain data, A is the conversion from frequency domain to spatial domain. (r,θ,ϕ) is the compensation coefficient;

[0064] The detection target images in each of the sub-regions are obtained, and a plurality of the detection target images are spliced ​​according to the preset sub-region division order, wherein:

[0065] If there is an overlapping image between two adjacent detection target images, the image features of the overlapping image are obtained, and after deleting the overlapping image of any one of the detection target images, the two adjacent detection target images are spliced.

[0066] Furthermore, performing image filtering enhancement on the image to be generated, eliminating artifacts in the image to be generated after the image filtering enhancement, and converting the image to be generated into a standard image format for output, includes:

[0067] Determining the spliced ​​detection target image as the image to be generated;

[0068] Acquiring edge sharpness in the image to be generated, and determining a filtering method for performing image filtering enhancement on the image to be generated according to a relationship between the edge sharpness and a preset edge sharpness;

[0069] Dividing the image to be generated after image filtering and enhancement into a plurality of detection areas, acquiring a plurality of detection areas with inconsistent brightness changes in the detection areas based on gradient detection, and determining the detection areas as artifact areas;

[0070] Obtain pixel values ​​in each of the detection areas adjacent to the artifact area, and adjust the pixel values ​​in the artifact area to the weighted average pixel value based on the weighted average pixel value between the pixel values ​​in each of the detection areas.

[0071] Further, according to the relationship between the edge sharpness and a preset edge sharpness, determining a filtering method for performing image filtering enhancement on the image to be generated includes:

[0072] When the edge sharpness is lower than the preset edge sharpness, determining to perform image filtering enhancement on the image to be generated based on high-pass filtering;

[0073] When the edge sharpness is higher than or equal to the preset edge sharpness, it is determined to perform image filtering enhancement on the image to be generated based on low-pass filtering.

[0074] Compared with the prior art, the beneficial effect of the present invention is that by adopting a comprehensive data processing flow, multiple technical problems faced in the process of synthetic aperture radar image generation, especially the Doppler effect, data distribution non-uniformity and artifact problems, can be effectively solved. By removing the Doppler effect, the signal distortion caused by platform motion and target relative motion can be significantly reduced, the real echo signal can be restored, and the high-quality reconstruction of the image can be ensured. The range Doppler algorithm used in this process can not only effectively remove the frequency change caused by motion, but also improve the spatial resolution of the image in a complex environment. In addition, the echo data is converted into a polar coordinate system and gridded, which helps to improve the flexibility and accuracy of data processing. The polar coordinate system can better adapt to the propagation characteristics of radar waves and ensure that information such as the azimuth and distance of the target is accurately represented. By dividing the data after removing the Doppler effect into sub-areas and performing block processing, the processing process of each sub-area can be more finely controlled, the interference between data can be reduced, and the quality and resolution of the image can be improved. Secondly, the image artifacts are effectively eliminated by image filtering enhancement, and the clarity and realism of the image are further improved. The artifact removal algorithm focuses on identifying possible artifact areas in the image and repairing them. This process is particularly critical in the processing of SAR images, especially when facing complex terrain or severe weather conditions. In this way, the image to be generated can ultimately achieve a more accurate and detailed effect. Finally, the image is converted into a standard image format and output, which is convenient for subsequent image analysis and application, greatly improving the compatibility and practicality of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0076] Figure 1 A flowchart of a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0078] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, including:

[0079] Step S100, obtaining electromagnetic signals when the radar wave is reflected when it reaches the detection target and flight path data of the radar wave.

[0080] Step S200: pre-process the electromagnetic signal and the flight path data, and create echo data.

[0081] Specifically, the electromagnetic signal and flight path data are preprocessed and the echo data is established, including: obtaining the noise spectrum characteristics in the electromagnetic signal, adjusting the filter parameters according to the noise spectrum characteristics, and removing the environmental noise in the electromagnetic signal based on the filter parameters. The position information, attitude information and speed information of the transmitter in the flight path data are obtained, the transmitter position is fitted according to the position information, attitude information and speed information, and the propagation time offset of the electromagnetic signal is determined according to the position distance between the transmitter and the detection target after fitting. The beam parameters of the radar wave are obtained, and the distance and angle information in the electromagnetic signal are corrected according to the relative position between the beam parameters and the detection target.

[0082] It can be understood that the noise spectrum characteristics are obtained, and the nature of the noise and its impact range are determined by analyzing the noise spectrum characteristics in the signal. Subsequently, the filter parameters are adjusted according to these characteristics to effectively remove the environmental noise. The key to this step is to maximize the retention of the effective information of the signal by optimizing the filter parameters, while removing the noise and improving the accuracy of subsequent data processing. Secondly, in the process of flight path data processing, obtaining the position information, attitude information and speed information of the transmitter is the basis for ensuring accurate signal transmission. This information can be obtained in real time through sensors and other means, and then the position fitting algorithm is applied to accurately fit the position of the transmitter. Position fitting refers to the calculation of the precise position of the transmitter based on the collected position information, attitude information and speed data, so as to obtain the actual distance between the transmitter and the detection target. This process is crucial because the propagation time of the electromagnetic wave is closely related to the distance from the transmitter to the target, and the accurate position of the transmitter can ensure the accuracy of the propagation time of the electromagnetic signal. Finally, the beam parameters of the radar wave also need to be corrected during the signal processing process. The beam parameters include beam width, main lobe direction, etc. These parameters determine the propagation range and scanning capability of the radar wave. In practical applications, the relative position between the detection target and the radar will affect the propagation path and propagation time of the signal. By correcting the distance and angle information of the electromagnetic signal based on the relative relationship between the beam parameters and the target position, the error caused by the propagation characteristics of the radar wave can be effectively eliminated.

[0083] Specifically, the noise spectrum characteristics in the electromagnetic signal are obtained, the filtering parameters are adjusted according to the noise spectrum characteristics, and the environmental noise in the electromagnetic signal is removed based on the filtering parameters, including: converting the electromagnetic signal into a plurality of discrete signals, and obtaining any discrete signal as a sample signal of the electromagnetic signal. The sample signal is converted from the time domain to the frequency domain based on the fast Fourier transform, the spectrum characteristics are extracted, and the amplitude and distribution characteristics of the noise at each frequency are obtained according to the spectrum characteristics. The power spectrum density of the sample signal is obtained based on the spectrum characteristics, and the frequency division and intensity of the noise are obtained according to the power spectrum density. The signal power of the sample signal is obtained according to the power spectrum density, the noise power of the noise is obtained according to the frequency division and intensity of the noise, and the frequency band in the filtering parameters is determined according to the relationship between the signal power and the noise power: wherein, when the signal power is greater than the noise power, the cutoff frequency in the filtering parameters is determined to be a narrow frequency band, and the environmental noise in the electromagnetic signal is removed based on the filtering parameters after the frequency band is adjusted to the narrow frequency band. When the signal power is less than the noise power, the cutoff frequency in the filtering parameters is determined to be a broadband frequency band, and the environmental noise in the electromagnetic signal is removed based on the filtering parameters after the frequency band is adjusted to the broadband frequency band.

[0084] It can be understood that the signal is converted from the time domain to the frequency domain by fast Fourier transform (FFT) for spectrum analysis. Spectral analysis can reveal the intensity and distribution characteristics of different frequency components in the signal, which is crucial for identifying noise and valid signals. By extracting spectral features, the distribution of noise at each frequency can be clarified, thus providing a basis for noise removal. The application of fast Fourier transform makes it possible to separate noise frequencies from complex electromagnetic signals, thereby improving the efficiency of signal processing. Secondly, based on the power spectral density (PSD) extracted from spectral features, the power distribution of signals and noise can be further analyzed. The power spectral density describes the power distribution of the signal in the frequency domain and provides power information of the signal and noise at each frequency. By calculating the power spectral density of the sample signal, the intensity and frequency distribution of the noise at different frequencies can be obtained, and then the power of the noise can be obtained. Through this analysis, the frequency characteristics of the noise can be accurately judged, thereby providing accurate parameters for the filtering process and optimizing the noise suppression effect of the signal. Finally, according to the relationship between the signal power and the noise power, the cutoff frequency of the filtering parameters is adjusted to achieve noise removal. When the signal power is greater than the noise power, it means that the signal is strong and the noise is weak. At this time, a narrower frequency band needs to be used for filtering to maintain the integrity of the signal and effectively remove the noise. When the signal power is less than the noise power, it means that the signal is weak and the noise is strong. At this time, a wider frequency band needs to be used for filtering to retain the effective components of the signal to the greatest extent and fully suppress the noise. This method of dynamically adjusting the filtering parameters according to the power spectrum characteristics can flexibly deal with noise problems under different signal conditions and ensure the quality of the signal and the processing effect.

[0085] Specifically, the transmitter position is fitted according to the position information, attitude information and speed information, and the propagation time offset of the electromagnetic signal is determined according to the position distance between the transmitter and the detection target after fitting, including: obtaining the fitting position of the transmitter based on the position information, attitude information, speed information and formula, wherein the formula is as follows:

[0086] .

[0087] Where: p fit is the fitting position of the transmitter, A is the design matrix between the position information, attitude information and speed information, A T is the transposed matrix of the design matrix, and p is the error value in the transmitter position information. According to the fitting position of the transmitter, the position of the detection target and the electromagnetic wave propagation speed, the propagation time offset of the electromagnetic signal is determined:

[0088] .

[0089] Where Δt is the propagation time offset, p targetis the position of the detected target, and c is the propagation speed of electromagnetic waves.

[0090] It can be understood that the transmitter position is fitted by using the position information, attitude information and speed information. The design matrix A is used in the formula to associate this information with the error value (p) of the transmitter, and the fitted position p of the transmitter is calculated by minimizing the error. fit . The design matrix A structures the relationship between position information, attitude information and speed information, which can systematically integrate various data and optimize the positioning accuracy of the transmitter. By fitting the position, the system error of the sensor or the error caused by environmental changes can be eliminated, thereby improving the accuracy of transmitter positioning. Secondly, after determining the fitting position of the transmitter, the position and the position of the detection target are combined with the propagation speed of the electromagnetic wave to calculate the propagation time offset of the electromagnetic signal. The propagation time offset Δt is caused by the relative position difference between the transmitter and the detection target, which affects the propagation time of the electromagnetic wave. The formula accurately describes the calculation method of the propagation time offset, where p target is the position of the detection target, and c is the propagation speed of the electromagnetic wave. This process can effectively correct the time delay of the signal and ensure the synchronization of the signal data. Finally, by accurately calculating the propagation time offset, the propagation time error caused by factors such as the relative motion and position difference between the transmitter and the detection target can be eliminated. In this way, during the imaging process, data at different positions can be processed synchronously to avoid image distortion or errors caused by time delay and improve image quality.

[0091] Specifically, obtaining the beam parameters of the radar wave and correcting the distance and angle information in the electromagnetic signal according to the relative position between the beam parameters and the detection target includes: obtaining the beam width and main lobe direction in the beam parameters, and determining the beam direction of the radar wave according to the beam width and the main lobe direction. Determine the actual direction of the radar wave according to the beam direction and the attitude information of the transmitter. Obtain the relative position vector between the transmitter and the detection target according to the fitting position of the transmitter and the position of the detection target. Obtain the main lobe deviation angle of the beam according to the main lobe direction, calculate the distance deviation of the radar wave caused by the actual pointing according to the main lobe deviation angle of the beam, and determine the distance difference between the relative position vector and the distance deviation as the corrected distance in the electromagnetic signal. Determine the offset angle according to the actual pointing and the relative position vector:

[0092] .

[0093] Where b is the actual pointing vector, Δp is the relative position vector, and θ offset According to the angle difference between the angle information in the electromagnetic signal and the offset angle, the angle difference is determined as the corrected angle information in the electromagnetic signal.

[0094] It can be understood that the beam direction of the radar wave is determined by the beam width and the main lobe direction. The beam width describes the propagation range of the radar wave, while the main lobe direction is the main energy direction of the beam. These two parameters combined can clarify the propagation direction of the radar wave. Through this information, the actual pointing direction of the radar wave can be inferred, and the necessary basic data for subsequent distance and angle corrections can be provided. Through these steps, the consistency between the propagation direction of the radar wave and the actual pointing is ensured, and accurate direction data is provided for subsequent signal processing. Next, based on the position of the transmitter and the relative position vector of the detection target, the distance deviation caused by the actual pointing of the radar wave is calculated. This process calculates the distance difference caused by the actual pointing by combining the main lobe deviation angle with the relative position vector. The relative position vector represents the relative spatial position between the transmitter and the detection target, and the distance deviation is caused by the actual deviation of the radar wave propagation direction. By calculating this distance difference, the distance information in the electromagnetic signal can be accurately corrected, the distance measurement deviation caused by the pointing error can be eliminated, and the accuracy of the signal can be ensured. Finally, combined with the calculation of the offset angle, the angle information in the electromagnetic signal is further corrected. By analyzing the relationship between the actual pointing and the relative position vector, the angle offset during the propagation of the radar wave is determined. This offset angle θoffset reflects the relative angle difference between the actual direction of the radar wave and the detected target. By comparing this angle difference with the angle information in the electromagnetic signal, the angle information can be corrected to improve the angle accuracy of the imaging result.

[0095] Step S300: Acquire the rectangular coordinate system of the echo data, convert the rectangular coordinate system into a polar coordinate system, and perform grid processing on the echo data in the polar coordinate system.

[0096] Specifically, obtaining the rectangular coordinate system of the echo data, converting it into a polar coordinate system according to the rectangular coordinate system, and gridding the echo data in the polar coordinate system include: obtaining the position data of the echo data in the spatial rectangular coordinate system, and converting it into the radial distance, azimuth and pitch angle between the detection target and the transmitting end in the polar coordinate system according to the position data:

[0097] .

[0098] Among them, x, y, z are the coordinates of the position data, r is the radial distance between the detection target and the transmitter, θ is the azimuth between the detection target and the transmitter, and ϕ is the pitch angle between the detection target and the transmitter. The radial distance, azimuth and pitch angle between the detection target and the transmitter in the polar coordinate system are divided into discrete grid points according to a preset step size, and the reflection intensity in the echo data is obtained. According to the relationship between the reflection intensity and the radial distance, azimuth and pitch angle between the detection target and the transmitter, the reflection intensity is allocated to each discrete grid point, wherein: if the discrete grid point does not contain the reflection intensity, the weighted average intensity between the reflection intensities in two adjacent discrete grid points is obtained, and the reflection intensity of the discrete grid point is determined according to the weighted average intensity.

[0099] It can be understood that the echo data is converted from the spatial rectangular coordinate system to the polar coordinate system. The core of this process is to determine the radial distance, azimuth and elevation angle between the detection target and the transmitter through the conversion of position data. Specifically, the position data (x, y, z) in the spatial rectangular coordinate system is converted into three key parameters in the polar coordinate system through mathematical formulas: radial distance r, azimuth θ and elevation ϕ. The radial distance represents the distance between the detection target and the transmitter, while the azimuth and elevation angles describe the horizontal and vertical positional relationship of the target relative to the transmitter, respectively. This conversion process is a basic step in radar signal processing and provides data support for subsequent signal gridding and reflection intensity distribution. Next, the data converted to the polar coordinate system is further gridded. The purpose of gridding is to discretize the continuous data in the polar coordinate system for subsequent calculation and processing. By setting a predetermined step size, the radial distance, azimuth and elevation angle are divided into a number of discrete grid points, so that the echo data is distributed according to these grid points. The purpose of this step is to convert the continuous signal in space into operational discrete data, ensuring that the reflection intensity of each location point can be accurately analyzed during the signal processing process. Finally, the reflection intensity of the echo data is distributed according to the relationship between it and the grid point. If a discrete grid point does not contain reflection intensity data, the reflection intensity of the point is filled by calculating the weighted average of the reflection intensities of the two adjacent grid points. This weighted average method effectively solves the problem of missing data caused by the fact that some grid points do not directly measure echo data, and interpolates and fills in the reflection intensities of adjacent points. This technical method ensures the continuity and consistency of the echo data in the entire polar coordinate grid, thereby improving the accuracy and quality of the final image.

[0100] Step S400: removing the Doppler effect in the echo data after the gridding process based on the range Doppler algorithm, and determining the azimuth of the echo data based on the polar coordinate fast Fourier transform.

[0101] Specifically, the Doppler effect in the echo data after gridding is removed based on the range Doppler algorithm, and the azimuth of the echo data is determined based on the polar coordinate fast Fourier transform, including: obtaining the relative radial velocity between the detection target and the transmitter in the reflection intensity of each discrete grid point. Based on the range Doppler algorithm, the frequency distribution of the echo data is analyzed in the time-frequency domain, and the Doppler frequency shift component is extracted:

[0102] .

[0103] Among them, f d is the Doppler frequency shift component, v r To detect the relative radial velocity between the target and the transmitter, λ is the wavelength of the radar wave. According to the relative radial velocity and the Doppler frequency shift component, the Doppler effect is removed from the echo data based on phase compensation. The frequency domain signal is extracted from the echo data with the Doppler effect removed based on the polar coordinate fast Fourier transform:

[0104] .

[0105] Among them, F (r,θ) is the frequency domain signal, f (r,θ) is the echo data in the polar coordinate system, u is the frequency component in the radial direction, v is the frequency component in the angular direction, and j is the imaginary unit in Fourier transform. The power spectrum density of the frequency domain signal is obtained, and based on the power spectrum density of the frequency domain signal, the energy points of the azimuth angle of each discrete grid point are obtained, and the energy point with the largest energy value among the energy points of the azimuth angle of each discrete grid point is taken as the azimuth direction of the echo data.

[0106] It can be understood that the Doppler effect in the echo data is removed based on the range Doppler algorithm. The Doppler effect originates from the relative motion between the detection target and the transmitter, which causes the frequency of the echo signal to change. In order to remove this frequency change, the range Doppler algorithm first analyzes the echo data in the time-frequency domain and extracts the Doppler frequency shift component. The Doppler frequency shift is related to the relative radial velocity (the relative velocity between the detection target and the transmitter), which can be calculated by the known wavelength of the radar wave. After extracting the Doppler frequency shift component, the Doppler effect is removed from the echo data by the phase compensation method, thereby restoring the original characteristics of the echo signal. This process helps to eliminate the impact of relative motion on the signal and improve the accuracy of the data. Next, the echo data after the Doppler effect is removed will be subjected to polar coordinate fast Fourier transform to extract the frequency domain signal. Through Fourier transform, the echo data is converted to the frequency domain and divided into frequency components in the radial direction and the angular direction. In the polar coordinate system, the radial frequency component reflects the change in distance from the target, while the angular frequency component is related to the change in the azimuth of the target. By performing Fourier transform on the echo data, the frequency components in different directions can be clearly separated, laying the foundation for the subsequent azimuth extraction. Finally, by analyzing the power spectrum density of the frequency domain signal, the energy point of the azimuth in the echo data is determined. In the frequency domain, the power spectrum density reflects the energy distribution of different frequency components, and the points with large energy values ​​correspond to the significant features in the echo signal. At each discrete grid point, the maximum energy point is considered to be the azimuth of the point. In this way, the azimuth in the echo data can be effectively extracted, further improving the accuracy and resolution of SAR imaging.

[0107] Step S500: divide the echo data after the Doppler effect is removed into a plurality of sub-regions based on a pre-configured viewing angle for block processing, and reconstruct the image to be generated according to the plurality of sub-regions after the block processing.

[0108] Specifically, based on the pre-configured viewing angle, the echo data after the Doppler effect is removed is divided into several sub-areas for block processing, and the image to be generated is reconstructed according to the several sub-areas after the block processing, including: obtaining the scanning angle and imaging pixel requirements of the transmitting end, and establishing a preset segmentation distance according to the relationship between the scanning angle and the imaging pixel requirements. The echo data after grid processing is divided into several sub-areas according to the segmentation distance. The echo data blocks in the sub-areas are obtained, and the echo data blocks are converted from time domain data to frequency domain data based on Fourier transform, and the spectrum information in the sub-areas is extracted. According to the radar wavelength in the echo data, the operating frequency of the radar wave, the position information and attitude information of the transmitting end, the position information and attitude information of the receiving end, the flight path data and the polar coordinate system of the detected target, and the beam width and main lobe direction of the radar wave, the frequency domain data is converted into the detected target image based on the inverse Fourier transform:

[0109] .

[0110] Among them, I (r,θ,ϕ) is the target image, S (f,θ,ϕ) is the frequency domain data, A is the conversion from frequency domain to spatial domain. (r,θ,ϕ) is the compensation coefficient. The detection target images in each sub-region are obtained, and the sub-region division order is preset to splice several detection target images, wherein: if there is an overlapping image between two adjacent detection target images, the image features of the overlapping image are obtained, and after deleting the overlapping image of any of the detection target images, the two adjacent detection target images are spliced.

[0111] It can be understood that the echo data after the Doppler effect is removed is divided into several sub-areas for block processing according to the preset viewing angle. In order to ensure the quality and resolution of the image, a segmentation distance is first determined according to the relationship between the scanning angle of the transmitter and the required imaging pixels, and the distance determines the segmentation method of the echo data. According to the segmentation distance, the gridded echo data is divided into several sub-areas, each of which represents a specific imaging area. In this way, a large amount of echo data can be efficiently processed, and the echo data in each sub-area can be guaranteed to fully reflect the details of the detection target. Then, for each sub-area, the echo data block is converted from the time domain to the frequency domain data through Fourier transform. Frequency domain processing can more effectively extract the spectrum information in the echo data, which is crucial for subsequent image reconstruction. By extracting the spectrum information, combined with the radar's operating frequency, wavelength, position information of the transmitter and the receiver, attitude information and polar coordinate data of the detection target, an inverse Fourier transform can be accurately performed. The inverse Fourier transform converts the frequency domain data back to the spatial domain, thereby reconstructing the detection target image of each sub-area. This process relies on the compensation coefficient A(r,θ,ϕ), which is used to adjust the conversion from the frequency domain to the spatial domain to ensure the accuracy of image reconstruction. Finally, the detection target images of all sub-areas are spliced ​​according to the preset order to generate a complete imaging result. If there are overlapping images in adjacent sub-areas, the overlapping parts will be identified and deleted, and the image features of the overlapping areas will be analyzed to ensure that there is no redundant information after the images are spliced. Through this splicing technology, a seamless detection target image is finally obtained, ensuring the continuity and integrity of the entire image.

[0112] Step S600: performing image filtering and enhancement on the image to be generated, eliminating artifacts in the image to be generated after the image filtering and enhancement, and converting the image to be generated into a standard image format for output.

[0113] Specifically, when performing image filtering and enhancement on the image to be generated, eliminating artifacts in the image to be generated after image filtering and enhancement, and converting the image to be generated into a standard image format for output, it includes: determining the spliced ​​detection target image as the image to be generated. Obtaining the edge sharpness in the image to be generated, and determining the filtering method when performing image filtering and enhancement on the image to be generated based on the relationship between the edge sharpness and the preset edge sharpness. Dividing the image to be generated after image filtering and enhancement into several detection areas, obtaining detection areas with inconsistent brightness changes in several detection areas based on gradient detection, and determining the detection areas as artifact areas. Obtaining pixel values ​​in each detection area adjacent to the artifact area, and adjusting the pixel values ​​in the artifact area to the weighted average pixel value according to the weighted average pixel value between the pixel values ​​in each detection area.

[0114] Specifically, according to the relationship between the edge sharpness and the preset edge sharpness, the filtering method for image filtering enhancement of the image to be generated is determined, including: when the edge sharpness is lower than the preset edge sharpness, it is determined to perform image filtering enhancement on the image to be generated based on high-pass filtering. When the edge sharpness is higher than or equal to the preset edge sharpness, it is determined to perform image filtering enhancement on the image to be generated based on low-pass filtering.

[0115] It can be understood that by calculating the edge sharpness of the image to be generated and comparing it with the preset edge sharpness, it can be determined whether to use high-pass filtering or low-pass filtering. If the edge sharpness of the image to be generated is lower than the preset value, it indicates that the details of the image are relatively blurred, and high-pass filtering will be selected to enhance the details of the image; conversely, when the edge sharpness is higher than or equal to the preset value, low-pass filtering is selected to smooth the image and eliminate overly sharp edges, thereby avoiding noise interference and over-enhancement. Secondly, after the image is filtered and enhanced, the image is further processed, mainly to detect and eliminate artifacts. By dividing the filtered image into several detection areas, and based on the gradient detection method, the area with inconsistent brightness changes is identified as the artifact area. Artifacts are usually caused by abnormal brightness changes in the image processing process, and these areas will interfere with the quality of the image. In order to solve this problem, the pixel values ​​of the artifact area are adjusted by weighted averaging the pixel values ​​of the artifact area and its adjacent areas, so as to achieve artifact removal. Finally, after filtering enhancement and artifact removal, the image to be generated is converted into a standard image format and output. This process ensures that the image meets the standardized output requirements after optimization and can be used for subsequent image analysis or display. Through this image optimization process, high-quality output of images is ensured, while artifacts that may affect image quality are removed, thereby improving the realism and accuracy of the image.

[0116] In the above embodiment, by adopting a comprehensive data processing flow, multiple technical problems faced in the process of synthetic aperture radar image generation, especially the Doppler effect, data distribution non-uniformity and artifact problems, can be effectively solved. By removing the Doppler effect, the signal distortion caused by platform motion and target relative motion can be significantly reduced, the real echo signal can be restored, and the high-quality reconstruction of the image can be ensured. The range Doppler algorithm used in this process can not only effectively remove the frequency change caused by motion, but also improve the spatial resolution of the image in a complex environment. In addition, converting the echo data into a polar coordinate system and gridding it can help improve the flexibility and accuracy of data processing. The polar coordinate system can better adapt to the propagation characteristics of radar waves and ensure that information such as the direction and distance of the target is accurately represented. By dividing the data after removing the Doppler effect into sub-areas and performing block processing, the processing process of each sub-area can be more finely controlled, the interference between data can be reduced, and the quality and resolution of the image can be improved. Secondly, the artifacts in the image are effectively eliminated by image filtering enhancement, and the clarity and realism of the image are further improved. The artifact removal algorithm focuses on identifying possible artifact areas in the image and repairing them. This process is particularly critical in the processing of SAR images, especially when facing complex terrain or severe weather conditions. In this way, the image to be generated can ultimately achieve a more accurate and detailed effect. Finally, the image is converted into a standard image format and output, which is convenient for subsequent image analysis and application, greatly improving the compatibility and practicality of the image.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, characterized in that: include: Acquire electromagnetic signals when radar waves are reflected when reaching the detection target and the flight path data of radar waves; Performing data preprocessing on the electromagnetic signal and the flight path data, and establishing echo data; Acquire a rectangular coordinate system of the echo data, convert the rectangular coordinate system into a polar coordinate system, and perform grid processing on the echo data of the polar coordinate system; Removing the Doppler effect in the echo data after gridding based on a range Doppler algorithm, and determining the azimuth of the echo data based on a polar coordinate fast Fourier transform; Based on a pre-configured viewing angle, the echo data after the Doppler effect is removed is divided into a plurality of sub-regions for block processing, and an image to be generated is reconstructed according to the plurality of sub-regions after the block processing; Perform image filtering and enhancement on the image to be generated, eliminate artifacts in the image to be generated after image filtering and enhancement, and convert the image to be generated into a standard image format for output.

2. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm according to claim 1, characterized in that: The electromagnetic signal and the flight path data are preprocessed and the echo data is established, including: Acquire noise spectrum characteristics in the electromagnetic signal, adjust filtering parameters according to the noise spectrum characteristics, and remove environmental noise in the electromagnetic signal based on the filtering parameters; Acquire the position information, attitude information and speed information of the transmitter in the flight path data, perform transmitter position fitting according to the position information, attitude information and speed information, and determine the propagation time offset of the electromagnetic signal according to the position distance between the transmitter and the detection target after fitting; The beam parameters of the radar wave are obtained, and the distance and angle information in the electromagnetic signal are corrected according to the relative position between the beam parameters and the detection target.

3. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm as claimed in claim 2, characterized in that: Acquiring noise spectrum characteristics in an electromagnetic signal, adjusting filtering parameters according to the noise spectrum characteristics, and removing environmental noise in the electromagnetic signal based on the filtering parameters, includes: Converting the electromagnetic signal into a plurality of discrete signals, and acquiring any of the discrete signals as a sample signal of the electromagnetic signal; Based on the fast Fourier transform, the sample signal is converted from the time domain to the frequency domain, the spectrum characteristics are extracted, and the amplitude and distribution characteristics of the noise at each frequency are obtained according to the spectrum characteristics; Acquire a power spectrum density of the sample signal based on the frequency spectrum feature, and acquire a frequency division ratio and intensity of the noise according to the power spectrum density; The signal power of the sample signal is obtained according to the power spectrum density, the noise power of the noise is obtained according to the frequency division ratio and intensity of the noise, and the frequency band in the filtering parameter is determined according to the relationship between the signal power and the noise power: wherein, When the signal power is greater than the noise power, determining the cutoff frequency in the filter parameters to be a narrowband frequency band, and removing the environmental noise in the electromagnetic signal based on the filter parameters after the frequency band is adjusted to the narrowband frequency band; When the signal power is less than the noise power, the cutoff frequency in the filter parameter is determined to be a broadband frequency band, and the environmental noise in the electromagnetic signal is removed based on the filter parameter after the frequency band is adjusted to the broadband frequency band.

4. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm as claimed in claim 3, characterized in that: When performing transmitter position fitting according to the position information, attitude information and speed information, and determining the propagation time offset of the electromagnetic signal according to the position distance between the transmitter and the detection target after fitting, the method includes: Based on the position information, attitude information, speed information and formula, the fitting position of the transmitting end is obtained, wherein the formula is as follows: ； Where: p fit is the fitting position of the transmitter, A is the design matrix between the position information, attitude information and speed information, and A T is the transposed matrix of the design matrix, and p is the error value in the transmitter position information; The propagation time offset of the electromagnetic signal is determined according to the fitting position of the transmitting end, the position of the detection target and the propagation speed of the electromagnetic wave: ； Where Δt is the propagation time offset, p target is the position of the detection target, and c is the propagation speed of the electromagnetic wave.

5. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm as claimed in claim 4, characterized in that: Acquiring the beam parameters of the radar wave and correcting the distance and angle information in the electromagnetic signal according to the relative position between the beam parameters and the detection target includes: Acquire the beam width and the main lobe direction in the beam parameters, and determine the beam direction of the radar wave according to the beam width and the main lobe direction; Determining the actual direction of the radar wave according to the beam direction and the attitude information of the transmitting end; Acquire a relative position vector between the transmitting end and the detecting target according to the fitting position of the transmitting end and the position of the detecting target; Acquire a beam main lobe deviation angle according to the main lobe direction, calculate the distance deviation caused by the actual pointing of the radar wave according to the beam main lobe deviation angle, and determine the distance difference between the relative position vector and the distance deviation as the corrected distance in the electromagnetic signal; According to the actual pointing direction and the relative position vector, the offset angle is determined: ； Wherein, b is the pointing vector of the actual pointing, Δp is the relative position vector, θ offset is the offset angle; According to the angle difference between the angle information in the electromagnetic signal and the offset angle, the angle difference is determined as the corrected angle information in the electromagnetic signal.

6. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm according to claim 5, characterized in that: Acquiring a rectangular coordinate system of the echo data, converting the rectangular coordinate system into a polar coordinate system, and gridding the echo data of the polar coordinate system includes: Acquire the position data of the echo data in a spatial rectangular coordinate system, and convert the position data into a radial distance, azimuth angle, and elevation angle between the detection target and the transmitting end in a polar coordinate system: ； Wherein, x, y, z are the coordinates of the position data respectively, r is the radial distance between the detection target and the transmitting end, θ is the azimuth angle between the detection target and the transmitting end, and ϕ is the pitch angle between the detection target and the transmitting end; Divide the radial distance, azimuth and elevation angle between the detection target and the transmitting end in the polar coordinate system into discrete grid points according to a preset step size; Acquire the reflection intensity in the echo data, and distribute the reflection intensity to each of the discrete grid points according to the relationship between the reflection intensity and the radial distance, azimuth and elevation angle between the detection target and the transmitting end, wherein: If the discrete grid point does not contain the reflection intensity, a weighted average intensity between the reflection intensities in two adjacent discrete grid points is obtained, and the reflection intensity of the discrete grid point is determined according to the weighted average intensity.

7. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm according to claim 6, characterized in that: When removing the Doppler effect in the echo data after gridding processing based on the range Doppler algorithm and determining the azimuth of the echo data based on the polar coordinate fast Fourier transform, it includes: Obtaining the relative radial velocity between the detection target and the transmitting end in the reflection intensity of each discrete grid point; Based on the range Doppler algorithm, the frequency distribution of the echo data is analyzed in the time-frequency domain, and the Doppler shift component is extracted: ； Among them, f d is the Doppler frequency shift component, v r is the relative radial velocity between the detection target and the transmitting end, and λ is the wavelength of the radar wave; removing the Doppler effect from the echo data based on phase compensation according to the relative radial velocity and the Doppler frequency shift component; The echo data with the Doppler effect removed is used to extract the frequency domain signal based on polar coordinate fast Fourier transform: ； Among them, F (r,θ) is the frequency domain signal, f (r,θ) is the echo data in the polar coordinate system, u is the frequency component in the radial direction, v is the frequency component in the angular direction, and j is the imaginary unit in Fourier transform; The power spectrum density of the frequency domain signal is obtained, and based on the power spectrum density of the frequency domain signal, the energy point of each discrete grid point azimuth is obtained, and the energy point with the largest energy value among the energy points of each discrete grid point azimuth is used as the azimuth of the echo data.

8. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm as claimed in claim 7, characterized in that: The method includes dividing the echo data after the Doppler effect is removed into a plurality of sub-regions for block processing based on a pre-configured viewing angle, and reconstructing the image to be generated according to the plurality of sub-regions after the block processing, comprising: Acquiring the scanning angle and imaging pixel requirement of the transmitting end, and establishing a preset segmentation distance according to the relationship between the scanning angle and the imaging pixel requirement; Dividing the echo data after gridding processing into a plurality of sub-areas according to the segmentation distance; Acquire the echo data block in the sub-region, convert the echo data block from time domain data to frequency domain data based on Fourier transform, and extract the spectrum information in the sub-region; According to the radar wavelength, the operating frequency of the radar wave, the position information and attitude information of the transmitter, the position information and attitude information of the receiver, the flight path data and the polar coordinate system of the detection target, and the beam width and main lobe direction of the radar wave in the echo data, the frequency domain data is converted into the detection target image based on the inverse Fourier transform: ； Among them, I (r,θ,ϕ) is the target image, S (f,θ,ϕ) is the frequency domain data, A is the conversion from frequency domain to spatial domain. (r,θ,ϕ) is the compensation coefficient; The detection target images in each of the sub-regions are obtained, and a plurality of the detection target images are spliced ​​according to the preset sub-region division order, wherein: If there is an overlapping image between two adjacent detection target images, the image features of the overlapping image are obtained, and after deleting the overlapping image of any one of the detection target images, the two adjacent detection target images are spliced.

9. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm as claimed in claim 8, characterized in that: When performing image filtering enhancement on the image to be generated, eliminating artifacts in the image to be generated after the image filtering enhancement, and converting the image to be generated into a standard image format for output, it includes: Determining the spliced ​​detection target image as the image to be generated; Acquiring edge sharpness in the image to be generated, and determining a filtering method for performing image filtering enhancement on the image to be generated according to a relationship between the edge sharpness and a preset edge sharpness; Dividing the image to be generated after image filtering and enhancement into a plurality of detection areas, acquiring a plurality of detection areas with inconsistent brightness changes in the detection areas based on gradient detection, and determining the detection areas as artifact areas; Obtain pixel values ​​in each of the detection areas adjacent to the artifact area, and adjust the pixel values ​​in the artifact area to the weighted average pixel value based on the weighted average pixel value between the pixel values ​​in each of the detection areas.

10. The synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm according to claim 9, characterized in that: Determining the filtering method for performing image filtering enhancement on the image to be generated according to the relationship between the edge sharpness and a preset edge sharpness, comprising: When the edge sharpness is lower than the preset edge sharpness, determining to perform image filtering enhancement on the image to be generated based on high-pass filtering; When the edge sharpness is higher than or equal to the preset edge sharpness, it is determined to perform image filtering enhancement on the image to be generated based on low-pass filtering.

Images