Arc SAR imaging method and system

Abstract

The present invention discloses a circular arc SAR imaging method and system, comprising the following steps: S1. establishing a radar signal model and a motion geometry model to derive the range history of a target point at different times; S2. converting the range history to the Doppler frequency domain, analyzing the correspondence between range migration and Doppler frequency, designing a phase compensation coefficient, and correcting the range migration in the Doppler frequency domain in the time domain to obtain corrected range data; S3. analyzing the spectral characteristics of the azimuth response function and estimating the effective support domain based on an energy approximation threshold; S4. using a Chirp-Z transform to perform azimuth accumulation of the spectral data within the support domain to obtain an azimuth accumulation result; and S5. combining the corrected range data with the azimuth accumulation result to output a SAR imaging result. The present invention has the advantages of reducing computational complexity and memory requirements.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the field of radar imaging technology, and in particular to a circular arc SAR imaging method and system. BACKGROUND

[0002] Synthetic aperture radar (SAR) technology has been widely used in the detection of airport runway foreign objects (FOD) 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 an airport runway and in scenarios requiring high real-time performance, these traditional methods have the following significant drawbacks:

[0003] 1. High computational complexity

[0004] Although the BPA algorithm can achieve high-precision imaging, its point-by-point superposition calculation method makes it difficult to meet the real-time performance requirements. The RD algorithm reduces some computational load through frequency domain processing, but still requires global Fourier transform for high-resolution imaging, resulting in high computational load.

[0005] 2. Large memory occupation

[0006] Traditional algorithms require storage of full-bandwidth spectral data to complete matched filtering. For example, when a radar scans along a circular arc trajectory at a high sampling rate (such as in an airport runway scenario), the amount of azimuth data increases exponentially, resulting in exponential growth in memory requirements. Actual measurements show that the traditional RD algorithm requires tens of GB of memory when processing typical runway data, severely limiting its deployment on low-cost hardware platforms.

[0007] 3. Waste of spectral resources

[0008] 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 requires computation on all frequency points, resulting in a waste of resources in a large number of invalid frequency bands, further exacerbating the computational and storage pressure. SUMMARY

[0009] To address the technical problems of the prior art, the present application provides a circular arc SAR imaging method and system that reduces computational complexity and required memory.

[0010] To solve the above technical problems, the technical solution proposed by the present application is as follows:

[0011] A circular arc SAR imaging method, comprising the steps of:

[0012] S1. Establishing a radar signal model and a motion geometry model, and deducing a distance history of a target point at different times;

[0013] S2. Converting the distance history to a Doppler frequency domain, analyzing a correspondence between a range migration and a Doppler frequency, designing a phase compensation coefficient, correcting a range migration in the Doppler frequency domain in a time domain, and obtaining corrected range direction data;

[0014] S3. Analyzing a spectrum characteristic of an azimuth direction response function, and estimating an effective support domain based on an energy approximate threshold value;

[0015] S4. Using a Chirp-Z transform to accumulate spectrum data in the support domain in the azimuth direction, and obtaining an azimuth direction accumulation result;

[0016] S5. Combining the corrected range direction data and the azimuth direction accumulation result, and outputting a SAR imaging result.

[0017] Preferably, in step S1, a specific process of establishing the radar signal model is as follows:

[0018] The radar transmits a signal :

[0019]

[0020] wherein , is a slow time, is a pulse width, is a frequency modulation slope, is a carrier frequency, is a total time, , m represents an mth pulse transmitted, is a pulse repetition period;

[0021] After the transmitted signal interacts with a target environment, a reflected echo signal of an arbitrary point P in a scene is described as: P

[0022]

[0023] wherein is an attenuation coefficient, is a time delay of the echo signal of the point P;

[0024] The echo signal received by the radar is mixed and filtered with the transmitted signal, and a difference frequency signal is obtained:

[0025]

[0026] wherein ​​​is a complex constant; and the difference frequency signal is Fourier transformed with respect to the fast time to obtain a difference frequency domain is expressed as:

[0027]

[0028] wherein is the distance to the frequency index, and c is the speed of light;

[0029] The residual video phase is processed by the desloping process to obtain a target one-dimensional range image:

[0030] .

[0031] Preferably, in step S1, the distance history of the target point at different time instants is expressed as:

[0032]

[0033] 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 time instant of OP in the positive direction of OS, wherein S is the position of the radar.

[0034] Preferably, in step S2, the distance history is converted to the Doppler frequency domain, and the specific process of analyzing the correspondence between the distance migration and the Doppler frequency is as follows:

[0035] The expression of the distance history is expressed by the first-order approximation as:

[0036]

[0037] wherein ;

[0038] The first-order approximation of the distance history in the Doppler domain is expressed as:

[0039]

[0040] wherein is the instantaneous Doppler shift of the radar and the target in the radial direction, is the Doppler frequency corresponding to the tangential velocity of the radar and the target;

[0041] 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 target positions have different distance histories.​ corresponding to different It is difficult to correct the range migration caused by different target positions, while in Doppler domain, the range history change is only related to Doppler frequency and is irrelevant to target position.

[0042] Preferably, in step S2, the phase compensation coefficient Specifically:

[0043] .

[0044] Preferably, the specific process of step S3 is:

[0045] S301. Second-order approximation is made on the azimuth phase to obtain an approximate spectral expression of the azimuth response function;

[0046] S302. In combination with the narrowband characteristics of the azimuth response function, an energy approximation threshold is set, and the effective support domain range of the azimuth spectrum satisfying the threshold is calculated.

[0047] Preferably, in step S302, the effective support domain range of the azimuth spectrum satisfying the threshold is calculated by the following formula:

[0048]

[0049] wherein g represents the spectral range index participating in power calculation, is a set of positive integers; is a frequency index, is a spectral position corresponding to the maximum value of the spectral approximation function of each distance unit, is a spectral approximation function of the azimuth response function of each distance unit; is a conjugate of , , N represents the total number of distance unit cells, K represents the total number of spectral unit cells.

[0050] Preferably, the specific process of step S4 is:

[0051] S401. According to the support domain range estimated in step S3, the starting frequency and step length of Chirp-Z transformation are determined;

[0052] S402. The spectral complex values within the support domain range of each distance unit are calculated using Chirp-Z transformation to generate an accumulated azimuth signal.

[0053] Preferably, in step S401, Chirp-ZThe starting point of the transformed arc is: The step length calculated along the arc is .

[0054] The application further discloses a circular arc SAR imaging system, comprising a memory and a processor connected with each other, and the memory stores a computer program.

[0055] Compared with the prior art, the application has the following advantages:

[0056] The application establishes the relationship between the distance migration quantity and the Doppler frequency domain, designs the phase compensation parameter of the distance migration correction to realize the migration correction, estimates the range of the azimuth spectrum support area through the energy approximate threshold method, and then realizes imaging accumulation through Chirp-Z transformation, so that the required computing memory for imaging is greatly reduced, the computing complexity is greatly reduced, and the background noise of the imaging result is improved. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The flowchart of the circular arc SAR imaging method of the application is shown in the embodiment.

[0058] Figure 2 The schematic diagram of the difference frequency signal obtained by mixing the transmitting signal and the echo signal in the application is shown.

[0059] Figure 3 The ArcSAR imaging geometry model diagram in the application is shown.

[0060] Figure 4 The Chirp-Z algorithm flowchart in the application is shown.

[0061] Figure 5 The experimental layout diagram in the application is shown.

[0062] Figure 6 The relationship diagram between the FOD radar spectrum occupancy rate and the energy occupancy rate in the application is shown.

[0063] Figure 7 The imaging result diagrams corresponding to different imaging methods are shown; (a) is the imaging result diagram corresponding to the BP imaging algorithm; (b) is the imaging result diagram corresponding to the RD imaging algorithm; (c) is the imaging result diagram corresponding to the algorithm of the application (energy approximate threshold value 60%); (d) is the imaging result diagram corresponding to the algorithm of the application (energy approximate threshold value 95.1%); (e) is the imaging result diagram corresponding to the algorithm of the application (energy approximate threshold value 99.8%).

[0064] Figure 8Comparison diagrams of azimuth profiles at multiple distances; (a) is a comparison diagram of the azimuth profile at 19.2m; (b) is a comparison diagram of the azimuth profile at 85.7m; (c) is a comparison diagram of the azimuth profile at 80.1m. DETAILED DESCRIPTION

[0065] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0066] like Figure 1 As shown, the arc SAR imaging method provided by the embodiment of the present invention specifically includes the following steps:

[0067] S1. Establish a radar signal model and motion geometry model to determine the distance history of the target point at different times;

[0068] Specifically comprising: S101. constructing a radar signal model based on the linear frequency modulated continuous wave signal transmitted by the radar;

[0069] Since the airport environment has restrictions on the transmission power of radar equipment and the electromagnetic environment is relatively complex, the FOD radar system adopts a linear FMCW system with low transmission power and large bandwidth.

[0070] The radar transmits a signal for:

[0071] (1)

[0072] in , For quick time, For slow time, is the pulse width, is the frequency modulation slope, is the carrier frequency, For full time, , is the pulse repetition period, and m represents the mth pulse emitted.

[0073] After the transmitted signal interacts with the target environment, any point in the scene P The reflected echo signal Described as:

[0074] (2)

[0075] in is the attenuation coefficient, is the echo signal delay of point P;

[0076] like Figure 2 As shown, the echo signal received by the radar is mixed and filtered with the transmitted signal to obtain the difference frequency signal :

[0077] (3)

[0078] wherein is a complex signal constant, the difference frequency signal of formula (3) is Fourier transformed with respect to fast time, and thus a difference frequency domain expression of is obtained:

[0079] (4)

[0080] wherein is a distance direction frequency index, c is the speed of light, the first index term in the above formula is Dechirping processing of the residual video phase unique to pulse compression (RVP) , which will cause a slight change in Doppler, and thus needs to be filtered out, the processing of the term can be completed through a simple "deslope" process RVP , and a target one-dimensional range image is obtained:

[0081] (5)

[0082] S102. According to the relative motion relationship between the radar and the target point, a geometric model of the circular arc synthetic aperture radar is established, and a distance history formula of the target point at different slow times is derived.

[0083] Specifically, the geometric model of the Arc-SAR imaging is shown in Figure 3 , wherein S is the position of the radar array element, is the length of the rotating arm, is the radar erection height, represents the distance from the radar rotation center O to the target point P, represents the angle between the OP line and the ground, represents the platform rotation speed, represents the time of OP in the OS positive direction.

[0084] The formula for the distance of the target point P from the radar at the first time is:

[0085] (6)

[0086] wherein ;

[0087] As can be seen from formula (6), the distance history depends on the target position, the rotation angle, and the length of the rotating arm.

[0088] S2. The echo signal is subjected to range migration correction, and the phase compensation parameters are determined through Doppler frequency domain analysis.

[0089] Specifically comprising: S201. converting the distance history formula to the Doppler frequency domain, and analyzing the corresponding relationship between the distance migration amount and the Doppler frequency;

[0090] Due to the relative motion between the radar and the target, the echo of the same target will be offset in the distance direction. When the offset amount exceeds a distance unit, the distance migration phenomenon occurs, leading to imaging blur, and therefore distance correction must be performed. Compared with the phase change, the sensitivity of the distance migration to the distance change is relatively low. In addition, due to the rotation arm length and the erection height are usually much smaller than the distance r p Therefore, when analyzing the distance migration problem, the formula (6) can be expressed by the first-order approximation as follows:

[0091] (7)

[0092] Wherein ;

[0093] The first-order approximation of the formula (7) in the Doppler domain can be expressed as:

[0094] (8)

[0095] Wherein is the instantaneous Doppler shift of the radar and the target in the radial direction, is the Doppler frequency corresponding to the tangential velocity of the radar and the target.

[0096] It can be seen from the comparison between the formula (7) and the formula (8) that the change of the distance history in the slow time domain is related to the target position, and different correspond to different It is difficult to correct the distance migration caused by different position targets one by one, and in the Doppler domain, the distance history change is only related to the Doppler frequency and is not related to the target position. Therefore, only the migration amount corresponding to the Doppler frequency needs to be corrected in sequence to complete the distance migration correction.

[0097] S202. Designing a phase compensation coefficient to correct the distance migration in the Doppler frequency domain in the time domain;

[0098] It can be seen from the formula (8) that there is a distance difference of the same target at different Doppler frequencies, and after the pulse compression processing, the target distance migration will be caused. According to the Fourier transform property, the frequency distance migration can be corrected by performing the phase compensation in the time domain, and the specific compensation coefficient is as follows:

[0099] (9).

[0100] S3. Analyzing the spectrum characteristics of the azimuth response function, estimating the effective support domain based on an energy approximation threshold;

[0101] Specifically, the second-order approximation is performed on the azimuth phase after the deskew processing, and an approximate spectrum expression of the azimuth response function is obtained.

[0102] Specifically, after the "deskew" processing and the range migration correction (i.e., the decoupling of range and azimuth is completed), the same target will be located in the same range cell at different azimuths. At this time, formula (5) can be simplified as:

[0103] (10)

[0104] wherein is the amplitude term varying along , and at this time, the azimuth phase is mainly affected by the exponential term .

[0105] In the azimuth direction, the conjugate of the exponential term in formula (10) is set as the response function of the matched filter, and the matched filtering of the radar echo along the azimuth direction can complete the azimuth focusing imaging, wherein is the wavelength.

[0106] Generally, the matched filtering is realized by using the fast Fourier transform, however, in actual application, the bandwidth of the response function is limited, and is limited by the motor speed, and in order to improve the signal-to-noise ratio of the system, the sampling rate of the radar along the arc is high, and generally the sampling frequency is much higher than the bandwidth of the response function, so when the matched filtering is realized by using the FFT, a large amount of computing resources is wasted.

[0107] Based on the standing phase principle, the spectrum approximation function of the azimuth response function of each range cell is calculated, , wherein N represents the total number of range cell grids, and K represents the total number of spectrum cell grids.

[0108] S302. Combining the narrowband characteristics of the azimuth response function, setting an energy approximation threshold, and calculating the effective support domain range of the azimuth spectrum when the threshold is met;

[0109] Specifically, an energy approximation threshold is set, and the spectrum width (effective support domain range) corresponding to the energy ratio requirement of each range cell is calculated by formula (11):

[0110] (11)

[0111] wherein g represents the spectrum range index participating in the power calculation, is a set of positive integers; is a frequency index; is a conjugate of the spectrum approximation function is a spectrum position corresponding to a maximum value of the spectrum approximation function power of each distance unit.

[0112] S4. Utilize Chirp-Z transform to efficiently accumulate the spectrum data in the support domain in the azimuth direction;By introducing Chirp-Z transform, the calculation time and storage space required for azimuth response function and radar data azimuth accumulation imaging are significantly reduced.

[0113] Specifically, it comprises:

[0114] S401. According to the support domain range estimated in step S3, determine Chirp-Z the starting frequency and step length of the

[0115] Take the first distance unit as an example, wherein the arc starting point of the Chirp-Z transform is:

[0116] The step length calculated along the arc is .

[0117] S402. On the basis of step S401, use Chirp-Z transform to calculate the spectrum complex value in the support domain range of each distance unit, and generate focused azimuth direction signal. Wherein the specific Chirp-Z transform process is shown in Figure 4 .

[0118] S5. Combine the corrected range direction data with the azimuth accumulation result, and output the SAR imaging result with low background noise.

[0119] The present application establishes a signal model and a motion geometry model, derives the relationship between the range migration quantity and the Doppler frequency domain, designs the phase compensation parameters of the range migration correction to realize the migration correction;In addition, by analyzing the frequency characteristics of the azimuth response function, a method for estimating the support domain based on energy approximation threshold is proposed, and Chirp-Z transform is introduced to reduce the calculation complexity and the required memory. The analysis of the measured data shows that the proposed method can reduce the memory requirement by an order of magnitude, reduce the calculation amount by about 67%, and the imaging result has lower background noise, better environmental adaptability, and important significance for reducing the airport runway monitoring cost, improving the real-time monitoring level, promoting the popularization of the airport runway monitoring scheme, and improving the airport safety.

[0120] Experimental analysis: as Figure 5 ​As shown, a FOD radar monitoring system is placed at the edge of an airport runway, a row of metal pipe target bodies with a spacing d = 5 m is placed along the edge of the runway, the outer diameter of the metal pipe is Φ = 2 cm, the height h = 2 cm, the vertical distance D = 18 m from the center of the target body to the center of the FOD radar base, the distance of each target body to the center of the radar is 18-90 m, the FOD radar rotates from left to right to scan and detect, the scanning angle is -45-190°, and the imaging quality is tested. The FOD monitoring radar system integrates radar, optical camera detection equipment, is deployed at the edge of the airport runway, taxiway and connecting line, and is used for monitoring foreign objects on the airport runway. The device mainly comprises a W-band millimeter wave radar, an infrared video module, a servo mechanism, an edge intelligent computing unit, a laser positioner and the like. For monitoring of a runway, a plurality of edge light type FOD radars are cascaded and work in cooperation in a network, each FOD radar covers a certain area, the data is sorted and the results are reported by the edge computing unit, and the platform collects radar data and uniformly processes the radar data. The millimeter wave radar works in the W band with a bandwidth of 2 GHz, is developed by using a frequency-modulated continuous wave system and an arc synthetic aperture radar imaging (Arc-SAR) technology, breaks through the Arc-SAR fast imaging and weak small target detection in a complex environment, and improves the real-time performance and environmental adaptability of the system.

[0121] Two airport runways are selected, the FOD radar and the target body are placed according to the above layout, and testing is respectively performed, so that it is verified that the algorithm significantly reduces the calculation amount and the memory required for imaging under the premise of ensuring the imaging quality.

[0122] Firstly, the relationship between the spectrum occupancy rate and the response function energy occupancy rate of the current FOD radar azimuth response function is analyzed, and the specific relationship is as shown in Figure 6

[0123] As can be seen from Figure 6 , about 8% of the spectrum resources bear 99.8% of the energy of the azimuth response function. This shows that the spectrum support range of the azimuth response function of the arc FOD radar is relatively narrow, and if all frequency points are calculated in the imaging process, significant resource waste will be caused.

[0124] Based on the above method, the results of processing radar data by the imaging method proposed in the application under different energy approximation threshold values and other imaging methods are compared, and the specific imaging effect is as shown in Figure 7

[0125] Figure 7 The imaging results of the back projection algorithm (BP), the range Doppler algorithm (RD) and the algorithm proposed in the application under different energy approximation threshold values are compared. From Figure 7 ​​From (a) and (b), it can be seen that, compared with the RD imaging result, the BP imaging result has lower background noise in the middle region of the image (which actually corresponds to the lawn around the airport runway), and the imaging quality of the two algorithms is basically equivalent for the metal pipe target distributed on the runway, and the target at a distance of about 150° and 85 meters presents clear imaging effect. Figure 7 From (c)-(e), it can be seen that, as the energy approximation threshold increases, the image quality of the imaging result is continuously improved, and when the energy approximation threshold is 60%, the target in the imaging result has higher sidelobes in the azimuth direction, while when the energy approximation threshold is 95.1%, the sidelobe problem is fundamentally improved, and when the energy approximation threshold is 99.8%, the obtained image is basically consistent with the BP imaging algorithm and the RD imaging algorithm.

[0126] In order to more clearly compare the performance differences of different imaging methods, the nearest target (19.2 m), the farthest target (85.7 m) and a clutter at an intermediate distance (80.1 m) are selected, and the azimuth direction imaging profiles are analyzed in detail. The specific results are as follows: from Figure 8 From (a)-(b), it can be seen that when the energy approximation threshold reaches 99.8%, the azimuth direction imaging effect of the algorithm of the present application is basically equivalent to that of the BP algorithm and the RD algorithm. In the imaging of the farthest metal pipe target (85.7 m), the signal-to-noise ratio reaches about 12 dB, which fully meets the actual detection requirements. In addition, Figure 8 As shown in (c), compared with the traditional RD algorithm, the algorithm of the present application performs better in clutter suppression and has a lower background noise level.

[0127] In combination with Figure 6 It can be seen that when 99.8% of the energy is used for azimuth direction imaging calculation, only about 8% of the spectrum resource is needed. This feature makes the memory requirement of the imaging process reduced by one order of magnitude, and the calculation amount required for imaging reduced by about 67%.

[0128] The present application establishes the relationship between the distance migration quantity and the Doppler frequency domain, designs the phase compensation parameters for distance migration correction to realize migration correction, estimates the azimuth direction spectrum support area range through the energy approximation threshold method, and then realizes imaging accumulation through Chirp-Z transformation, which greatly reduces the calculation memory required for imaging, greatly reduces the calculation complexity, and improves the background noise of the imaging result.

[0129] The embodiment of the present application further provides a circular arc SAR imaging system, comprising a memory and a processor connected with each other, the memory stores a computer program, and the computer program performs the steps of the method as described above when executed by the processor. The system of the present application, corresponding to the method as described above, also has the advantages of the method as described above. The present application realizes all or part of the processes in the above-mentioned embodiment method, and can also be completed by computer program instruction related hardware. The computer program can be stored in a computer readable storage medium, and the computer program can realize the steps of the above-mentioned method embodiment when executed by the processor. The computer program includes computer program code, which 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, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc. The memory is used to store computer programs and / or modules, and 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 can include high-speed random access memory, and can 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 memory device, or other volatile solid state storage device, etc.

[0130] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiment. Any technical solution falling within the concept of the present application belongs to the protection scope of the present application. It should be noted that, for ordinary skilled in the art, some improvements and refinements without departing from the principle of the present application should be considered as the protection scope of the present application.

Claims

1. A circular arc SAR imaging method, characterized in that: Including steps: S1. Establish a radar signal model and motion geometry model to derive the distance history of the target point at different times; S2. Convert the range history to the Doppler frequency domain, analyze the relationship between range migration and 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 data; S3. Analyze the spectral characteristics of the azimuth response function and estimate the effective support region based on the energy approximate threshold; S4. Using Chirp-Z transform to perform azimuth accumulation of the spectrum data in the support domain to obtain an azimuth accumulation result; S5. Combining the corrected range data with the azimuth accumulation results, outputting the SAR imaging results; The specific process of step S3 is: S301. Perform a second-order approximation on the azimuth phase to obtain an approximate spectrum expression of the azimuth response function; S302. Combined with the narrowband characteristics of the azimuth response function, set the energy approximate threshold , calculate the effective support domain range of the azimuth spectrum when the threshold is met.

2. The circular arc SAR imaging method according to claim 1, wherein: In step S1, the specific process of establishing the radar signal model is as follows: Radar transmission signal for: in , For slow time, is the pulse width, is the frequency modulation slope, is the carrier frequency, For full time, , m represents the mth pulse emitted, is the pulse repetition period; After the transmitted signal interacts with the target environment, any point in the scene P The reflected echo signal Described as: in is the attenuation coefficient, is the echo signal delay of point P; The radar received echo signal and the transmitted signal are mixed and filtered to obtain the difference frequency signal : in is a complex signal constant; perform Fourier transform on the difference frequency signal in fast time to obtain the difference frequency domain The expression is: in is the distance frequency index, c is the speed of light; Then, the remaining video phase is processed by de-skewing to obtain the target one-dimensional range image: 。 3. The circular 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: in is the rotating arm length, Indicates the distance from the radar's rotation center O to the target point P, Indicates the angle between the OP line and the ground, Express the radar rotation speed, It indicates the moment when OP is in the positive direction of OS, where S is the position of the radar.

4. The circular arc SAR imaging method according to claim 3, wherein: In step S2, the range history is converted to the Doppler frequency domain, and the specific process of 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: in ; Distance History The first-order approximation of is expressed in the Doppler domain as: in is the instantaneous Doppler 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 distance history The first-order approximate expression and Doppler domain expression of the distance history in the slow time domain are related to the target position. Corresponding to different , it is difficult to correct the range migration caused by targets at different positions one by one. In the Doppler domain, the range history change is only related to the Doppler frequency and has nothing to do with the target position.

5. The circular arc SAR imaging method according to claim 4, characterized in that: In step S2, the phase compensation coefficient Specifically: 。 6. The circular arc SAR imaging method according to claim 4, characterized in that: In step S302, the threshold is calculated by the following formula The effective support range of the time-azimuth spectrum is: Where g represents the spectrum range index involved in power calculation, is a set of positive integers; is the frequency index, is the spectrum position corresponding to the maximum power value of the spectrum approximation function of each distance unit, is the spectrum approximation function of the azimuth response function of each distance unit; for The conjugate of , , N Represents the total number of distance cells, K Indicates the total number of spectrum cells.

7. The circular arc SAR imaging method according to claim 6, wherein: The specific process of step S4 is: S401. Determine the support range estimated in step S3. Chirp-Z The starting frequency and step size of the transformation; S402. Use Chirp-Z The transformation calculates the complex value of the spectrum within the support domain of each range unit to generate an accumulated azimuth signal.

8. The circular arc SAR imaging method according to claim 7, wherein: In step S401, Chirp-Z The starting point of the transformed arc is: , the step length calculated along the arc is .

9. A circular arc SAR imaging system comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 8.

Images