Video SAR Imaging Method with Time-Varying Parameters for Large Squint

Abstract

The present invention relates to a time-varying parameter video SAR imaging method for large squint, including: Step S1: Transmit signals using a time-varying parameter system, and perform dechirping and residual video phase removal processing on the echo signals of the transmitted signals; Step S2: Calculate the azimuth angle estimation values corresponding to each frame in the time-varying parameter video SAR image and the azimuth angle estimation value corresponding to the large frame; Step S3: Calculate the accurate azimuth angle values corresponding to each frame within the large frame and the accurate azimuth angle value corresponding to the large frame according to the azimuth resolution and the azimuth angle estimation value corresponding to the large frame; Step S4: Divide the echo signals processed in Step S1 according to the accurate azimuth angle value corresponding to the large frame to obtain large frames, and perform range interpolation and azimuth interpolation on the data of each large frame in sequence; Step S5: Perform data extraction and two-dimensional fast Fourier transform on the data of each frame in the large frames after interpolation processing in sequence; Step S6: Perform geometric distortion correction and coordinate system rotation correction on the imaging results of Step S5.

Description

Technical Field

[0001] The present invention relates to a method for video SAR imaging with time-varying parameters in large squint, and belongs to the technical field of Synthetic Aperture Radar (SAR). Background Art

[0002] Synthetic Aperture Radar (SAR) is an active remote sensing device that emits electromagnetic waves and can achieve high-resolution imaging all day and all weather. It is an indispensable technical means in the current field of earth observation. As a new SAR system, video SAR can achieve high-frame-rate continuous frame images by continuously observing the scene area and can detect the dynamic changes of the target scene. Large squint video SAR is an important working mode, in which the complementary angle (squint angle) between the beam line-of-sight direction and the track direction changes continuously with the synthetic aperture time and can reach more than sixty degrees, enabling early perception of scene information and having obvious advantages in battlefield reconnaissance, terrain mapping, etc. However, large squint video SAR faces problems of poor image resolution and low operation efficiency. In the large squint configuration, the traditional constant parameter system causes serious distortion of the Point Spread Function (PSF) of the target in the video SAR image, and the distortion degree of the target PSF in each frame is inconsistent, which is not conducive to observation and analysis. When traditional large squint video SAR is imaging, each frame is processed separately. Taking the Polar Format Algorithm (PFA) as an example, two-dimensional interpolation operations need to be performed on each frame of data, resulting in low operation efficiency and being difficult to meet the real-time requirements of video SAR.

[0003] Therefore, it is quite necessary to develop a video SAR imaging system and method under large squint. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for video SAR imaging with time-varying parameters in large squint to solve the problems of poor resolution performance and low operation efficiency in large squint video SAR imaging.

[0005] To solve the above technical problems, the present invention provides a method for video SAR imaging with time-varying parameters in large squint, including:

[0006] Step S1: Transmit signals using a time-varying parameter system, receive the echo signals of the transmitted signals, and perform dechirping and residual video phase removal processing on the echo signals;

[0007] Step S2: Calculate the azimuth angle estimation value corresponding to each frame in the processed echo signal in Step S1, and calculate the azimuth angle estimation value corresponding to the large frame according to the requirement of the azimuth wave number width loss rate, where the large frame is formed by combining several frames;

[0008] Step S3: Calculate the accurate azimuth angle value corresponding to each frame within the large frame according to the azimuth resolution and the azimuth angle estimation value corresponding to the large frame, and calculate the accurate azimuth angle value corresponding to the large frame;

[0009] Step S4: Divide the echo signal processed in Step S1 according to the accurate azimuth angle value corresponding to the large frame to obtain the data corresponding to the large frame, and perform range - direction and azimuth - direction interpolation on the data of each large frame in turn;

[0010] Step S5: Perform data extraction and two - dimensional fast Fourier transform on the data of each frame in the large frame after interpolation processing in turn to obtain the imaging result in the large - frame coordinate system;

[0011] Step S6: Perform geometric distortion correction and coordinate system rotation correction on the imaging result in Step S5 to obtain the video SAR imaging result.

[0012] Optionally, in the time - varying parameter system, the carrier frequency and chirp rate of the radar platform are related to the platform position at the azimuth sampling time, expressed as:

[0013]

[0014] where t represents the azimuth time, f c0 and γ0 respectively represent the initial transmitted signal carrier frequency and the chirp rate of the linear frequency - modulated signal, f c and γ respectively represent the carrier frequency and chirp rate varying with the azimuth time, β(t) represents the instantaneous depression angle from the platform to the scene center at the azimuth sampling time, and β0 represents the depression angle at the center time of the synthetic aperture.

[0015] Optionally, the azimuth wave number width loss rate P aziloss in Step S2 is obtained by the following formula:

[0016]

[0017] where K xa is the original azimuth wave number width, and K xb is the azimuth wave number width used for frame imaging at the edge of the large frame.

[0018] Optionally, the method for calculating the azimuth angle estimation value corresponding to the large frame in Step S2 is as follows:

[0019] Continuously adjust the azimuth angle of the large frame to find the azimuth wave number width loss rate P aziloss closest to and less than the azimuth wave number width loss rate threshold Paziindex The azimuth angle of the large frame at this time, and this azimuth angle of the large frame is the estimated value of the azimuth angle corresponding to the final large frame

[0020] Optionally, in step S3, the accurate azimuth angle value corresponding to the large frame is obtained by accumulating the accurate azimuth angle values corresponding to each frame within the large frame.

[0021] In the technical solution of the present invention, in terms of the radar system, aiming at the problem that the target PSF distortion in each frame is inconsistent under the constant parameter system, a method of time-varying adjustment of radar parameters (referred to as time-varying parameters) is adopted, so that the PSF of each frame image of the large squint video SAR meets consistency, improving the spatial resolution performance and imaging performance; in terms of operation efficiency, based on the large squint video SAR of the wavenumber domain algorithm PFA, aiming at the problem of low operation efficiency, the present invention combines several frames into a large frame, performs unified range-direction and azimuth-direction interpolation (interpolation batch processing) on the data of this large frame, and then extracts and performs subsequent imaging processing on the data of each frame within the large frame. Because the large squint video SAR realizes high frame rate imaging by overlapping frame data, the data interpolation batch processing operation can reduce the amount of calculation and achieve the purpose of improving the operation efficiency. Brief Description of the Drawings

[0022] Figure 1 is a flowchart of the large squint time-varying parameter video SAR imaging method according to an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of the geometric configuration of the large squint video SAR according to an embodiment of the present invention;

[0024] Figure 3(a) is a schematic diagram of dividing the wavenumber spectrum of the large frame in the video SAR wavenumber spectrum according to an embodiment of the present invention; Figure 3(b) is a schematic diagram of dividing frames in the wavenumber spectrum of the large frame according to an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of calculating the estimated value of the azimuth angle of the large frame provided by an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of calculating the accurate azimuth angle value corresponding to each frame within the large frame according to an embodiment of the present invention;

[0027] Figure 6(a) and 6(b) is a schematic diagram of performing unified range-direction interpolation within the large frame according to an embodiment of the present invention;

[0028] Figure 7(a) is a schematic diagram of the wavenumber spectrum before performing unified azimuth-direction interpolation within the large frame according to an embodiment of the present invention; Figure 7(b) is a schematic diagram of the wavenumber spectrum after performing unified azimuth-direction interpolation within the large frame according to an embodiment of the present invention;

[0029] Figure 8 It is a schematic diagram of extracting frame data from large-frame data in an embodiment of the present invention;

[0030] Figure 9 It is a schematic diagram of the simulation result for verifying the superiority of the time-varying parameter system in an embodiment of the present invention;

[0031] Figure 10 It is a schematic diagram of the simulation result for verifying the accuracy of the method proposed in the present invention in an embodiment of the present invention. Detailed implementation manners

[0032] The following further describes in detail a large squint time-varying parameter video SAR imaging method proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. According to the following description and the claims, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in a very simplified form and use non-precise scales, only for conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0033] The present invention provides a large squint time-varying parameter video SAR imaging method, and its process is as Figure 1 shown, including the following steps:

[0034] Step S1: Transmit signals using a time-varying parameter system, receive the echo signals of the transmitted signals, and perform dechirping and residual video phase removal processing on the echo signals;

[0035] The geometric configuration of the large squint video SAR is as Figure 2 shown. In the figure, a coordinate system XYZ is established with the center point of the scene as the origin O. XOY represents the ground plane, the direction of OZ points away from the center of the earth and is perpendicular to the ground surface, and the direction of OY is along the projection direction of the slant range at the center time of the synthetic aperture on the ground plane. Points A and S represent the positions of the platform at the center time of the synthetic aperture and at any azimuth sampling time respectively. Point P represents any scatterer in the scene, and its coordinates are (x p , y p , 0). H represents the flight altitude of the platform at the center time of the synthetic aperture, t represents the azimuth time (slow time), R c (t) represents the instantaneous reference slant range from the platform to the center of the scene, and R p (t) represents the instantaneous slant range from the platform to the scatterer P. φ is the dive angle of the radar velocity. β(t) represents the instantaneous depression angle from the platform to the center of the scene, and β0 represents the depression angle at the center time of the synthetic aperture. δ(t) represents the angle between the ground projection of the platform movement direction and the ground projection of the reference slant range R c (t) (the complement of the ground squint angle), and δ0 represents the complement of the ground squint angle at the center time of the synthetic aperture. The azimuth angle α(t) represents the instantaneous reference slant range R c(t) The instantaneous angle formed by the projection on the ground plane and OY. In the video SAR system, the synthetic aperture time is relatively long, and the change range of the azimuth angle α(t) is relatively large, up to more than twenty degrees.

[0036] Assume that the transmitted signal is a chirp signal. However, different from the traditional constant radar parameter system, in the time-varying parameter system, the radar platform parameters (carrier frequency and chirp rate) are related to the platform position at the azimuth sampling time, and they are expressed as

[0037]

[0038] where t represents the azimuth time (slow time), f c0 and γ0 respectively represent the initial transmitted signal carrier frequency and the chirp rate of the chirp signal, f c and γ respectively represent the carrier frequency and chirp rate that change with the azimuth time. Then the transmitted signal can be expressed as

[0039]

[0040] where j is the imaginary symbol, τ represents the range time (fast time), T p represents the pulse width, and rect(·) represents the window function.

[0041] Demodulate the received echo signal to the baseband. The echo signal of any target point P can be expressed as

[0042]

[0043] where c represents the speed of light.

[0044] Next, perform de-chirp and remove the residual video phase (RVP) processing on the received echo signal, and map the processed echo signal from the analog domain to the digital domain, which can be expressed as

[0045]

[0046] where i and m respectively represent the fast time sequence and the slow time sequence, N r and N a are the number of sampling points in the range direction and the azimuth direction respectively, R c (m) and R p (m) respectively represent the digital domain representations of R c (t) and R p (t), and F s is the sampling rate. According to the wavefront plane hypothesis and the geometric configuration, project Equation (4) into the wavenumber domain, and at the azimuth wavenumber K x and the range wavenumber K yPerform two-dimensional Taylor expansion, and the result is

[0047] s2(i,m)≈exp{j((y p +U1(x p ,y p ))K y (i,m)+(x p +U2(x p ,y p ))K x (i,m))} (5)

[0048] Among them, x p and y p are the azimuth and range coordinates of the imaging target respectively. The azimuth wavenumber K x (i,m) and the range wavenumber K y (i) are as shown in the following formula (6). U1(x p ,y p ) and U2(x p ,y p ) are the geometric distortion variables in the range and azimuth directions caused by the wavefront hypothesis respectively. They are only determined by the scene fixed parameters and the coordinates (x p ,y p ) of the scene target point.

[0049]

[0050] Among them, α(m) is the digital domain representation of α(t).

[0051] Step S2: Calculate the azimuth angle estimation value corresponding to each frame in the echo signal processed in Step S1, and calculate the azimuth angle estimation value corresponding to the large frame according to the requirement of the azimuth wavenumber width loss rate. The large frame is formed by combining several frames;

[0052] Specifically, after receiving the echo signal using the time-varying parameter system, the wavenumber spectrum of the video SAR is shown as the entire fan ring in Fig. 3(a). The dark gray fan ring in the figure is the wavenumber spectrum corresponding to the azimuth angle α b of the large frame, and Δα b is the angle between adjacent large frames. Fig. 3(b) is a schematic diagram of dividing frames within the wavenumber spectrum of a large frame. At this time, it is established in the large frame coordinate system K x ′-K y ′. In the figure, α b represents the azimuth angle of the large frame corresponding to the entire wavenumber spectrum, α s represents the frame azimuth angle corresponding to the wavenumber spectrum of a certain frame, and Δα s represents the angle between adjacent frames (because the large squint video SAR realizes high frame rate imaging by overlapping frame data, so Δα s is less than α s)。

[0053] Next, start calculating the estimated values of the frame and large-frame azimuth angles. According to the synthetic aperture radar imaging theory, to achieve a certain azimuth resolution ρ x , the estimated value of the azimuth angle (synthetic aperture accumulation angle) corresponding to the frame in the time-varying parameter video SAR approximately satisfies the following formula

[0054]

[0055] To improve the operation efficiency, the data corresponding to the large frame needs to be uniformly interpolated in two dimensions. However, the frames at the edges will lose a certain azimuth wavenumber width. When the loss rate P of the azimuth wavenumber width aziloss is less than the azimuth wavenumber width loss rate threshold P aziindex (usually 5%), this loss can be approximately ignored. The loss rate of the azimuth wavenumber width increases with the increase of the large-frame azimuth angle. Therefore, according to the estimated value of the frame azimuth angle and the azimuth wavenumber width loss rate threshold P aziindex to calculate the estimated value of the large-frame azimuth angle The calculation method is as Figure 4 shown. In the figure, the dark gray sector ring represents the wavenumber spectrum corresponding to the large frame, and the light gray sector ring represents the wavenumber spectrum corresponding to the frame, and represent the azimuth angle corresponding to the frame and the azimuth angle corresponding to the large frame respectively. In the figure, this frame is located at the edge of the large frame, Figure 4 and the light-colored part in ya K and yb K are the expressions of the distance wavenumber between two points K1 and K2 in the large-frame wavenumber spectrum, as shown in Eq. (8). The horizontal dotted line represents the unified range interpolation boundary within the large frame, and the two vertical dotted lines on the left represent the azimuth interpolation boundaries when this frame is interpolated separately in azimuth (for selecting the frame data after the large-frame unified interpolation). The azimuth wavenumber width loss rate P aziloss is calculated by Eq. (9), where the original azimuth wavenumber width K xa and the azimuth wavenumber width K xb used for imaging the frame at the edge of the large frame are expressed in Eq. (10).

[0056]

[0057]

[0058]

[0059] Compare the calculated azimuth wavenumber width loss rate P aziloss with the azimuth wavenumber width loss rate threshold P aziindex If Paziloss <P aziindex , the azimuth angle of the large frame meets the loss rate requirement. By continuously adjusting the azimuth angle of the large frame, the loss rate P of the azimuth wavenumber width is found aziloss closest to and less than the loss rate threshold P of the azimuth wavenumber width aziindex The azimuth angle corresponding to the large frame at this time is the estimated value of the azimuth angle corresponding to the final large frame

[0060] Step S3: According to the azimuth resolution and the estimated value of the azimuth angle corresponding to the large frame, calculate the accurate value of the azimuth angle corresponding to each frame in the large frame, and calculate the accurate value of the azimuth angle corresponding to the large frame;

[0061] To make the azimuth resolution of each frame in the large frame the same, use the estimated value of the azimuth angle of the large frame calculated in step S2 and the azimuth resolution ρ x , calculate the accurate value of the azimuth angle α corresponding to each frame in the large frame s (for extracting the frame data in the large frame in step S5), and the solution method is as Figure 5 shown. In Figure 5 , the dark gray fan ring is the wavenumber spectrum corresponding to the large frame, and the light gray fan ring is the wavenumber spectrum corresponding to a certain frame. The starting rotation angle of this frame is α start_s . K1 and K2 are two points in the large frame wavenumber spectrum, and their distance wavenumber expressions K ya and K yb are as shown in Equation (8). The horizontal dotted line represents the unified range interpolation boundary in the large frame, and the leftmost and rightmost vertical dotted lines represent the azimuth interpolation boundaries when interpolating each frame separately (for selecting the frame data after unified interpolation in the large frame). According to the azimuth resolution ρ x calculate the required azimuth beam width 2π / ρ x , and the expressions of the wavenumber widths K xc and K xd in the figure are as shown in Equation (11). Therefore, Figure 5 the azimuth angle α of this frame in s is as shown in Equation (12).

[0062]

[0063]

[0064] According to the above method, find the accurate value of the azimuth angle α corresponding to each frame in the large frame s (n s )(the distribution diagram of the frames in the large frame is shown in Figure 3(b), and the angle between each frame is Δα s ), n s represents the frame sequence, and the value range is 1,…,N s , where Ns is the number of large frames. Then, for the obtained α s (n s ), accumulate them to calculate the accurate azimuth value α corresponding to the large frame b . Use the obtained accurate azimuth value α corresponding to the large frame b and the accurate azimuth value α corresponding to each frame within the large frame s (n s ) to calculate the azimuth wavenumber width loss rate of the frames at the edge of the large frame and verify whether the requirements are met..

[0065] Step S4: Divide the echo signal processed in Step S1 according to the accurate azimuth value corresponding to the large frame to obtain the data corresponding to the large frame, and perform range - direction and azimuth - direction interpolation on the data of each large frame in sequence;

[0066] According to the accurate azimuth value α corresponding to the large frame b , divide the video SAR data (see Equation (5)), and establish the K y ′ axis along the angular bisector of the azimuth angle of the large frame, and establish the K y ′ axis perpendicular to the K x ′ axis direction, as shown in Figure 6(a). In the figure, α z is the angle between the K y ′ axis and the K y axis (α z is negative on the left side of the K y axis and positive on the right side of the K y axis). The data corresponding to the divided large frame is expressed as

[0067]

[0068] where N ab represents the number of azimuth points in the large frame, and m b represents the slow - time sequence within the large frame. x′ p and y′ p are the positions of the target in the large - frame coordinate system. U1(x′ p ,y′ p ) and U2(x′ p ,y′ p ) are the geometric distortion variables in the range - direction and azimuth - direction caused by the wavefront hypothesis respectively. The expressions of the azimuth wavenumber K x ′(i,m b ) and the range wavenumber K y ′(i,m b ) in the large frame are shown in Equation (14), where α bin (m b ) represents the instantaneous azimuth angle in the large - frame coordinate system.

[0069]

[0070] Interpolate the data of the large frame in the range direction uniformly. As shown in Fig. 6(b), the dashed line in the figure represents the range direction interpolation boundary, and ΔK y is the range direction wavenumber width after range direction interpolation. The range direction interpolation is achieved through the mapping of the following formula.

[0071]

[0072] where i′ is the fast time series after range direction interpolation, K y ″(i′) and K y ″(i′)tanα bin (m b ) represent the range direction wavenumber and azimuth direction wavenumber after range direction interpolation respectively.

[0073] The signal expression after range direction interpolation is as follows

[0074] s bf (i′,m b ) = exp{j((y′ p +U1(x′ p ,y′ p ))K y ″(i′)+(x′ p +U2(x′ p ,y′ p ))(K y ″(i′)tanα bin (m b )))} (16)

[0075] Interpolate the signal after range direction interpolation in the azimuth direction uniformly. The wavenumber spectrum before azimuth direction interpolation is shown in Fig. 7(a). The dashed line in the figure is the boundary of the uniform azimuth direction interpolation. The azimuth direction interpolation can be achieved through the mapping of the following formula.

[0076]

[0077] where m b ′ represents the slow time series after azimuth direction interpolation, u x represents the azimuth direction wavenumber interval, and K x ″(m b ′) is the azimuth direction wavenumber after azimuth direction interpolation. The wavenumber spectrum after azimuth direction interpolation is shown in Fig. 7(b) and can be expressed as

[0078] s bf (i′,m b ′) = exp{j((y′ p +U1(x′ p ,y′ p))K y ″(i′)+(x′ p +U2(x′ p ,y′ p ))(K x ″(m b ′)))} (18)

[0079] Step S5: Perform data extraction and two-dimensional fast Fourier transform on the data of each frame in the large frame after interpolation processing to obtain the imaging result in the large frame coordinate system;

[0080] For the large frame data after azimuth interpolation in step S4, extract the frame data according to the accurate azimuth angle α s (n s ) of each frame in the large frame, as Figure 8 shown. The dark gray rectangle in the figure is the wavenumber spectrum after azimuth interpolation of the large frame, and the area enclosed by the dotted line in the figure is the wavenumber spectrum before azimuth interpolation of a certain frame. The wavenumber spectrum after azimuth interpolation of this frame is shown as the light gray rectangle in the figure. Therefore, extract this frame of data (light gray rectangle) from the large frame data (dark gray rectangle). ΔK x is the azimuth wavenumber width of this frame, and the signal of a certain frame after extraction is as follows

[0081] s sf (i′,m s )=exp{j((y′ p +U1(x′ p ,y′ p ))K y ″(i′)+(x′ p +U2(x′ p ,y′ p ))(K x ″(m s )))}(19)

[0082] where K x ″(m s ) represents the extracted azimuth wavenumber, m s is the extracted azimuth sequence, and the number of points is N as . K x ″(m s ) can also be expressed as K x ″(m s )=u x m s =u x m s ′+ΔK xdev , where ΔK xdev represents the azimuth wavenumber offset of this frame, m s ′=-N as / 2, …, N as / 2. Therefore, Equation (19) can be expressed as

[0083] s sf (i′, m s ′) = exp{j((y′ p + U1(x′ p , y′ p ))K y ″(i′)+(x′ p + U2(x′ p , y′ p ))(u x m s ′+ ΔK xdev ))} (20)

[0084] Performing a two-dimensional fast Fourier transform on the above equation gives the PFA processing result. The signal after the two-dimensional fast Fourier transform is as follows

[0085]

[0086] where, sinc(·) represents the sinc function, ΔK x and ΔK y respectively represent the azimuth wavenumber width and the range wavenumber spectrum width after interpolation.

[0087] Next, the operation efficiency is analyzed. Assume that the frame interval angle Δα s = P j α s . Since the large squint video SAR realizes high frame rate imaging by overlapping frame data, so P j < 1. Therefore, in the PFA interpolation operation, the operation efficiency of the large frame unified two-dimensional interpolation proposed by the present invention is about 1 / P j times that of the traditional method (performing two-dimensional interpolation on each frame separately).

[0088] Step S6: Perform geometric distortion correction and coordinate system rotation correction on the imaging result of Step S5 to obtain the video SAR imaging result.

[0089] The imaging result obtained in Step S5 is obtained in each large frame coordinate system K x ′-K y ′. It is necessary to obtain the video SAR coordinate system K x -K yThe imaging results under... Geometric distortion correction and coordinate system rotation correction are achieved through two image resamplings. The two image resampling processes are directly cascaded without any other operations in between. Therefore, these two image resamplings can be combined. Image resampling is achieved through the mapping of Equation (22). Through Equation (22), the imaging results in different large-frame coordinate systems are unified to the video SAR coordinate system.

[0090] (x′ p +U2(x′ p ,y′ p ),y′ p +U1(x′ p ,y′ p ))→(x p ,y p ) (22)

[0091] The video SAR imaging process can be summarized as follows: Interpolate the data of the first large frame in the range and azimuth directions using Step S4, and then perform imaging processing on each frame within this large frame in turn using the methods of Step S5 and Step S6. After that, interpolate the next large frame in the range and azimuth directions using Step S4, and so on, finally obtaining the imaging results of each frame of the video SAR.

[0092] To verify the superiority of the time-varying parameter SAR system, the parameters in Table 1 are used. Table 1 shows some simulation parameters of the large squint time-varying parameter video SAR required. Compare the point target PSFs of the conventional system video SAR and the time-varying parameter system video SAR. The simulation results are as Figure 9 shown. Figure 9 In (a), (b), and (c) are the result diagrams of the 1st frame, the 67th frame, and the 155th frame of the conventional system video SAR imaging the origin, without image resampling. Their PSF distortion angles are 48.1°, 53.2°, and 62.5° respectively. It can be seen that the PSFs of different frames are inconsistent. Figure 9 In (d), (e), and (f) are the result diagrams of the 1st frame, the 67th frame, and the 155th frame of the time-varying parameter system video SAR imaging the origin, without image resampling. Their PSF included angles are all 90°. It can be seen that the PSFs of different frames satisfy consistency, proving the superiority of the time-varying parameter system.

[0093] Table 1 List of key parameters of the large squint time-varying parameter video SAR

[0094]

[0095] To verify the accuracy of the proposed large squint time-varying parameter video SAR method, the parameters in Table 1 are used to perform simulation analysis on the dot matrix target. The distribution schematic diagram of the dot matrix is as Figure 10 shown in (a).Figure 10 In Figures (b), (c), and (d), the 11th, 77th, and 143rd frame result images of the proposed method for imaging dot matrix targets are shown respectively. Image resampling has been performed, and it can be seen that the position of the target in the imaging result is consistent with the set position, which proves the accuracy of the method of the present invention.

[0096] To verify the efficiency of the proposed large squint time-varying parameter video SAR method, it is compared with conventional parameter video SAR and time-varying parameter video SAR (without using interpolation batch processing). This method processes 176 frames of images and takes 160.88 seconds; conventional parameter video SAR processes 176 frames of images and takes 893.89 seconds (in order not to lose valid data, zero-padding is required before range interpolation, increasing the amount of computation); time-varying parameter video SAR (without using interpolation batch processing) processes 176 frames of images and takes 484.48 seconds. Therefore, the processing speed of this method is 5.5 times that of conventional parameter video SAR, and the processing speed of this method is 3 times that of time-varying parameter video SAR without using interpolation batch processing. Therefore, the efficiency of the large squint time-varying parameter video SAR imaging method of the present invention is proved.

[0097] The above specific description further details the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a description of the preferred embodiments of the present invention and does not limit the scope of the present invention in any way. Any changes and modifications made by those of ordinary skill in the field of the present invention based on the above disclosure fall within the protection scope of the claims.

Claims

1. A method for video SAR imaging with large squint time-varying parameters, characterized in that Including: Step S1: Transmit a signal using a time-varying parameter system, receive the echo signal of the transmitted signal, and perform dechirping and residual video phase removal processing on the echo signal; Step S2: Calculate the azimuth angle estimation value corresponding to each frame in the processed echo signal in Step S1, and calculate the azimuth angle estimation value corresponding to the large frame according to the requirement of the azimuth wave number width loss rate, where the large frame is formed by combining several frames; Step S3: Calculate the accurate azimuth angle value corresponding to each frame within the large frame according to the azimuth resolution and the azimuth angle estimation value corresponding to the large frame, and calculate the accurate azimuth angle value corresponding to the large frame; Step S4: Divide the echo signal processed in Step S1 according to the accurate azimuth angle value corresponding to the large frame to obtain the data corresponding to the large frame, and sequentially perform range interpolation and azimuth interpolation on the data of each large frame; Step S5: Sequentially perform data extraction and two-dimensional fast Fourier transform on the data of each frame in the large frame after interpolation processing to obtain the imaging result in the large frame coordinate system; Step S6: Perform geometric distortion correction and coordinate system rotation correction on the imaging result of Step S5 to obtain the video SAR imaging result.

2. The large squint time-varying parameter video SAR imaging method according to claim 1, wherein, In the time-varying parameter system, the carrier frequency and chirp rate of the radar platform are related to the platform position at the azimuth sampling moment, expressed as: where \(t\) represents the azimuth time, \(f\) c0 and \(\gamma_0\) respectively represent the initial transmit signal carrier frequency and the chirp rate of the chirp signal, \(f\) c and \(\gamma\) respectively represent the carrier frequency and the chirp rate varying with the azimuth time, \(\beta(t)\) represents the instantaneous depression angle from the platform to the scene center at the azimuth sampling moment, and \(\beta_0\) represents the depression angle at the synthetic aperture center moment.

3. The method for large squint time-varying parameter video SAR imaging according to claim 1, wherein The azimuth wavenumber width loss rate P in the step S2 aziloss is obtained by the following formula: Among them, K xa is the original azimuth wavenumber width, and K xb is the azimuth wavenumber width used for frame imaging at the edge of the large frame.

4. The large squint time-varying parameter video SAR imaging method according to claim 3, wherein The method for calculating the azimuth angle estimation value corresponding to the large frame in Step S2 is as follows: Continuously adjust the azimuth angle of the large frame to find the loss rate P of the azimuth wavenumber width aziloss The azimuth angle of the large frame that is closest to and less than the azimuth wavenumber width loss rate threshold P aziindex At this time, the azimuth angle of the large frame is the azimuth angle estimation value corresponding to the final large frame 5. The method for large squint time-varying parameter video SAR imaging according to claim 1, wherein In Step S3, the accurate azimuth angle value corresponding to the large frame is obtained by accumulating the accurate azimuth angle values corresponding to each frame within the large frame.

Images