Arc SAR imaging method and system

Abstract

The invention discloses an arc SAR imaging method and system, and the method comprises the steps: S1, building a radar signal model and a motion geometric model, and deducing the distance history of a target point at different moments; s2, converting the range history into a Doppler frequency domain, analyzing a corresponding relation between range migration and Doppler frequency, designing a phase compensation coefficient, and correcting the range migration of the Doppler frequency domain in a time domain to obtain corrected range data; s3, analyzing the spectral characteristics of the azimuth response function, and estimating an effective support domain based on an energy approximation threshold; s4, performing azimuth accumulation on the spectrum data in the support domain by using Chirp-Z transformation to obtain an azimuth accumulation result; and S5, outputting an SAR imaging result in combination with the corrected range direction data and the azimuth direction accumulation result. The method has the advantages that the calculation complexity and the memory required by calculation can be reduced, and the like.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of radar imaging, and particularly relates to a circular arc SAR imaging method and system. Background Art

[0002] Synthetic Aperture Radar (SAR) technology has been widely used in detection fields such as foreign object debris (FOD) on airport runways and deformation monitoring and early warning due to its all-weather and high-resolution imaging capabilities. Traditional SAR imaging algorithms, such as the Back Projection Algorithm (BPA) and the Range Doppler Algorithm (RD), achieve target focusing through phase compensation and pulse compression of radar echo signals. However, in the complex electromagnetic environment of airport runways and scenarios with high real-time requirements, these traditional methods have the following significant drawbacks: 1. High computational complexity Although the BPA algorithm can achieve high-precision imaging, its point-by-point superposition calculation method makes the time complexity difficult to meet the real-time requirements. Although the RD algorithm reduces part of the computational amount through frequency domain processing, global Fourier transform is still required for high-resolution imaging, and the computational amount remains high.

[0003] 2. Excessive memory occupation Traditional algorithms need to store full-bandwidth spectral data to complete matched filtering. For example, when the radar scans along a circular arc trajectory at a high sampling rate (such as in the airport runway scenario), the amount of azimuth data surges, resulting in an exponential increase in memory requirements. Field measurements show that the traditional RD algorithm needs to occupy dozens of GB of memory when processing typical runway data, severely limiting its deployment on low-cost hardware platforms.

[0004] 3. Spectrum resource waste In practical applications, the effective spectral energy of the azimuth response function is concentrated in a very small range of its bandwidth (about 8% of the frequency band carries 99.8% of the energy). Traditional FFT matched filtering needs to calculate all frequency points, resulting in waste of resources in a large number of invalid frequency bands, further exacerbating the computational and storage pressure. Summary of the Invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a circular arc SAR imaging method and system that reduce computational complexity and the memory required for calculation.

[0006] To solve the above technical problems, the technical solution proposed by the present invention is as follows: A circular arc SAR imaging method, comprising the steps of: S1. Establish a radar signal model and a motion geometry model, and deduce the range history of the target point at different times; S2. Convert the range history to the Doppler frequency domain, analyze the corresponding relationship between the range migration amount and the Doppler frequency, design the phase compensation coefficient, and correct the range migration in the Doppler frequency domain in the time domain to obtain the corrected range data; S3. Analyze the spectral characteristics of the azimuth response function and estimate the effective support domain based on the energy approximation threshold; S4. Use the Chirp-Z transform to accumulate the spectral data within the support domain in the azimuth direction to obtain the azimuth accumulation result; S5. Combine the corrected range data and the azimuth accumulation result to output the SAR imaging result.

[0007] Preferably, in step S1, the specific process of establishing the radar signal model is as follows: The radar transmit signal is:

[0008] where , is the slow time, is the pulse width, is the frequency modulation slope, is the carrier frequency, is the full time, , m represents the m-th transmitted pulse, is the pulse repetition period; After the transmit signal interacts with the target environment, for any point P P in the scene, the reflected echo signal is described as:

[0009] where is the attenuation coefficient, is the echo signal time delay of point P; Mix and filter the received echo signal of the radar with the transmit signal to obtain the difference frequency signal :

[0010] where is a complex signal constant; Perform a Fourier transform on the difference frequency signal with respect to the fast time to obtain the expression of the difference frequency domain :

[0011] where is the range frequency index, and c is the speed of light; Then, complete the processing of the remaining video phase through dechirping to obtain the one-dimensional range image of the target: 。

[0012] Preferably, in step S1, the distance history of the target point at different times has the following expression:

[0013] where is the length of the rotating arm, represents the distance from the rotation center O of the radar to the target point P, represents the angle between the line OP and the ground, represents the rotation speed of the radar, represents the time when OP is in the positive direction of OS, where S is the position of the radar.

[0014] Preferably, in step S2, the specific process of converting the distance history to the Doppler frequency domain and analyzing the corresponding relationship between the range migration amount and the Doppler frequency is as follows: The distance history is approximately expressed by the first-order approximation as:

[0015] where ; The first-order approximation of the distance history is expressed in the Doppler domain as:

[0016] where is the instantaneous Doppler frequency shift in the radial direction between the radar and the target, is the Doppler frequency corresponding to the tangential velocity between the radar and the target; Comparing the first-order approximation expression and the Doppler domain expression of the distance history , the change of the distance history in the slow time domain is related to the target position, and different correspond to different , and it is difficult to correct the range migration caused by targets at different positions one by one. In the Doppler domain, the change of the distance history is only related to the Doppler frequency and has nothing to do with the target position.

[0017] Preferably, in step S2, the phase compensation coefficient is specifically: 。

[0018] Preferably, the specific process of step S3 is as follows: S301. Perform a second-order approximation on the azimuth phase to obtain an approximate spectral expression of the azimuth response function; S302. Combining the narrowband characteristics of the azimuth response function, set the energy approximation threshold , calculate the effective support domain range of the azimuth spectrum when the threshold is met.

[0019] Preferably, in step S302, calculate the effective support domain range of the azimuth spectrum when the threshold is met through the following formula: :

[0020] where g represents the spectrum range index participating in power calculation, is the set of positive integers; is the frequency index, is the spectrum position corresponding to the power maximum and minimum of the spectrum approximation function of each range cell, is the spectrum approximation function of the azimuth response function of each range cell; is the conjugate of, , , N represents the total number of range cells, K represents the total number of spectrum cells.

[0021] Preferably, the specific process of step S4 is as follows: S401. Determine the starting frequency and step size of the Chirp-Z transformation according to the support domain range estimated in step S3; S402. Use the Chirp-Z transformation to calculate the complex spectrum values within the support domain range of each range cell, and generate the accumulated azimuth signal.

[0022] Preferably, in step S401, the starting point of the arc of the Chirp-Z transformation is: , and the step size calculated along the arc is .

[0023] The present invention also discloses a circular arc SAR imaging system, including a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method described above.

[0024] Compared with the prior art, the advantages of the present invention are as follows: By establishing the relationship between the range migration amount and the Doppler frequency domain, the present invention designs the phase compensation parameters for range migration correction to achieve migration correction; estimates the support area range of the azimuth spectrum through the energy approximation threshold method; and then realizes imaging accumulation through the Chirp-Z transform, greatly reducing the computing memory required for imaging, greatly reducing the computational complexity, and improving the background noise of the imaging result. Description of the Drawings

[0025] Figure 1Flowchart of the circular arc SAR imaging method of the present invention in an embodiment.

[0026] Figure 2 Schematic diagram of obtaining the difference frequency signal by mixing the transmitted signal and the echo signal in the present invention.

[0027] Figure 3 Geometric model diagram of ArcSAR imaging in the present invention.

[0028] Figure 4 Flowchart of the Chirp-Z algorithm in the present invention.

[0029] Figure 5 Experimental layout diagram in the present invention.

[0030] Figure 6 Relationship diagram between the spectrum occupancy rate and the energy occupancy rate of the FOD radar in the present invention.

[0031] Figure 7 Imaging result diagrams corresponding to different imaging methods; (a) Imaging result diagram corresponding to the BP imaging algorithm; (b) Imaging result diagram corresponding to the RD imaging algorithm; (c) Imaging result diagram corresponding to the algorithm of the present invention (energy approximation threshold 60%); (d) Imaging result diagram corresponding to the algorithm of the present invention (energy approximation threshold 95.1%); (e) Imaging result diagram corresponding to the algorithm of the present invention (energy approximation threshold 99.8%).

[0032] Figure 8 Multi-distance azimuth cross-section comparison diagram; (a) Azimuth cross-section comparison diagram at 19.2 m; (b) Azimuth cross-section comparison diagram at 85.7 m; (c) Azimuth cross-section comparison diagram at 80.1 m. Detailed implementation manners

[0033] The present invention will be further described below in conjunction with the specification drawings and specific embodiments.

[0034] As Figure 1 shown, the circular arc SAR imaging method provided by the embodiment of the present invention specifically includes the following steps: S1. Establish a radar signal model and a motion geometric model, and determine the distance history of the target point at different times; Specifically including: S101. Based on the linear frequency modulated continuous wave signal transmitted by the radar, construct a radar signal model; Due to the limited transmission power of the radar equipment in the airport environment and the relatively complex electromagnetic environment, the FOD radar system adopts a linear FMCW system with low transmission power and large bandwidth.

[0035] Among them, the radar transmitted signal is: (1) Among them , is the fast time, is the slow time, is the pulse width, is the frequency modulation slope, is the carrier frequency, is the total time, , is the pulse repetition period, and m represents the m-th pulse transmitted.

[0036] After the transmitted signal interacts with the target environment, the reflected echo signal at any point P in the scene is described as: (2) Among them is the attenuation coefficient, is the echo signal time delay of point P; As Figure 2 shown, by mixing and filtering the received echo signal of the radar with the transmitted signal, the difference frequency signal can be obtained: (3) Among them is a complex signal constant. By performing a Fourier transform on the difference frequency signal in Equation (3) with respect to the fast time, the expression in the difference frequency domain is obtained: (4) Among them is the range-direction frequency index, c is the speed of light. The first exponential term in the above equation is Dechirping the unique residual video phase for pulse compression processing, (RVP) which will cause a slight change in Doppler. Therefore, it needs to be filtered out, and the processing of the RVP term can be completed through simple "dechirping" processing to obtain the one-dimensional range image of the target: (5) S102. According to the relative motion relationship between the radar and the target point, establish the geometric model of the circular synthetic aperture radar, and deduce the range history formula of the target point at different slow times; Specifically, the geometric model of Arc-SAR imaging is as Figure 3 shown, where S is the position of the radar array element, is the length of the rotating arm, is the radar installation height, represents the distance from the radar rotation center O to the target point P, Denote the angle between the OP connection line and the ground. Express the platform rotation speed. Indicate the moment when OP is in the positive direction of OS.

[0037] The Formula for the distance from the target point P to the radar at the moment is expressed as: (6) Where ; It can be seen from equation (6) that the distance history Depends on the target position, rotation angle, and rotation arm length.

[0038] S2. Perform range migration correction on the echo signal and determine the phase compensation parameters through Doppler frequency domain analysis; Specifically include: S201. Convert the range history formula to the Doppler frequency domain and analyze the corresponding relationship between the range migration amount and the Doppler frequency; Due to the relative motion between the radar and the target, the echo of the same target will shift in the range direction. When the shift amount exceeds one range cell, range migration occurs, resulting in blurred imaging. Therefore, range correction must be performed. Compared with phase changes, range migration is relatively less sensitive to range changes. In addition, due to the rotation arm length And the installation height Are usually much smaller than the range r p , so when analyzing the range migration problem, equation (6) can be approximately expressed in the first order as: (7) Where ; The first-order approximation of equation (7) can be expressed in the Doppler domain as: (8) Where Is the instantaneous Doppler frequency shift in the radial direction between the radar and the target, Is the Doppler frequency corresponding to the tangential velocity between the radar and the target.

[0039] Comparing equation (7) with equation (8), it can be seen that the change of the range history in the slow time domain is related to the target position. Different Correspond to different , and it is difficult to correct the range migrations caused by targets at different positions one by one. However, in the Doppler domain, the change of the range history is only related to the Doppler frequency and has nothing to do with the target position. Therefore, only the migration amounts corresponding to the Doppler frequencies need to be corrected in sequence to complete the range migration correction.

[0040] S202. Design the phase compensation coefficient to correct the range migration in the Doppler frequency domain in the time domain; It can be seen from Equation (8) that there is a range difference for the same target at different Doppler frequencies. After pulse compression processing, it will cause target range migration. According to the properties of Fourier transform, the frequency range migration can be corrected by phase compensation in the time domain. The specific compensation coefficient is as follows: (9).

[0041] S3. Analyze the spectral characteristics of the azimuth response function and estimate the effective support domain based on the energy approximation threshold; Specifically, it includes: S301 performs a second-order approximation on the azimuth phase after dechirping processing to obtain an approximate spectral expression of the azimuth response function; Specifically, after "dechirping" processing and range migration correction (i.e., completing the decoupling of range and azimuth), the same target will be located within the same range cell at different azimuths. At this time, Equation (5) can be simplified as: (10) where is the amplitude term varying along . At this time, the azimuth phase is mainly affected by the exponential term ; In the azimuth direction, the conjugate of the exponential term in Equation (10) is set as the response function of the matched filter. Matching filtering with the radar echo along the azimuth direction can complete azimuth focusing imaging, where is the wavelength.

[0042] Generally, the fast Fourier transform is used to implement the matched filtering. However, in practical applications, the bandwidth of the response function is limited and restricted by the motor speed. At the same time, in order to improve the system signal-to-noise ratio, the radar has a high sampling rate along the arc, and the sampling frequency is generally much higher than the bandwidth of the response function. Therefore, when implementing the matched filtering by the FFT method, a large amount of computing resources are wasted.

[0043] Based on the stationary phase principle, calculate the spectral approximation function of the azimuth response function for each range cell, , , where N represents the total number of range cells and K represents the total number of spectral cells.

[0044] S302. Combine the narrowband characteristics of the azimuth response function, set the energy approximation threshold, and calculate the range of the effective support domain of the azimuth spectrum when the threshold is satisfied; Specifically, set an energy approximation threshold , and calculate the spectral width corresponding to each range cell meeting the energy ratio requirement through Equation (11) (effective support domain range): (11) where \(g\) represents the spectral range index participating in power calculation, is a set of positive integers; is the frequency index; is the spectral approximation function conjugate; is the spectral position corresponding to the maximum and minimum power values of the spectral approximation function of each range cell.

[0045] S4. Use Chirp-Z transform to perform efficient azimuth accumulation on the spectral data within the support domain; by introducing Chirp-Z transform, the computational time and storage space required for the azimuth response function and azimuth accumulation imaging of radar data are significantly reduced.

[0046] Specifically include: S401. According to the support domain range estimated in step S3, determine the Chirp-Z starting frequency and step size of the transform; Taking the th range cell as an example, where Chirp-Z the starting point of the arc of the transform is: , and the step length calculated along the arc is .

[0047] S402. On the basis of step S401, use Chirp-Z transform to calculate the spectral complex values within the support domain of each range cell, and generate a focused azimuth signal. The specific Chirp-Z transform process is as shown in Figure 4 .

[0048] S5. Combine the corrected range data and azimuth accumulation result, and output the SAR imaging result with low background noise.

[0049] By establishing a signal model and a motion geometry model, the present invention deduces the relationship between the range migration amount and the Doppler frequency domain, and designs the phase compensation parameters for range migration correction to achieve migration correction. In addition, by analyzing the frequency characteristics of the azimuth response function, a method based on the energy approximation threshold to estimate the support domain is proposed, and the Chirp-Z transform is introduced to reduce the computational complexity and the memory required for calculation. The analysis of measured data shows that the proposed method reduces the memory requirement by one order of magnitude and the computational amount by about 67% while ensuring the imaging quality, and the imaging result has lower background noise, better environmental adaptability, which is of great significance for reducing the cost of airport runway monitoring, improving the real-time monitoring level, promoting the popularization of airport runway monitoring solutions and enhancing airport safety.

[0050] Experimental analysis: As Figure 5 shown, place the FOD radar monitoring system at the edge of the airport runway, and place a row of metal pipe targets with a spacing d = 5 m along the runway edge. The outer diameter of the metal pipe is Φ = 2 cm and the height h = 2 cm. The vertical distance D from the center of the target to the center of the FOD radar pedestal is 18 m. The distance from each target to the radar center is 18 - 90 m. The FOD radar rotates and scans from left to right, and the scanning angle is -45 - 190°. Examine its imaging quality. The FOD monitoring radar system integrates radar and optical camera detection devices, and is deployed at the edges of airport runways, taxiways and connecting lines for monitoring foreign objects on the airport runway. The device mainly consists of a W-band millimeter-wave radar, an infrared video module, a servo mechanism, an edge intelligent computing unit, a laser locator, etc. For the monitoring of a runway, multiple side-light type FOD radars are cascaded and networked to work together. Each FOD radar covers a certain area, sorts out data separately through the edge computing unit and reports the results. After the platform collects the data of each radar, it is uniformly processed. Among them, the millimeter-wave radar operates in the W band with a bandwidth of 2 GHz, and is developed by using the frequency-modulated continuous-wave system and the arc synthetic aperture radar imaging (Arc-SAR) technology, breaking through the technologies such as Arc-SAR fast imaging and weak small target detection in complex environments, and improving the real-time performance and environmental adaptability of the system.

[0051] Select two airport runways, place the FOD radar and the targets according to the above layout, and conduct tests respectively to verify that the proposed algorithm significantly reduces the computational amount and memory required for imaging while ensuring the imaging quality.

[0052] First, analyze the relationship between the spectrum occupancy rate and the response function energy occupancy rate of the current FOD radar azimuth response function, specifically as Figure 6 shown: From Figure 6It can be seen that approximately 8% of the spectrum resources carry 99.8% of the energy of the azimuth response function. This indicates that the spectrum support range of the azimuth response function of the circular arc FOD radar is relatively narrow. If all frequency points are calculated during the imaging process, it will cause significant resource waste.

[0053] Based on the above method, the results of processing radar data by the imaging method proposed in the present invention and other imaging methods under different energy approximation thresholds are compared. The specific imaging effects are as Figure 7 shown: Figure 7 The imaging results of the back-projection algorithm (BP), the range-Doppler algorithm (RD), and the algorithm proposed in the present invention under different energy approximation thresholds are compared. From Figure 7 (a) and (b) therein, it can be seen that compared with the RD imaging result, the BP imaging result has lower background noise in the middle area of the image (actually corresponding to the lawn around the airport runway). For the metal pipe targets distributed on the runway, the imaging qualities of the two algorithms are basically the same, and the targets at about 150°, 85 meters away all show clear imaging effects. From Figure 7 (c)-(e) therein, it can be seen that with the increase of the energy approximation threshold, the image quality of the imaging result of the algorithm proposed in the present invention is continuously improved. When the energy approximation threshold is 60%, there are relatively high sidelobes in the azimuth direction of the targets in the imaging result, while when the energy approximation threshold is 95.1%, the sidelobe problem is fundamentally improved. When the energy approximation threshold is 99.8%, the obtained image is basically the same as that of the BP imaging algorithm and the RD imaging algorithm.

[0054] To more clearly compare the performance differences of different imaging methods, the nearest target (at 19.2 m), the farthest target (at 85.7 m), and a clutter at an intermediate distance (at 80.1 m) are selected, and their azimuth imaging profiles are compared and analyzed in detail. The specific results are as follows: From Figure 8 (a)-(b) therein, it can be seen that when the energy approximation threshold reaches 99.8%, the azimuth imaging effect of the algorithm of the present invention is basically the same as that of the BP algorithm and the RD algorithm. In the imaging of the farthest metal pipe target (at 85.7 m), the signal-to-noise ratio reaches about 12 dB, fully meeting the actual detection requirements. In addition, Figure 8 (c) therein shows that compared with the traditional RD algorithm, the algorithm of the present invention performs better in clutter suppression and has a lower background noise level.

[0055] Combined with Figure 6 it can be seen that when 99.8% of the energy is used for azimuth imaging calculation, only about 8% of the spectrum resources are required. This characteristic enables the memory requirement in the imaging process to be reduced by one order of magnitude, and at the same time, the required computational amount for imaging can be reduced by about 67%.

[0056] The present invention realizes migration correction by establishing the relationship between the range migration amount and the Doppler frequency domain, and designing the phase compensation parameters for range migration correction; estimates the range of the azimuth spectrum support area by the energy approximation threshold method; and then realizes imaging accumulation through the Chirp-Z transform, which greatly reduces the computing memory required for imaging, greatly reduces the computational complexity, and improves the background noise of the imaging result.

[0057] An embodiment of the present invention further provides an arc SAR imaging system, including a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method described above. The system of the present invention, corresponding to the above method, also has the advantages of the above method. Implementing all or part of the process in the above embodiment method of the present invention can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiment can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. The memory is used to store computer programs and / or modules. The processor realizes various functions by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices, etc.

[0058] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, several improvements and refinements made without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. An arc SAR imaging method, characterized in that, Including the steps: S1. Establish a radar signal model and a motion geometry model, and derive the distance history of the target point at different times; S2. Convert the distance history to the Doppler frequency domain, analyze the corresponding relationship between the range migration amount and the Doppler frequency, design a phase compensation coefficient, and correct the range migration in the Doppler frequency domain in the time domain to obtain the corrected range profile data; S3. Analyze the spectral characteristics of the azimuth response function, and estimate the effective support domain based on the energy approximation threshold; S4. Use the Chirp-Z transform to perform azimuth accumulation on the spectral data within the support domain to obtain the azimuth accumulation result; S5. Combine the corrected range profile data and the azimuth accumulation result to output the SAR imaging result.

2. The arc SAR imaging method according to claim 1, characterized in that, In step S1, the specific process of establishing the radar signal model is as follows: Radar transmission signal is as follows: Among them , is the slow time, is the pulse width, is the frequency modulation slope, is the carrier frequency, is the full time, , where m represents the m-th pulse transmitted, is the pulse repetition period; After the transmitted signal interacts with the target environment, any point in the scene P of the reflected echo signal is described as: wherein is the attenuation coefficient, is the echo signal time delay of point P; Mix the echo signal received by the radar with the transmitted signal and perform mixing and filtering processing to obtain a difference frequency signal : wherein is a complex signal constant; the difference frequency signal is Fourier-transformed with respect to the fast time to obtain an expression in the difference frequency domain as follows: wherein is the range frequency index, and c is the speed of light; Then, complete the processing of the remaining video phase through dechirping to obtain the one-dimensional range image of the target: 。 3. The arc SAR imaging method according to claim 2, wherein In step S1, the distance history of the target point at different times The expression is: wherein is the length of the rotating arm, represents the distance from the rotation center O of the radar to the target point P, represents the angle between the OP line and the ground, represents the rotation speed of the radar, represents the moment when OP is in the positive direction of OS, where S is the position of the radar.

4. The arc SAR imaging method according to claim 3, wherein In step S2, the specific process of converting the distance history to the Doppler frequency domain and analyzing the corresponding relationship between the range migration amount and the Doppler frequency is as follows: The distance journey The expression of is expressed as a first-order approximation: Among them ; Range history The first-order approximation in the Doppler domain is expressed as: wherein is the instantaneous Doppler frequency shift in the radial direction between the radar and the target, is the Doppler frequency corresponding to the tangential velocity between the radar and the target; Comparative range history For the first-order approximation expression and the Doppler domain expression, the variation of the range history in the slow time domain is related to the target position. Different correspond to different , and it is difficult to correct the range migration caused by targets at different positions one by one. In the Doppler domain, however, the variation of the range history is only related to the Doppler frequency and independent of the target position.

5. The arc SAR imaging method according to claim 4, characterized in that In step S2, the phase compensation coefficient Specifically: 。 6. The arc SAR imaging method according to claim 5, wherein The specific process of step S3 is as follows: S301. Perform a second-order approximation on the azimuth phase to obtain an approximate spectral expression of the azimuth response function; S302. Set an approximate energy threshold in combination with the narrowband characteristics of the azimuth response function , and calculate the effective support domain range of the azimuth spectrum when this threshold is satisfied.

7. The arc SAR imaging method according to claim 6, wherein In step S302, the effective support domain range of the azimuth spectrum is calculated by the following formula when it satisfies the threshold : where \(g\) represents the spectral range index involved in power calculation, is the set of positive integers; is the frequency index, is the spectral position corresponding to the power maximum and minimum of the spectral approximation function of each range cell, is the spectral approximation function of the azimuth response function of each range cell; is the conjugate of, , , N represents the total number of range cells, K represents the total number of spectral cells.

8. The arc SAR imaging method according to claim 7, wherein The specific process of step S4 is as follows: S401. Determine the starting frequency and step size of the transformation according to the support domain range estimated in step S3 Chirp-Z of the transformation; S402. Usage Chirp-Z Calculate the complex spectrum values within the support domain of each range cell through transformation, and generate the accumulated azimuth signal.

9. The arc SAR imaging method according to claim 8, characterized in that, In step S401, Chirp-Z The starting point of the transformed arc is: , and the step size calculated along the arc is .

10. An arc SAR imaging system, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, characterized in that, When the computer program is run by a processor, it executes the steps of the method according to any one of claims 1-9.

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