A method for imaging moving targets using a one-station fixed dual-station low-frequency ultra-wideband SAR

Abstract

The invention provides a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method. The method firstly simulates a moving target echo signal; secondly, compresses the echo signal in the range direction; then, roughly estimates the speed of the moving target, or uses an existing moving target speed estimation method to estimate its speed more accurately to obtain the speed of the moving target in the range and azimuth directions; finally, after improving the BP algorithm, imaging processing is performed in the time domain, and the speed information of the moving target needs to be added in this process. The one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method improved based on the BP algorithm of the invention is suitable for a one-station fixed dual-station low-frequency ultra-wideband SAR system to perform high-precision imaging processing on a moving target in the time domain.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and more specifically to a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method. Background Art

[0002] Synthetic aperture radar (SAR) has unique advantages in imaging. It is not affected by conditions such as lighting and climate, and is capable of large-scale imaging.

[0003] Compared with the level flight / flying dual-station low-frequency ultra-wideband SAR, the one-station fixed dual-station low-frequency ultra-wideband SAR has a simpler structure due to the existence of fixed stations. It not only saves costs and reduces the risk of being detected, but also makes it easier to process echo signals.

[0004] SAR imaging methods are generally divided into time domain imaging methods and frequency domain imaging methods. The imaging accuracy of frequency domain imaging methods is low, and sometimes it is difficult to meet the military requirements for high-precision imaging of targets. The existing methods for imaging moving targets in the time domain are basically for single-station SAR, while there are fewer methods for imaging moving targets in the time domain with bistatic SAR. This is because the bistatic SAR system has a complex bistatic distance history in the echo signal due to the separate transmission and reception antennas, which greatly increases the derivation of the bistatic SAR refocusing imaging process for moving targets in the time domain.

[0005] The BP algorithm is applicable to all modes of SAR systems, but the BP algorithm can only effectively image stationary targets. When imaging moving targets, defocusing and position shift will occur. Therefore, the BP algorithm cannot perform high-precision imaging of moving targets.

[0006] The prior art discloses a SAR moving target imaging method based on minimum image entropy. The method first uses the RMA algorithm to perform imaging processing on the SAR echo data to obtain a SAR complex image of the observation scene. Secondly, the sub-image data containing the moving target is extracted and recorded as ROI data. Then, a compensation function for the phase error is constructed. With the minimum image entropy as the criterion, the ROI data self-focusing imaging process is converted into an optimization problem. Finally, the imaging processing result with the minimum entropy value is obtained by searching and optimizing, which is the final moving target imaging result. The method can realize SAR moving target imaging processing using a small amount of echo data, thereby providing important information for the accurate identification of moving targets. However, the patent rarely reports on how to overcome the problem that the existing BP algorithm cannot effectively focus on the moving target. Summary of the invention

[0007] The present invention provides a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method, which can effectively perform high-precision imaging on a moving target when the target speed is known.

[0008] In order to achieve the above technical effects, the technical solution of the present invention is as follows:

[0009] A one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method comprises the following steps:

[0010] S1: simulate the echo signal of moving target;

[0011] S2: compress the echo signal in step S1 in the range direction;

[0012] S3: Estimate the speed of the moving target;

[0013] S4: Perform time domain imaging processing on the range compressed signal.

[0014] Furthermore, the center frequency of the one-station fixed dual-station low-frequency ultra-wideband SAR echo signal is f c , prf is the pulse repetition frequency, B is the bandwidth, Kr is the signal modulation frequency; let the point where the moving station antenna is perpendicular to the ground at the slow time t = 0 be the origin of the three-dimensional coordinate system, that is, the moving station antenna is on the Z axis at t = 0; let the moving station move along the X axis with a speed of V, and the Y axis is perpendicular to the X axis and the Z axis; let the coordinates of the moving station antenna at t = 0 be (0,0,H M ), then the coordinates at time t are (Vt,0,H M ); Assume the coordinates of the fixed station antenna are (0,Y S ,H S ); Let P be any moving target in the imaging scene, its coordinates at time t = 0 are set to (x, y, 0), and its velocity is set to v, where the velocity components in the azimuth and range directions are v respectively. x and v y , then its coordinates at time t are (x+v x t,y+v y t, 0); at time t, the distances from the moving target P to the fixed station and the moving station are R S (t) and R M (t), then the sum of the distances to the fixed station and the moving station is R b (t) is:

[0015]

[0016] Furthermore, the specific process of step S1 is:

[0017] Assume that the transmission signal of a fixed dual-station low-frequency ultra-wideband SAR system is:

[0018] f(τ)=exp(j2πf c τ)exp(jπK r τ 2 )

[0019] After the transmitted signal passes through the moving target P, the echo signal received by the receiver is:

[0020]

[0021] where τ represents fast time, σ(x, y) represents the scattering coefficient of the moving target P, ω(t) represents the bistatic SAR antenna pattern, and c represents the speed of light.

[0022] Furthermore, the specific process of step S2 is:

[0023] The moving target echo signal is subjected to range compression. This process is the same as the range compression of the stationary target. When the moving target echo signal is subjected to range compression, windowing is performed to suppress the side lobes and improve the target imaging accuracy. The windowing process and the range compression process are as follows:

[0024]

[0025] s1(τ,t)=IFFT r {FFT r {s(τ,t)}·H r (f τ )}

[0026] Where W k (f τ ) represents the Kaiser window in the frequency domain, β is an adjustable smoothing coefficient, I0 is the standard zero-order Bessel function, H r (f τ ) is the distance frequency domain matched filter function after adding Kaiser window, FFT r / IFFT r It is the fast Fourier transform / inverse transform of distance.

[0027] Furthermore, the specific process of step S3 is:

[0028] After the range compression, the echo signal of the moving target appears as an inclined curve on the image. The range velocity of the moving target can be estimated according to the inclination of the curve. Assuming that the values ​​of the two ends of the curve in the range are l1 and l2, and the values ​​in the azimuth are t1 and t2, it means that in the slow time |t2-t1|, the moving target spans |l2-l1| in the range. Based on this, the speed of the moving target in the range can be estimated, that is:

[0029]

[0030] When calculating the range velocity, first perform a second-order keystone transform on the echo signal after range compression to obtain a signal image after range bending correction. After range bending correction, the moving target echo signal still has range movement, which appears as an inclined straight line on the image. Based on this, the slope of the inclined line can be calculated, and then the range velocity can be calculated based on the slope. The second-order keystone transform can be expressed as:

[0031]

[0032] Using the slope to find the speed in the range direction is as follows:

[0033]

[0034] where f s is the distance sampling frequency;

[0035] The two-station distance history R of the echo signal b (t) Perform Taylor expansion at t = 0:

[0036]

[0037] Among them, φ(t) is a high-order term, and other parameters are expressed as follows:

[0038]

[0039] Ignoring the higher-order terms, we get the azimuth Doppler modulation frequency f d :

[0040]

[0041] Among them, the stationary target azimuth Doppler modulation frequency f ds for:

[0042]

[0043] Then the moving target azimuth Doppler modulation frequency f dm for:

[0044]

[0045] According to the above formula, the moving target azimuth Doppler modulation frequency f dm , its azimuth velocity can be found.

[0046] Furthermore, the specific process of step S4 is:

[0047] The echo signal after range compression is:

[0048]

[0049] When a single-station SAR performs time-domain imaging on a moving target, the moving target is first imaged using the BP algorithm for stationary targets, and then the defocused moving target image is refocused to obtain a moving target image with higher accuracy. The algorithm is expressed as follows:

[0050]

[0051] Where R bs (t) is the two-station distance history of a stationary target:

[0052]

[0053] When the speed information of the moving target is known, the static BP algorithm is improved, that is, R in the reference phase bs (t) Add the speed information of the moving target to make it the two-station distance history R of the moving target b (t), and finally R b By bringing (t) into the image, a high-precision moving target image can be obtained.

[0054] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0055] The method of the present invention first simulates the echo signal of the moving target; secondly, compresses the echo signal in the range direction; then, roughly estimates the speed of the moving target, or uses the existing moving target speed estimation method to estimate its speed more accurately to obtain the speed of the moving target in the range and azimuth directions; finally, after improving the BP algorithm, imaging processing is performed in the time domain, and the speed information of the moving target needs to be added in this process; the one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method based on the BP algorithm improvement of the present invention is suitable for a one-station fixed dual-station low-frequency ultra-wideband SAR system to perform high-precision imaging processing on the moving target in the time domain. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 This is a schematic diagram of the echo signal of a moving target after range compression;

[0058] Figure 3 The BP imaging algorithm for stationary targets and the imaging results of the method of the present invention for moving targets. DETAILED DESCRIPTION

[0059] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0060] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0061] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0062] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0063] Example 1

[0064] like Figure 1 As shown, a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method comprises the following steps:

[0065] S1: simulate the echo signal of moving target;

[0066] S2: compress the echo signal in step S1 in the range direction;

[0067] S3: Estimate the speed of the moving target;

[0068] S4: Perform time domain imaging processing on the range compressed signal.

[0069] The center frequency of the one-station fixed dual-station low-frequency ultra-wideband SAR echo signal is f c , prf is the pulse repetition frequency, B is the bandwidth, Kr is the signal modulation frequency; let the point where the moving station antenna is perpendicular to the ground at the slow time t = 0 be the origin of the three-dimensional coordinate system, that is, the moving station antenna is on the Z axis at t = 0; let the moving station move along the X axis with a speed of V, and the Y axis is perpendicular to the X axis and the Z axis; let the coordinates of the moving station antenna at t = 0 be (0,0,H M ), then the coordinates at time t are (Vt,0,H M ); Assume the coordinates of the fixed station antenna are (0,Y S ,H S ); Let P be any moving target in the imaging scene, its coordinates at time t = 0 are set to (x, y, 0), and its velocity is set to v, where the velocity components in the azimuth and range directions are v respectively. x and v y , then its coordinates at time t are (x+v x t,y+v y t, 0); at time t, the distances from the moving target P to the fixed station and the moving station are R S (t) and R M (t), then the sum of the distances to the fixed station and the moving station is R b (t) is:

[0070]

[0071] The specific process of step S1 is:

[0072] Assume that the transmission signal of a fixed dual-station low-frequency ultra-wideband SAR system is:

[0073] f(τ)=exp(j2πf c τ)exp(jπK r τ 2 )

[0074] After the transmitted signal passes through the moving target P, the echo signal received by the receiver is:

[0075]

[0076] where τ represents fast time, σ(x, y) represents the scattering coefficient of the moving target P, ω(t) represents the bistatic SAR antenna pattern, and c represents the speed of light.

[0077] The specific process of step S2 is:

[0078] The moving target echo signal is subjected to range compression. This process is the same as the range compression of the stationary target. When the moving target echo signal is subjected to range compression, windowing is performed to suppress the side lobes and improve the target imaging accuracy. The windowing process and the range compression process are as follows:

[0079]

[0080] s1(τ,t)=IFFT r {FFT r {s(τ,t)}·H r (f τ )}

[0081] Where W k (f τ ) represents the Kaiser window in the frequency domain, β is an adjustable smoothing coefficient, I0 is the standard zero-order Bessel function, H r (f τ ) is the distance frequency domain matched filter function after adding Kaiser window, FFT r / IFFT r It is the fast Fourier transform / inverse transform of distance.

[0082] The specific process of step S3 is:

[0083] After the range compression, the echo signal of the moving target appears as an inclined curve on the image. The range velocity of the moving target can be estimated according to the inclination of the curve. Assume that the values ​​of the two ends of the curve in the range are l1 and l2, and the values ​​in the azimuth are t1 and t2. In the slow time |t2-t1|, the moving target spans |l2-l1| in the range. Based on this, the speed of the moving target in the range can be estimated, that is:

[0084]

[0085] When calculating the range velocity, first perform a second-order keystone transform on the echo signal after range compression to obtain a signal image after range bending correction. After range bending correction, the moving target echo signal still has range movement, which appears as an inclined straight line on the image. Based on this, the slope of the inclined line can be calculated, and then the range velocity can be calculated based on the slope. The second-order keystone transform can be expressed as:

[0086]

[0087] Using the slope to find the speed in the range direction is as follows:

[0088]

[0089] where f s is the distance sampling frequency;

[0090] The two-station distance history R of the echo signal b (t) Perform Taylor expansion at t = 0:

[0091]

[0092] Among them, φ(t) is a high-order term, and other parameters are expressed as follows:

[0093]

[0094]

[0095] Ignoring the higher-order terms, we get the azimuth Doppler modulation frequency f d :

[0096]

[0097] Among them, the stationary target azimuth Doppler modulation frequency f ds for:

[0098]

[0099] Then the moving target azimuth Doppler modulation frequency f dmfor:

[0100]

[0101] According to the above formula, the moving target azimuth Doppler modulation frequency f dm , its azimuth velocity can be found.

[0102] The specific process of step S4 is:

[0103] The echo signal after range compression is:

[0104]

[0105] When a single-station SAR performs time-domain imaging on a moving target, the moving target is first imaged using the BP algorithm for stationary targets, and then the defocused moving target image is refocused to obtain a moving target image with higher accuracy. The algorithm is expressed as follows:

[0106]

[0107] Where R bs (t) is the two-station distance history of a stationary target:

[0108]

[0109] When the speed information of the moving target is known, the static BP algorithm is improved, that is, R in the reference phase bs (t) Add the speed information of the moving target to make it the two-station distance history R of the moving target b (t), and finally R b By bringing (t) into the image, a high-precision moving target image can be obtained.

[0110] Example 2

[0111] Assume that the center frequency of a fixed dual-station low-frequency ultra-wideband SAR echo signal is f c , prf is the pulse repetition frequency, B is the bandwidth, Kr is the signal modulation frequency; let the point where the moving station antenna is perpendicular to the ground at the slow time t = 0 be the origin of the three-dimensional coordinate system, that is, the moving station antenna is on the Z axis at t = 0; let the moving station move along the X axis with a speed of V, and the Y axis is perpendicular to the X axis and the Z axis; let the coordinates of the moving station antenna at t = 0 be (0,0,H M ), then the coordinates at time t are (Vt,0,H M ); Assume the coordinates of the fixed station antenna are (0,Y S ,H S); Let P be any moving target in the imaging scene, its coordinates at time t = 0 are set to (x, y, 0), and its velocity is set to v, where the velocity components in the azimuth and range directions are v respectively. x and v y , then its coordinates at time t are (x+v x t,y+v y t, 0); at time t, the distances from the moving target P to the fixed station and the moving station are R S (t) and R M (t), then the sum of the distances to the fixed station and the moving station is R b (t) is:

[0112]

[0113] like Figure 1 As shown, a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method comprises the following steps:

[0114] The first step is to simulate the echo signal of the moving target;

[0115] Assume that the transmission signal of a fixed dual-station low-frequency ultra-wideband SAR system is:

[0116] f(τ)=exp(j2πf c τ)exp(jπK r τ 2 )

[0117] After the transmitted signal passes through the moving target P, the echo signal received by the receiver is:

[0118]

[0119] where τ represents fast time, σ(x, y) represents the scattering coefficient of the moving target P, ω(t) represents the bistatic SAR antenna pattern, and c represents the speed of light.

[0120] The second step is to compress the echo signal in the range direction;

[0121] like Figure 2 As shown, the moving target echo signal is compressed in the range direction, and this process is the same as the range compression of the stationary target. When the moving target echo signal is compressed in the range direction, windowing is generally performed to suppress the side lobes and improve the target imaging accuracy. The window functions that can be used in this process include Hamming window, Hanning window, Blackman window and Kaiser window. The more effective window function is the Kaiser window, which can effectively suppress the side lobes and maximize the energy of the main lobe. The process of windowing and range compression is:

[0122]

[0123] s1(τ,t)=IFFT r {FFT r {s(τ,t)}·H r (f τ )}

[0124] Where W k (f τ ) represents the Kaiser window in the frequency domain. β is an adjustable smoothing coefficient, and I0 is a standard zero-order Bessel function. r (f τ ) is the distance frequency domain matched filter function after adding Kaiser window, FFT r / IFFT r It is the fast Fourier transform / inverse transform of distance.

[0125] The third step is to estimate the speed of the moving target;

[0126] The existing moving target velocity estimation method can be used to estimate the velocity of the moving target more accurately. A simple method can also be used to estimate the velocity of the moving target to improve the efficiency of the imaging algorithm. The echo signal of the moving target after range compression appears as an inclined curve on the image. The range velocity of the moving target can be estimated based on the inclination of the curve. Assume that the values ​​of the two ends of the curve in the range direction (expressed in distance) are l1 and l2, and the values ​​in the azimuth direction (expressed in time) are t1 and t2. In other words, during the slow time |t2-t1|, the moving target spans |l2-l1| in the range direction. Based on this, the velocity of the moving target in the range direction can be estimated, namely:

[0127]

[0128] When calculating the range velocity, you can also first perform a second-order keystone transform on the echo signal after range compression to obtain a signal image after range curvature correction. After range curvature correction, the moving target echo signal still has range movement, which is displayed as an inclined straight line on the image. Based on this, the slope of the inclined line can be calculated, and then the range velocity can be calculated based on the slope. The second-order keystone transform can be expressed as:

[0129] Using the slope to find the speed in the range direction is as follows:

[0130]

[0131] where f s is the distance sampling frequency.

[0132] The two-station distance history R of the echo signalb (t) Perform Taylor expansion at t = 0:

[0133]

[0134] Among them, φ(t) is a high-order term, and other parameters are expressed as follows:

[0135]

[0136]

[0137] Ignoring the higher-order terms, we can get the azimuth Doppler modulation frequency f d :

[0138]

[0139] Among them, the stationary target azimuth Doppler modulation frequency f ds for:

[0140]

[0141] Then the moving target azimuth Doppler modulation frequency f dm for:

[0142]

[0143] According to the above formula, the moving target azimuth Doppler modulation frequency f dm , its azimuth velocity can be found.

[0144] The fourth step is to perform time domain imaging processing on the range compressed signal;

[0145] The echo signal after range compression is:

[0146]

[0147] When a single-station SAR performs time-domain imaging on a moving target, it first uses the BP algorithm for stationary targets to image the moving target, and then refocuses the defocused moving target image to obtain a moving target image with higher accuracy. The algorithm is expressed as follows:

[0148]

[0149] Where R bs (t) is the two-station distance history of a stationary target:

[0150]

[0151] When the speed information of the moving target is known, the static BP algorithm can be improved, that is, R bs(t) Add the speed information of the moving target to make it the two-station distance history R of the moving target b (t), and finally R b By bringing (t) into the image, a high-precision moving target image can be obtained.

[0152] Figure 3 It is the result of imaging the moving target using the stationary target BP algorithm and the result of imaging the moving target using the method of the present invention. Figure 3 As can be seen in (a), when the static target BP algorithm is used to image a moving target, the moving target image obtained has defocus and position shift, and it is impossible to obtain a high-precision imaging result. Refocusing it will increase a lot of workload. Figure 3 As can be seen from (b), the result obtained when imaging a moving target using the method of the present invention not only does not cause positional deviation, but also can well focus the image. Figure 3 It can be seen that the application of the method of the present invention can effectively perform high-precision imaging of moving targets in the time domain.

[0153] Example 3

[0154] Assume that the center frequency of a fixed dual-station low-frequency ultra-wideband SAR echo signal is f c , prf is the pulse repetition frequency, B is the bandwidth, Kr is the signal modulation frequency; let the point where the moving station antenna is perpendicular to the ground at the slow time t = 0 be the origin of the three-dimensional coordinate system, that is, the moving station antenna is on the Z axis at t = 0; let the moving station move along the X axis with a speed of V, and the Y axis is perpendicular to the X axis and the Z axis; let the coordinates of the moving station antenna at t = 0 be (0,0,H M ), then the coordinates at time t are (Vt,0,H M ); Assume the coordinates of the fixed station antenna are (0,Y S ,H S ); Let P be any moving target in the imaging scene, its coordinates at time t = 0 are set to (x, y, 0), and its velocity is set to v, where the velocity components in the azimuth and range directions are v respectively. x and v y , then its coordinates at time t are (x+v x t,y+v y t, 0); at time t, the distances from the moving target P to the fixed station and the moving station are R S (t) and R M (t), then the sum of the distances to the fixed station and the moving station is R b (t) is:

[0155]

[0156] Specific parameters are shown in Table 1:

[0157] Table 1 Parameter detailed assignment table

[0158] parameter value parameter value Center frequency 750MHz Signal bandwidth 200MHz Sampling frequency 205MHz Pulse Width 1us Pulse repetition frequency 200Hz Ideal speed for sports station 100m / s Ideal height for fixed stations 10m Ideal height for exercise stations 100m

[0159] like Figure 1 As shown, a one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method comprises the following steps:

[0160] S1: simulate the echo signal of moving target;

[0161] S2: compress the echo signal in step S1 in the range direction;

[0162] S3: Estimate the speed of the moving target;

[0163] S4: Perform time domain imaging processing on the range compressed signal.

[0164] The specific process of step S1 is:

[0165] Assume that the transmission signal of a fixed dual-station low-frequency ultra-wideband SAR system is:

[0166] f(τ)=exp(j2πf c τ)exp(jπK r τ 2 )

[0167] After the transmitted signal passes through the moving target P, the echo signal received by the receiver is:

[0168]

[0169] where τ represents fast time, σ(x, y) represents the scattering coefficient of the moving target P, ω(t) represents the bistatic SAR antenna pattern, and c represents the speed of light.

[0170] The specific process of step S2 is:

[0171] The moving target echo signal is subjected to range compression. This process is the same as the range compression of the stationary target. When the moving target echo signal is subjected to range compression, windowing is performed to suppress the side lobes and improve the target imaging accuracy. The windowing process and the range compression process are as follows:

[0172]

[0173] s1(τ,t)=IFFT r {FFT r {s(τ,t)}·H r (f τ )}

[0174] Where Wk k (f τ ) represents the Kaiser window in the frequency domain, β is an adjustable smoothing coefficient, I0 is the standard zero-order Bessel function, H r (f τ ) is the distance frequency domain matched filter function after adding Kaiser window, FFT r / IFFT r It is the fast Fourier transform / inverse transform of distance.

[0175] The specific process of step S3 is:

[0176] After the range compression, the echo signal of the moving target appears as an inclined curve on the image. The range velocity of the moving target can be estimated according to the inclination of the curve. Assuming that the values ​​of the two ends of the curve in the range are l1 and l2, and the values ​​in the azimuth are t1 and t2, it means that in the slow time |t2-t1|, the moving target spans |k2-l1| in the range. Based on this, the speed of the moving target in the range can be estimated, that is:

[0177]

[0178] When calculating the range velocity, first perform a second-order keystone transform on the echo signal after range compression to obtain a signal image after range bending correction. After range bending correction, the moving target echo signal still has range movement, which appears as an inclined straight line on the image. Based on this, the slope of the inclined line can be calculated, and then the range velocity can be calculated based on the slope. The second-order keystone transform can be expressed as:

[0179]

[0180] Using the slope to find the speed in the range direction is as follows:

[0181]

[0182] where f s is the distance sampling frequency;

[0183] The two-station distance history R of the echo signal b (t) Perform Taylor expansion at t = 0:

[0184]

[0185] Among them, φ(t) is a high-order term, and other parameters are expressed as follows:

[0186]

[0187] Ignoring the higher-order terms, we get the azimuth Doppler modulation frequency f d :

[0188]

[0189] Among them, the stationary target azimuth Doppler modulation frequency f ds for:

[0190]

[0191] Then the moving target azimuth Doppler modulation frequency f dm for:

[0192]

[0193] According to the above formula, the moving target azimuth Doppler modulation frequency f dm , its azimuth velocity can be found.

[0194] The specific process of step S4 is:

[0195] The echo signal after range compression is:

[0196]

[0197] When a single-station SAR performs time-domain imaging on a moving target, the moving target is first imaged using the BP algorithm for stationary targets, and then the defocused moving target image is refocused to obtain a moving target image with higher accuracy. The algorithm is expressed as follows:

[0198]

[0199] Where R bs (t) is the two-station distance history of a stationary target:

[0200]

[0201] When the speed information of the moving target is known, the static BP algorithm is improved, that is, R in the reference phase bs (t) Add the speed information of the moving target to make it the two-station distance history R of the moving target b (t), and finally R b By bringing (t) into the image, a high-precision moving target image can be obtained.

[0202] The same or similar reference numerals correspond to the same or similar components;

[0203] The positional relationships described in the drawings are only for illustrative purposes and should not be construed as limiting the present patent.

[0204] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method, It is characterized in that The following steps are involved: S1: simulate the echo signal of moving target; S2: compress the echo signal in step S1 in the range direction; S3: Estimate the speed of the moving target; S4: Perform time domain imaging processing on the range compressed signal; Among them, the center frequency of the one-station fixed dual-station low-frequency ultra-wideband SAR echo signal is f c , prf is the pulse repetition frequency, B is the bandwidth, Kr is the signal modulation frequency; suppose that the point where the moving station antenna is perpendicular to the ground at the slow time t = 0 is the origin of the three-dimensional coordinate system, that is, the moving station antenna is on the Z axis at t = 0; suppose that the moving station moves along the X axis with a speed of V, and the Y axis is perpendicular to the X axis and the Z axis; suppose that the coordinates of the moving station antenna at t = 0 are (0, 0, H M ), then the coordinates at time t are (Vt, 0, H M ); Assume the coordinates of the fixed station antenna are (0, Y S , H S ); Let P be any moving target in the imaging scene, its coordinates at time t = 0 are set to (x, y, 0), and its velocity is set to v, where the velocity components in the azimuth and range directions are v respectively. x and v y , then its coordinates at time t are (x+v x t,y+v y t, 0); at time t, the distances from the moving target P to the fixed station and the moving station are R S (t) and R M (t), then the sum of the distances to the fixed station and the moving station is R b (t) is: as well as, The specific process of step S1 is: Assume that the transmission signal of a fixed dual-station low-frequency ultra-wideband SAR system is: f(τ)=exp(j2πf c τ)exp(jπK r t 2 ) After the transmitted signal passes through the moving target P, the echo signal received by the receiver is: where τ represents fast time, σ(x, y) represents the scattering coefficient of the moving target P, ω(t) represents the bistatic SAR antenna pattern, and c represents the speed of light.

2. The one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method according to claim 1, It is characterized in that The specific process of step S2 is: The moving target echo signal is subjected to range compression. This process is the same as the range compression of the stationary target. When the moving target echo signal is subjected to range compression, windowing is performed to suppress the side lobes and improve the target imaging accuracy. The windowing process and the range compression process are as follows: s 1 (τ,t)=IFFT r {FFT r {s(τ,t)}·H r (f τ )} Where W k (f τ ) represents the Kaiser window in the frequency domain, β is an adjustable smoothing coefficient, I 0 is the standard zero-order Bessel function, H r (f τ ) is the distance frequency domain matched filter function after adding Kaiser window, FFT r With IFFT r They are the distance-to-fast Fourier transform and the distance-to-inverse fast Fourier transform respectively.

3. The one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method according to claim 2, It is characterized in that The specific process of step S3 is: After the range compression, the echo signal of the moving target appears as an inclined curve on the image. The range velocity of the moving target can be estimated according to the inclination of the curve. Suppose the values ​​of the two ends of the curve in the range direction are l and 1 and l 2 , the values ​​in the azimuth direction are t 1 and t 2 , indicating that at slow time |t 2 -t 1 | time, the moving target has crossed |l 2 -l 1 |, based on which the speed of the moving target in the distance direction can be estimated, namely:

4. The one-station fixed dual-station low-frequency ultra-wideband SAR moving target imaging method according to claim 3, It is characterized in that When calculating the range velocity, first perform a second-order keystone transform on the echo signal after range compression to obtain a signal image after range bending correction. After range bending correction, the moving target echo signal still has range movement, which appears as an inclined straight line on the image. Based on this, the slope of the inclined line can be calculated, and then the range velocity can be calculated based on the slope. The second-order keystone transform can be expressed as: Using the slope to find the speed in the range direction is as follows: where f s is the distance sampling frequency.

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