A method, device and storage medium for defocusing moving target images

Abstract

The present invention relates to the field of image processing technology, and in particular to a method, device and storage medium for defocusing a moving target image. The method comprises: obtaining an image to be processed; the image to be processed is a moving target image obtained by a synthetic aperture radar; performing azimuth offset correction on the image to be processed to obtain an offset-free image; determining an iterative parameter involved in an iterative equation of a compensation phase matrix; the initial value of the iterative parameter is related to the offset-free image; iterating the iterative parameter using a BFGS algorithm to solve the iterative equation to obtain a target compensation phase matrix; and performing phase error compensation on the offset-free image using the target compensation phase matrix to obtain a defocus-free image. The method proposed in the present invention can effectively improve the blur and defocus problem of moving target images and improve image quality.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method, device and storage medium for defocusing a moving target image. Background Art

[0002] Synthetic Aperture Radar (SAR) is an active microwave imaging sensor that can obtain rich target details and has high resolution in harsh environments. At present, the technology of high-resolution imaging of stationary targets by spaceborne SAR systems is very mature. However, for moving targets, the moving targets themselves have azimuth velocity and range velocity, and there will also be swings and oscillations with different degrees of freedom. Therefore, a moving target is a non-cooperative target with complex compound motion. If the imaging method of a stationary target is used to image a moving target, it will cause a mismatch in Doppler parameters, resulting in blurred defocusing and azimuth offset of the target in the image, which will affect the recognition of the moving target and the extraction of information.

[0003] At present, some researchers have proposed synthetic aperture radar moving target image processing methods, but the images processed by existing methods still have problems such as blur, defocus, and poor imaging quality.

[0004] Therefore, there is an urgent need for a moving target image defocusing processing method to solve the above technical problems. Summary of the invention

[0005] The embodiments of the present invention provide a moving target image defocusing processing method, device and storage medium, which can improve the blur and defocus problem of the moving target image and enhance the image quality.

[0006] In a first aspect, an embodiment of the present invention provides a method for defocusing a moving target image, comprising:

[0007] Acquire an image to be processed; the image to be processed is a moving target image obtained by a synthetic aperture radar;

[0008] Performing azimuth offset correction on the image to be processed to obtain an offset-free image;

[0009] Determining an iteration parameter involved in an iteration equation of a compensation phase matrix; an initial value of the iteration parameter is related to the non-offset image;

[0010] Iterating the iterative parameters using a BFGS algorithm to solve the iterative equation to obtain a target compensation phase matrix;

[0011] The target compensation phase matrix is ​​used to perform phase error compensation on the non-offset image to obtain a non-defocused image.

[0012] In one possible design, the iterative equation includes:

[0013] θ (i+1) (h, k) = θ (i) (h,k)+α a (i) ·d a (i) +α r (i) ·d r (i)

[0014] In the formula, the value range of i is an integer not less than 0; θ (i) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i-th iteration calculation, θ (i+1) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i+1th iteration calculation; α a (i) is the optimal step length in the azimuth direction during the i-th iteration calculation, α r (i) The optimal step length for the distance in the i-th iteration calculation; d a (i) is the azimuth iteration direction during the i-th iteration calculation, d r (i) The distance is in the iteration direction when calculating the i-th iteration;

[0015] The iterative parameters involved in the iterative equation for determining the compensation phase matrix include:

[0016] Determining parameters related to the iteration direction according to a calculation method of the iteration direction; the calculation method of the iteration direction includes: calculating the iteration direction using the product of an approximate Hessian matrix and a sharpness gradient;

[0017] Determining parameters related to the sharpness gradient according to the calculation method of the sharpness gradient; the calculation method of the sharpness gradient includes: determining the image background intensity of the image according to the image parameters of the image, determining the image sharpness of the image according to the image background intensity, and calculating the image sharpness gradient according to the image sharpness and the image background intensity;

[0018] The compensated phase matrix, the approximate Hessian matrix in the range direction, the approximate Hessian matrix in the azimuth direction, the image background intensity, the image sharpness and the image sharpness gradient are determined as iteration parameters.

[0019] In a possible design, the iterative parameters are iterated using a BFGS algorithm to solve the iterative equation to obtain a target compensation phase matrix, and the non-offset image is compensated for phase error using the target compensation phase matrix to obtain a non-defocused image, including:

[0020] S1, determine the initial value of each iteration parameter, and use the initial value as the iteration value of the iteration parameter in the current iteration;

[0021] S2, determining the optimal step length in the range and the optimal step length in the azimuth of the current iteration, and substituting the optimal step length in the range and the optimal step length in the azimuth of the current iteration and the iteration value of the iteration parameter in the current iteration into the iterative equation to calculate the compensation phase matrix after the current iteration;

[0022] S3, performing phase compensation on the non-offset image using the compensation phase matrix after the current iteration to obtain an image to be confirmed after the current iteration;

[0023] S4, determining the image sharpness of the image to be confirmed after the current iteration, and calculating the difference between the image sharpness of the image to be confirmed after the current iteration and the image sharpness of the image to be confirmed after the previous iteration; judging whether the difference is not greater than a preset threshold, if so, executing S6; if not, executing S5;

[0024] S5, calculating the iteration value of the iteration parameter in the next iteration by using the iteration value of the iteration parameter after the current iteration, and executing S2 by using the iteration value of the iteration parameter in the next iteration as the iteration value of the iteration parameter in the current iteration, so as to perform the next iteration;

[0025] S6, stop iteration, and determine the compensation phase matrix after the current iteration as the target compensation phase matrix, and determine the image to be confirmed after the current iteration as the defocus-free image.

[0026] In a possible design, the calculation method of the iteration value of the approximate Hessian matrix in the iteration parameter in the next iteration includes:

[0027] Determine a sharpness gradient difference value of a current iteration, where the sharpness gradient difference value is a difference between a sharpness gradient value of a next iteration and a sharpness gradient value of a current iteration;

[0028] Determine an operator for a current iteration, where the operator is the product of an iteration direction for the current iteration and an optimal step length for the current iteration;

[0029] The iteration value of the approximate Hessian matrix in the iteration parameters in the next iteration is determined according to the sharpness gradient difference of the current iteration, the operator of the current iteration and the approximate Hessian matrix of the current iteration.

[0030] In a possible design, the iterative equation of the approximate Hessian matrix is:

[0031]

[0032] In the formula, the value range of i is an integer not less than 0; B (i) is the approximate Hessian matrix for the i-th iteration, B (i +1) is the approximate Hessian matrix for the i+1th iteration; y (i) is the sharpness gradient difference calculated at the i-th iteration, δ (i) is the operator used in the i-th iteration calculation.

[0033] In a possible design, performing azimuth offset correction on the image to be processed to obtain an offset-free image includes:

[0034] Determining the phase angle of the autocorrelation function of the radar echo signal;

[0035] Calculating an estimated value of the Doppler center offset frequency based on a phase angle of the autocorrelation function and a relationship between the autocorrelation function and a power spectrum function;

[0036] Determining a final Doppler center offset frequency based on the estimated value of the Doppler center offset frequency;

[0037] The image to be processed is subjected to azimuth offset correction based on the Doppler center offset frequency to obtain an offset-free image.

[0038] In a possible design, after performing phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image, the method further includes:

[0039] The non-defocused image is subjected to sidelobe suppression processing based on a spatial apodization method to obtain an image without sidelobe effects.

[0040] In a second aspect, an embodiment of the present invention further provides a moving target image defocusing processing device, comprising:

[0041] An acquisition module is used to acquire an image to be processed; the image to be processed is an image of a moving target obtained by a synthetic aperture radar;

[0042] A correction module, used for performing azimuth offset correction on the image to be processed to obtain a first image without offset;

[0043] A determination module, used to determine an iteration parameter involved in an iteration equation of a compensation phase matrix; an initial value of the iteration parameter is related to the non-offset image;

[0044] A solution module, used for iterating the iteration parameters using a BFGS algorithm to solve the iteration equation to obtain a target compensation phase matrix;

[0045] A compensation module is used to perform phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image.

[0046] In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0047] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0048] The embodiment of the present invention provides a defocusing processing method, device and storage medium for a moving target image. The processing method is for a moving target image obtained by a synthetic aperture radar. Due to the movement of the moving target, the obtained image will have offset and blurred defocusing problems. Therefore, the method first performs azimuth offset correction on the image to be processed to obtain an offset-free image; then phase compensation is performed on the offset-free image. Since the phase error of the moving target is in the form of a high-order error, the present invention uses the BFGS algorithm to iterate the iterative parameters involved in the iterative equation of the compensation phase matrix to solve the iterative equation to obtain the target compensation phase matrix; using the BFGS algorithm for iterative calculation, a two-dimensional compensation function in the two-dimensional frequency domain can be searched, and the target compensation phase matrix calculated in this way can not only compensate for one-dimensional motion errors, but also be extended to compensate for two-dimensional coupling errors in the moving target image, thereby achieving more accurate phase error compensation. Therefore, the method proposed in the present invention can effectively improve the blurring and defocusing problem of the moving target image and improve the image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 is a flow chart of a moving target image defocusing processing method provided by one embodiment of the present invention;

[0051] Figure 2is a flow chart of a moving target image defocusing processing method provided by another embodiment of the present invention;

[0052] Figure 3 is a single-point moving target image before processing provided by an embodiment of the present invention;

[0053] Figure 4 An embodiment of the present invention provides a method of the present invention for Figure 3 The processed image;

[0054] Figure 5 An embodiment of the present invention provides a method of the present invention for Figure 3 Azimuthal cross-sections before and after treatment;

[0055] Figure 6 An embodiment of the present invention provides a method of the present invention for Figure 3 Range profiles before and after treatment;

[0056] Figure 7 is a real SAR image before processing provided by an embodiment of the present invention;

[0057] Figure 8 An embodiment of the present invention provides a method of the present invention for Figure 7 The processed image;

[0058] Fig. 9 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention;

[0059] Fig.10 It is a structural diagram of a moving target image defocusing processing device provided by one embodiment of the present invention;

[0060] Fig.11 It is a structural diagram of a moving target image defocusing processing device provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0062] As mentioned above, images processed by existing methods still have problems such as blur, defocus, and poor imaging quality.

[0063] Based on the above problems, the inventors found that this is because the phase error generated by the moving target image is a high-order error, and the existing methods can only compensate for low-order errors. Therefore, the inventors proposed that the BFGS algorithm can be used to solve the iterative equation to obtain the target compensation phase matrix. This matrix can not only compensate for one-dimensional motion errors, but can also be extended to compensate for two-dimensional coupling errors in moving target images, thereby achieving more accurate phase error compensation for moving target images to obtain high-quality images without blur or defocus.

[0064] The specific implementation of the above concept is described below.

[0065] Please refer to Figure 1 The embodiment of the present invention provides a method for defocusing a moving target image, the method comprising:

[0066] Step 100, obtaining an image to be processed; the image to be processed is an image of a moving target obtained by a synthetic aperture radar;

[0067] Step 102, performing azimuth offset correction on the image to be processed to obtain an offset-free image;

[0068] Step 104, determining an iteration parameter involved in an iteration equation of the compensation phase matrix; an initial value of the iteration parameter is related to the non-offset image;

[0069] Step 106, iterating the iteration parameters using the BFGS algorithm to solve the iteration equation to obtain the target compensation phase matrix;

[0070] Step 108 , using the target compensation phase matrix to perform phase error compensation on the non-offset image to obtain a non-defocused image.

[0071] The processing object of this embodiment is the moving target image obtained by the synthetic aperture radar. Due to the movement of the moving target, the obtained image will have offset and blur defocus problems. Therefore, the embodiment first performs azimuth offset correction on the image to be processed to obtain an offset-free image; then phase compensation is performed on the offset-free image. Since the phase error of the moving target is in the form of a high-order error, this embodiment uses the BFGS algorithm to iterate the iterative parameters involved in the iterative equation of the compensation phase matrix to solve the iterative equation to obtain the target compensation phase matrix; using the BFGS algorithm for iterative calculation, a two-dimensional compensation function in the two-dimensional frequency domain can be searched, and the target compensation phase matrix calculated in this way can not only compensate for the one-dimensional motion error, but also be extended to compensate for the two-dimensional coupling error in the moving target image, thereby realizing more accurate phase error compensation for the moving target image to obtain a high-quality picture without blur and defocus.

[0072] In order to better understand the solution, before describing the specific execution methods of each step, it is necessary to explain the mechanism of phase error generated by the moving target image. The following is an example of a ship target:

[0073] First, a linear frequency modulation signal is used in the synthetic aperture radar system. This signal can obtain a uniform signal bandwidth for transmission. Generally speaking, the raw data after imaging can be expressed as:

[0074]

[0075] Among them, σ A represents the backscatter coefficient of the moving target, W a (t) represents the two-way antenna radiation pattern, c represents the speed of light, r represents the slant range corresponding to the center moment in azimuth, λ represents the wavelength of synthetic aperture radar, t represents the slow time in azimuth, and τ represents the fast time in range.

[0076] For a general stationary target, the target slant range r 0 (t; r) can be expressed as:

[0077]

[0078] Among them, x 0 Indicates the distance difference between the target and the radar along the radar range direction, y 0 It indicates the distance difference between the target and the radar along the radar azimuth direction, H indicates the radar flight altitude, V vel Indicates radar flight speed.

[0079] Since the slant range r(t; r) of the ship target is different from the slant range r of the traditional stationary target 0 The different changing rules of (t; r) will cause the two-way distance history to be different. It can be assumed that the motion model of the ship is a motion model that combines periodic heaving motion and linear variable speed motion. Assuming that the heaving motion of the ship is a simple harmonic motion with the coordinate origin as the equilibrium origin and the OZ axis as the axis in the ship coordinate system, combined with the linear variable speed motion of the ship, the motion model of the ship can be expressed as

[0080]

[0081] Among them, X r (t), Y r (t), Z r (t) represents the motion offset transformed over time on the x, y, and z axes of the radar coordinate system. c ,y c , z c is the position of the ship in the radar coordinate system, v x is the speed of movement along the distance direction, ax , is the first and second order acceleration of motion along the distance direction, v y is the velocity along the azimuth direction, a y , are the first and second order accelerations of motion along the azimuth direction, ω z is the angular frequency of the undulating motion, A z is the amplitude of the undulating motion.

[0082] Therefore, the slant range expression of the ship can be obtained as shown in formula (4), and the expression after Taylor expansion of this formula is shown in formula (5):

[0083]

[0084]

[0085] In formula (4) and formula (5), the meaning of each symbol is the same as the meaning of each symbol in formula (3).

[0086] When the synthetic aperture time of the radar is short, that is, when the synthetic aperture time is much smaller than the period of the heaving motion, sin(ω z t) is expanded by Maclaurin, and the polynomial terms within the third order are taken. The obtained phase error expression can be used as the phase error model of high-resolution SAR ship refocusing, as shown in formula (6).

[0087]

[0088] The meanings of the symbols in formula (6) are the same as those in formula (3).

[0089] It can be seen from equation (6) that in high-resolution images, the phase error caused by the ship target takes the form of a high-order error.

[0090] Therefore, in order to estimate and compensate for the high-order errors caused by target motion and part of the coupling errors caused by range migration, and to achieve fine focusing of the ship image, the present invention proposes to use the BFGS algorithm to solve the iterative equation of the compensation phase matrix to obtain the target compensation phase matrix. The target compensation phase matrix can not only compensate for the one-dimensional motion error, but also be extended to compensate for the two-dimensional coupling error in the moving target image, thereby achieving more accurate phase error compensation.

[0091] That is to say, from the perspective of image domain, the method of the present invention regards the focusing problem of synthetic aperture radar ship moving target image as an optimization problem in image domain, and utilizes the BFGS algorithm, a more efficient quasi-Newton method, to avoid the tedious calculation of the Hessian matrix, and instead uses a more concise way to iteratively calculate the approximate Hessian matrix. Therefore, it has the characteristics of fast convergence speed, low computational complexity and high accuracy.

[0092] Described below Figure 1 How the various steps are performed.

[0093] First, with respect to step 100, an image to be processed is obtained; the image to be processed is an image of a moving target obtained by a synthetic aperture radar.

[0094] In this step, the moving target can be a moving ship, car, airplane, or a moving person or animal, etc., as long as the image obtained by the radar is offset and blurred and defocused due to the target being in a non-stationary state when the synthetic aperture radar detects the target.

[0095] Then, with respect to step 102, the image to be processed is corrected for azimuth offset to obtain an image without offset.

[0096] In this embodiment, the image offset of the image to be processed mainly occurs in the azimuth direction, and the offset in the range direction can be ignored. Therefore, the image offset correction only needs to consider the correction in the azimuth direction.

[0097] In some implementations, performing azimuth offset correction on the image to be processed to obtain an offset-free image includes:

[0098] Determine the phase angle of the autocorrelation function of the radar echo signal;

[0099] Based on the phase angle of the autocorrelation function and the relationship between the autocorrelation function and the power spectrum function, the estimated value of the Doppler center shift frequency is calculated;

[0100] Determining a final Doppler center offset frequency based on the estimated value of the Doppler center offset frequency;

[0101] The image to be processed is corrected for azimuth offset based on the Doppler center offset frequency to obtain an offset-free image.

[0102] In this embodiment, the estimated value of the Doppler center offset frequency is determined mainly based on the correlation function method, because the correlation function method can estimate a more accurate Doppler center offset frequency while being relatively simple and low in calculation. The correlation function method first estimates the estimated value of the Doppler offset frequency by measuring the phase angle of the autocorrelation function of the echo signal, and then performs a defuzzification process on the estimated value to obtain a more accurate Doppler center offset frequency.

[0103] Specifically, in the correlation function method, the signal expression with the Doppler center offset frequency can be simplified to be regarded as an expression s affected only by the slow time t h (t), the signal expression can be simplified as:

[0104]

[0105] Among them, s 0 (t) represents the target signal without Doppler center frequency shift in the static state, f dc Indicates the Doppler center offset frequency.

[0106] According to the relationship between the autocorrelation function and the power spectrum function, the autocorrelation function R can be derived. h (τ t ) expression:

[0107]

[0108] Among them, τ t represents the azimuth delay, R 0 (τ t ) represents the autocorrelation function of the stationary target signal, so the Doppler center offset frequency f can be obtained from equation (8): dc and the autocorrelation function R h (τ t ) relationship.

[0109] Assume T PRT Represents the pulse repetition period. Since the signal echo received by the radar in the actual detection process is discrete, that is, τ t = k·T PRT , k is an integer not less than 1. When k = 1, the estimated value f′ of the Doppler center offset frequency can be obtained dc for:

[0110]

[0111] In the formula, arg{R h (T PRT )} is the phase angle of the autocorrelation function of the measured echo signal.

[0112] When f′ dc T PRT When <1, formula (9) can obtain a more accurate estimation result, which is the final Doppler center offset frequency.

[0113] When f′ dc T PRT >1, the estimated f′ dc There may be ambiguity, and f′ needs to be adjusteddc Deblurring is performed to obtain the final Doppler center offset frequency, as shown in equation (10):

[0114]

[0115] In the formula, f e is the rough but unambiguous Doppler center frequency, and PRF is the pulse repetition frequency.

[0116] After determining the final Doppler center offset frequency, the first-order offset phase of the image to be processed can be calculated according to the frequency. The image to be processed can be compensated using the first-order offset phase to obtain an offset-free image. The specific compensation process is as follows:

[0117] First, assuming that t represents the slow time in azimuth, τ represents the fast time in range, and f represents the frequency in azimuth, the image data s(t; τ) to be processed is subjected to azimuth Fourier transform, and the image is converted to the range-Doppler domain to obtain the range-Doppler domain data s(f; τ).

[0118] Then, assuming that h represents the discrete points of azimuth frequency, k represents the discrete points of range frequency, m represents the discrete points of azimuth time, and n represents the discrete points of range time, the discrete form of the range-Doppler domain signal can be expressed as s 0 (h, n), for frequency domain data s 0 (h, n) is subjected to first-order phase compensation, as shown in equation (11), to obtain the offset-free range-Doppler domain image data s(h, n):

[0119]

[0120] By performing an inverse Fourier transform in azimuth on the non-offset range-Doppler domain image data s(h, n), the data can be converted from the frequency domain to the time domain to obtain the non-offset image data s(m, n), that is, the non-offset image.

[0121] It should be noted that in the present invention, there is a corresponding relationship between the image and the image data s(m, n). This is because the image obtained by the synthetic aperture radar is a complex image, and the complex image in the computer is composed of several image data s(m, n). The computer processes the image through the image data s(m, n), and after processing, it is displayed in the form of an image through a display.

[0122] Next, for step 104, the iteration parameters involved in the iteration equation of the compensation phase matrix are determined; the initial values ​​of the iteration parameters are related to the non-offset image.

[0123] In this step, the iteration parameters are indirect parameters, through which the direct parameters of the iterative equation calculation can be determined. In each iterative calculation, the iteration parameters need to be re-determined until the iterative calculation reaches the preset termination condition.

[0124] Generally speaking, image sharpness is positively correlated with image quality. Therefore, in some implementations, image sharpness can be used as an indicator to measure image quality, that is, image sharpness can be used as a cost function of an iterative equation. Then, the iterative equation is:

[0125] θ (i+1) (h, k) = θ (i) (h,k)+α a (i) ·d a (i) +α r (i) ·d r (i) (12)

[0126] In the formula, the value range of i is an integer not less than 0; θ (i) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i-th iteration calculation, θ (i+1) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i+1th iteration calculation; α a (i) is the optimal step length in the azimuth direction during the i-th iteration calculation, α r (i) The optimal step length for the distance in the i-th iteration calculation; d a (i) is the azimuth iteration direction during the i-th iteration calculation, d r (i) The distance is in the iteration direction when calculating the i-th iteration.

[0127] In some embodiments, determining the iteration parameters involved in the iteration equation of the compensation phase matrix includes:

[0128] Step A1, determining parameters related to the iteration direction according to a calculation method of the iteration direction; the calculation method of the iteration direction includes: calculating the iteration direction using the product of an approximate Hessian matrix and a sharpness gradient;

[0129] Step A2, determining parameters related to the sharpness gradient according to a calculation method of the sharpness gradient; the calculation method of the sharpness gradient includes: determining an image background intensity of the image according to an image parameter of the image, determining the image sharpness of the image according to the image background intensity, and calculating the image sharpness gradient according to the image sharpness and the image background intensity;

[0130] In step A3, the compensation phase matrix, the approximate Hessian matrix in the range direction, the approximate Hessian matrix in the azimuth direction, the image background intensity, the image sharpness and the image sharpness gradient are determined as iteration parameters.

[0131] In this embodiment, for step A1, the calculation formula for the iteration direction is:

[0132]

[0133] Where, d (i) is the iteration direction of the i-th iteration calculation, B (i) is the approximate Hessian matrix of the i-th iteration, S x (i) is the image sharpness calculated at the i-th iteration, is the sharpness gradient calculated at the i-th iteration.

[0134] For step A2, the calculation formula of the sharpness gradient is:

[0135]

[0136] Where h represents the discrete point of azimuth frequency, k represents the discrete point of range frequency, s(h, k) represents the ideal signal without offset error, θ(h, k) represents the phase error at the hth azimuth frequency point and the kth range frequency point, M represents the total number of sample points in azimuth, N represents the total number of sample points in range, m represents the discrete point of azimuth time, n represents the discrete point of range time, s(m, n) represents the complex value of the image at m in azimuth and n in range, b represents, S x Indicates the sharpness of the image.

[0137] Among them, the image sharpness S x The expression is:

[0138]

[0139] The expression of image background intensity b is:

[0140]

[0141] The meanings of the symbols in formula (15) and formula (16) are the same as those in formula (14).

[0142] According to the calculation formulas of step A1 and step A2, the indirect iterative parameters of the iterative equation are: compensation phase matrix, approximate Hessian matrix in range, approximate Hessian matrix in azimuth, image background intensity, image sharpness and image sharpness gradient. According to these indirect parameters, the direct parameters of the iterative equation (including the compensation phase matrix, iteration direction and optimal iteration step length of the last iteration calculation) can be determined accordingly.

[0143] Finally, for step 106 and step 108, the BFGS algorithm is used to iterate the iteration parameters to solve the iteration equation to obtain the target compensation phase matrix; the target compensation phase matrix is ​​used to compensate the phase error of the non-offset image to obtain a non-defocused image.

[0144] In these two steps, the use of the BFGS algorithm can simplify the calculation process of the equation, especially the calculation process of the approximate Hessian matrix. On the other hand, the BFGS algorithm can search for a two-dimensional compensation function in the two-dimensional frequency domain. The target compensation phase matrix calculated in this way can not only compensate for the one-dimensional motion error, but also be extended to compensate for the two-dimensional coupling error in the moving target image, thereby achieving more accurate phase error compensation, thereby effectively improving the blur and defocus problem of the moving target image and improving the image quality.

[0145] In some implementations, the iteration parameters are iterated using a BFGS algorithm to solve the iteration equation to obtain a target compensation phase matrix, and the target compensation phase matrix is ​​used to perform phase error compensation on the non-offset image to obtain a non-defocused image, including:

[0146] S1, determine the initial value of each iteration parameter, and use the initial value as the iteration value of the iteration parameter in the current iteration;

[0147] S2, determining the optimal step length in the range and the optimal step length in the azimuth of the current iteration, and substituting the optimal step length in the range and the optimal step length in the azimuth of the current iteration and the iteration value of the iteration parameter in the current iteration into the iteration equation, and calculating the compensation phase matrix after the current iteration;

[0148] S3, performing phase compensation on the non-offset image using the compensation phase matrix after the current iteration to obtain an image to be confirmed after the current iteration;

[0149] S4, determining the image sharpness of the image to be confirmed after the current iteration, and calculating the difference between the image sharpness of the image to be confirmed after the current iteration and the image sharpness of the image to be confirmed after the previous iteration; judging whether the difference is not greater than a preset threshold, if so, executing S6; if not, executing S5;

[0150] S5, calculating the iteration value of the iteration parameter in the next iteration by using the iteration value of the iteration parameter after the current iteration, and executing S2 by using the iteration value of the iteration parameter in the next iteration as the iteration value of the iteration parameter in the current iteration, so as to perform the next iteration;

[0151] S6, stop the iteration, and determine the compensation phase matrix after the current iteration as the target compensation phase matrix, and determine the image to be confirmed after the current iteration as the image without defocus.

[0152] In S1, the initial value of the compensation phase matrix is ​​a zero matrix, the initial value of the approximate Hessian matrix is ​​a unit matrix, and the initial image background intensity, initial image sharpness and initial image sharpness gradient are all calculated through the image data corresponding to the unbiased image determined in step 102, that is, the image data s(m,n) corresponding to the unbiased image is substituted into formula (16) to calculate the initial image background intensity, the initial image background intensity and the image data s(m,n) corresponding to the unbiased image are substituted into formula (15) to calculate the initial image sharpness, and the initial image background intensity and the image data s(m,n) corresponding to the unbiased image are substituted into formula (14) to calculate the initial image sharpness gradient.

[0153] In S2, the optimal step size is searched by a line search algorithm. During the search process, the step size is continuously reduced until the change between the step size of the current search and the step size of the previous search is less than a certain threshold.

[0154] It should be noted that the optimal step size and iteration direction include values ​​in both the range and azimuth directions, and the user can input the values ​​in the corresponding directions according to actual needs.

[0155] In S3, the image to be confirmed may be the final image without defocus or a non-final image, which needs to be determined by S4. In S4, the difference between the image sharpness of the image to be confirmed after the current iteration and the image sharpness of the image to be confirmed after the previous iteration is used as the judgment standard for whether to end the calculation. If the end standard is not met, the iteration parameters of the next iteration calculation are calculated. The calculation process is:

[0156] The image data corresponding to the image to be confirmed in S3 is substituted into formula (16) to calculate the image background intensity of the next iterative calculation; the image background intensity of the next iterative calculation and the image data corresponding to the image to be confirmed in S3 are substituted into formula (15) to calculate the image sharpness of the next iterative calculation; the image background intensity of the next iterative calculation and the image data corresponding to the image to be confirmed in S3 are substituted into formula (14) to calculate the image sharpness gradient of the next iterative calculation.

[0157] In some embodiments, the calculation method of the iteration value of the approximate Hessian matrix in the iteration parameter at the next iteration includes:

[0158] Determine the sharpness gradient difference of the current iteration, where the sharpness gradient difference is the difference between the sharpness gradient value of the next iteration and the sharpness gradient value of the current iteration;

[0159] Determine the operator of the current iteration, where the operator is the product of the iteration direction of the current iteration and the optimal step size of the current iteration;

[0160] The iteration value of the approximate Hessian matrix in the iteration parameters at the next iteration is determined according to the sharpness gradient difference of the current iteration, the operator of the current iteration and the approximate Hessian matrix of the current iteration.

[0161] In some embodiments, the iterative equation for approximating the Hessian matrix is:

[0162]

[0163] In the formula, the value range of i is an integer not less than 0; B (i) is the approximate Hessian matrix for the i-th iteration, B (i +1) is the approximate Hessian matrix for the i+1th iteration; y (i) is the sharpness gradient difference calculated at the i-th iteration, δ (i) is the operator used in the i-th iteration calculation.

[0164] It should be noted that the approximate Hessian matrix, sharpness gradient difference and operator all include range and azimuth directions, and users can use the values ​​of the corresponding directions according to actual calculations.

[0165] In this embodiment, the sharpness gradient difference y (i) The calculation formula is:

[0166]

[0167] in, is the sharpness gradient calculated at the i+1th iteration, is the sharpness gradient calculated at the i-th iteration;

[0168] Operator δ (i) The calculation formula is:

[0169] δ (i) =α (i) d (i) (19)

[0170] Among them, α (i) is the optimal step size for the i-th iteration calculation, d (i) Iteration direction for the i-th iteration calculation.

[0171] In this embodiment, the BFGS algorithm is used, so that the calculation process of the approximate Hessian matrix is ​​simplified, thereby greatly shortening the iterative calculation time.

[0172] In addition, in some implementations, with respect to step 108, using the target compensation phase matrix to perform phase error compensation on the non-offset image to obtain a non-defocused image specifically includes:

[0173] Perform azimuth Fourier transform and distance Fourier transform on the data s(m, n) corresponding to the non-offset image, convert the image data into a two-dimensional frequency domain, and obtain two-dimensional frequency domain data s(h, k);

[0174] According to the target compensation phase matrix θ(h, k), phase compensation is performed on the two-dimensional frequency domain data s(h, k) in the two-dimensional frequency domain to obtain the compensated image data s c (h, k), as shown in formula (20):

[0175] s c (h, k) = s(h, k)e jθ(h，k) (20)

[0176] The compensated image data s c (h, k) performs inverse Fourier transform in azimuth and distance directions to obtain the compensated defocus-free image, and the image data corresponding to the defocus-free image is recorded as s c (m, n).

[0177] In some implementations, after step 108, the method further includes step 110: performing sidelobe suppression processing on the non-defocused image based on a spatial apodization method to obtain an image without sidelobe effects.

[0178] Specifically, for the compensated defocus-free image data s c (m, n) Spatially Variant Apodization (SVA) is used to suppress the sidelobe to solve the cross sidelobe effect caused by the backscattering of some structures of the moving target.

[0179] The time domain weighted expression of the spatial apodization method is:

[0180]

[0181] Where s(n) is one-dimensional data, g(n) is one-dimensional data processed by spatial apodization method, α(n) is weighting coefficient, and the calculation formula of weighting coefficient is shown in formula (22):

[0182]

[0183] By using formulas (21) and (22) to perform sidelobe suppression processing in the range direction and azimuth direction respectively, an image without sidelobe effects can be obtained.

[0184] In order to illustrate the effectiveness of the method of the present invention, the inventor first verifies the method by taking a simulation image as an example. The simulation calculation takes a single-point moving target image as an example, and uses the method of the present invention to process the image.

[0185] like Figure 3As shown in the figure, it is a single point moving target image before processing. Figure 3 It can be seen that the image has obvious blur, defocus and sidelobe effects.

[0186] like Figure 4 As shown in FIG. 1 , a single-point moving target image is processed using the method of the present invention. Figure 4 It can be seen that the defocusing and sidelobe effects of the image are effectively controlled.

[0187] in addition, Figure 5 and Figure 6 They are the azimuth profile and range profile of a single-point moving target before and after image processing. Figure 5 and Figure 6 It can be seen that before and after processing, the azimuth resolution is improved from 7.132m to 1.056m, the azimuth peak sidelobe ratio is improved from -9.005dB to -10.774dB; the range peak sidelobe ratio is improved from -8.845dB to -10.032dB, which shows that the method of the present invention has good effectiveness in simulating point targets.

[0188] In order to further demonstrate the effectiveness of the method of the present invention, the inventors also verified the method of the present invention using a real SAR image as an example.

[0189] Figure 7 and Figure 8 They are the images before and after processing, Figure 7 and Figure 8 It can be seen that after being treated by the method of the present invention, Figure 7 The defocus condition is significantly improved, and the image contrast increases from 6.2501 to 12.7201, effectively improving the image indicators.

[0190] From the above analysis, it can be seen that the method of the present invention can obtain better processing results in both simulated images and real images.

[0191] like Fig. 9 , Fig.10 As shown, an embodiment of the present invention provides a moving target image defocusing processing device. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Fig. 9 FIG. 1 is a hardware architecture diagram of a computing device in which a moving target image defocusing processing device provided by an embodiment of the present invention is located, except that Fig. 9 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Fig.10As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a moving target image defocusing processing device, including:

[0192] The acquisition module 1000 is used to acquire an image to be processed; the image to be processed is an image of a moving target obtained by a synthetic aperture radar;

[0193] A correction module 1002 is used to perform azimuth offset correction on the image to be processed to obtain a first image without offset;

[0194] A determination module 1004 is used to determine an iteration parameter involved in an iteration equation of a compensation phase matrix; an initial value of the iteration parameter is related to the non-offset image;

[0195] A solution module 1006 is used to iterate the iteration parameters using the BFGS algorithm to solve the iteration equation to obtain the target compensation phase matrix;

[0196] The compensation module 1008 is used to perform phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image.

[0197] In an embodiment of the present invention, the acquisition module 1000 can be used to execute step 100 in the above method embodiment, the correction module 1002 can be used to execute step 102 in the above method embodiment, the determination module 1004 can be used to execute step 104 in the above method embodiment, the solution module 1006 can be used to execute step 106 in the above method embodiment, and the compensation module 1008 can be used to execute step 108 in the above method embodiment.

[0198] In some implementations, the correction module 1002 is configured to perform:

[0199] Determine the phase angle of the autocorrelation function of the radar echo signal;

[0200] Based on the phase angle of the autocorrelation function and the relationship between the autocorrelation function and the power spectrum function, the estimated value of the Doppler center shift frequency is calculated;

[0201] Determining a final Doppler center offset frequency based on the estimated value of the Doppler center offset frequency;

[0202] The image to be processed is corrected for azimuth offset based on the Doppler center offset frequency to obtain an offset-free image.

[0203] In some embodiments, the iterative equation includes:

[0204] θ (i+1) (h, k) = θ (i) (h,k)+αa (i) ·d a (i) +α r (i) ·d r (i)

[0205] In the formula, the value range of i is an integer not less than 0; θ (i) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i-th iteration calculation, θ (i+1) (h, k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i+1th iteration calculation; α a (i) is the optimal step length in the azimuth direction during the i-th iteration calculation, α r (i) The optimal step length for the distance in the i-th iteration calculation; d a (i) is the azimuth iteration direction during the i-th iteration calculation, d r (i) The distance is in the iteration direction when calculating the i-th iteration;

[0206] The determination module 1004 is used to execute:

[0207] Determine parameters related to the iteration direction according to a calculation method of the iteration direction; the calculation method of the iteration direction includes: calculating the iteration direction using the product of an approximate Hessian matrix and a sharpness gradient;

[0208] Determining parameters related to the sharpness gradient according to a calculation method of the sharpness gradient; the calculation method of the sharpness gradient includes: determining an image background intensity of the image according to an image parameter of the image, determining an image sharpness of the image according to the image background intensity, and calculating an image sharpness gradient according to the image sharpness and the image background intensity;

[0209] The compensated phase matrix, the approximate Hessian matrix in the range direction, the approximate Hessian matrix in the azimuth direction, the image background intensity, the image sharpness and the image sharpness gradient are determined as iteration parameters.

[0210] In some implementations, the solution module 1006 and the compensation module 1008 are configured to perform:

[0211] S1, determine the initial value of each iteration parameter, and use the initial value as the iteration value of the iteration parameter in the current iteration;

[0212] S2, determining the optimal step length in the range and the optimal step length in the azimuth of the current iteration, and substituting the optimal step length in the range and the optimal step length in the azimuth of the current iteration and the iteration value of the iteration parameter in the current iteration into the iteration equation, and calculating the compensation phase matrix after the current iteration;

[0213] S3, performing phase compensation on the non-offset image using the compensation phase matrix after the current iteration to obtain an image to be confirmed after the current iteration;

[0214] S4, determining the image sharpness of the image to be confirmed after the current iteration, and calculating the difference between the image sharpness of the image to be confirmed after the current iteration and the image sharpness of the image to be confirmed after the previous iteration; judging whether the difference is not greater than a preset threshold, if so, executing S6; if not, executing S5;

[0215] S5, calculating the iteration value of the iteration parameter in the next iteration by using the iteration value of the iteration parameter after the current iteration, and executing S2 by using the iteration value of the iteration parameter in the next iteration as the iteration value of the iteration parameter in the current iteration, so as to perform the next iteration;

[0216] S6, stop the iteration, and determine the compensation phase matrix after the current iteration as the target compensation phase matrix, and determine the image to be confirmed after the current iteration as the image without defocus.

[0217] In some embodiments, the calculation method of the iteration value of the approximate Hessian matrix in the iteration parameter at the next iteration includes:

[0218] Determine the sharpness gradient difference of the current iteration, where the sharpness gradient difference is the difference between the sharpness gradient value of the next iteration and the sharpness gradient value of the current iteration;

[0219] Determine the operator of the current iteration, where the operator is the product of the iteration direction of the current iteration and the optimal step size of the current iteration;

[0220] The iteration value of the approximate Hessian matrix in the iteration parameters at the next iteration is determined according to the sharpness gradient difference of the current iteration, the operator of the current iteration and the approximate Hessian matrix of the current iteration.

[0221] In some embodiments, the iterative equation for approximating the Hessian matrix is:

[0222]

[0223] In the formula, the value range of i is an integer not less than 0; B (i) is the approximate Hessian matrix for the i-th iteration, B (i +1) is the approximate Hessian matrix for the i+1th iteration; y (i)is the sharpness gradient difference calculated at the i-th iteration, δ (i) is the operator used in the i-th iteration calculation.

[0224] like Fig.11 As shown, in some embodiments, the device further includes a suppression module 1010, which is used to execute, after executing the compensation module 1008:

[0225] The sidelobe suppression process of the non-defocused image is performed based on the spatial apodization method to obtain an image without sidelobe effect.

[0226] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a moving target image defocusing processing device. In other embodiments of the present invention, a moving target image defocusing processing device may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0227] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0228] An embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a moving target image defocusing processing method in any embodiment of the present invention is implemented.

[0229] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a moving target image defocusing processing method in any embodiment of the present invention.

[0230] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0231] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0232] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0233] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0234] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0235] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0236] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for defocusing a moving target image, characterized in that: include: Acquire an image to be processed; the image to be processed is a moving target image obtained by a synthetic aperture radar; Performing azimuth offset correction on the image to be processed to obtain an offset-free image; Determining iterative parameters involved in iterative equations for compensating phase matrices; The initial value of the iteration parameter is related to the non-offset image; Iterating the iterative parameters using a BFGS algorithm to solve the iterative equation to obtain a target compensation phase matrix; Performing phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image; The iterative equation includes: i (i+1) (h,k)=θ (i) (h,k)+a a (i) ·d a (i) +a r (i) ·d r (i) In the formula, the value range of i is an integer not less than 0; θ (i) (h,k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i-th iteration calculation, θ (i+1) (h,k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i+1th iteration calculation; α a (i) is the optimal step length in the azimuth direction during the i-th iteration calculation, α r (i) The optimal step length for the distance in the i-th iteration calculation; d a (i) is the azimuth iteration direction during the i-th iteration calculation, d r (i) The distance is in the iteration direction when calculating the i-th iteration; The iterative parameters involved in the iterative equation for determining the compensation phase matrix include: Determining parameters related to the iteration direction according to a calculation method of the iteration direction; the calculation method of the iteration direction includes: calculating the iteration direction using the product of an approximate Hessian matrix and a sharpness gradient; Determining parameters related to the sharpness gradient according to the calculation method of the sharpness gradient; the calculation method of the sharpness gradient includes: determining the image background intensity of the image according to the image parameters of the image, determining the image sharpness of the image according to the image background intensity, and calculating the image sharpness gradient according to the image sharpness and the image background intensity; The compensated phase matrix, the approximate Hessian matrix in the range direction, the approximate Hessian matrix in the azimuth direction, the image background intensity, the image sharpness and the image sharpness gradient are determined as iteration parameters.

2. The method according to claim 1, characterized in that: The iterative parameters are iterated by using the BFGS algorithm to solve the iterative equation to obtain a target compensation phase matrix, and the non-offset image is compensated for phase errors by using the target compensation phase matrix to obtain a non-defocused image, including: S1, determine the initial value of each iteration parameter, and use the initial value as the iteration value of the iteration parameter in the current iteration; S2, determining the optimal step length in the range and the optimal step length in the azimuth of the current iteration, and substituting the optimal step length in the range and the optimal step length in the azimuth of the current iteration and the iteration value of the iteration parameter in the current iteration into the iterative equation to calculate the compensation phase matrix after the current iteration; S3, performing phase compensation on the non-offset image using the compensation phase matrix after the current iteration to obtain an image to be confirmed after the current iteration; S4, determining the image sharpness of the image to be confirmed after the current iteration, and calculating the difference between the image sharpness of the image to be confirmed after the current iteration and the image sharpness of the image to be confirmed after the previous iteration; judging whether the difference is not greater than a preset threshold, if so, executing S6; if not, executing S5; S5, calculating the iteration value of the iteration parameter in the next iteration by using the iteration value of the iteration parameter after the current iteration, and executing S2 by using the iteration value of the iteration parameter in the next iteration as the iteration value of the iteration parameter in the current iteration, so as to perform the next iteration; S6, stop iteration, and determine the compensation phase matrix after the current iteration as the target compensation phase matrix, and determine the image to be confirmed after the current iteration as the defocus-free image.

3. The method according to claim 2, characterized in that The calculation method of the iteration value of the approximate Hessian matrix in the iteration parameter in the next iteration includes: Determine a sharpness gradient difference value of a current iteration, where the sharpness gradient difference value is a difference between a sharpness gradient value of a next iteration and a sharpness gradient value of a current iteration; Determine an operator for a current iteration, where the operator is the product of an iteration direction for the current iteration and an optimal step length for the current iteration; The iteration value of the approximate Hessian matrix in the iteration parameters in the next iteration is determined according to the sharpness gradient difference of the current iteration, the operator of the current iteration and the approximate Hessian matrix of the current iteration.

4. The method according to claim 3, characterized in that The iterative equation of the approximate Hessian matrix is: In the formula, the value range of i is an integer not less than 0; B (i) is the approximate Hessian matrix for the i-th iteration, B (i+1) is the approximate Hessian matrix for the i+1th iteration; y (i) is the sharpness gradient difference calculated at the i-th iteration, δ (i) is the operator used in the i-th iteration calculation.

5. The method according to claim 1, characterized in that The step of performing azimuth offset correction on the image to be processed to obtain an image without offset includes: Determining the phase angle of the autocorrelation function of the radar echo signal; Calculating an estimated value of the Doppler center shift frequency based on a phase angle of the autocorrelation function and a relationship between the autocorrelation function and a power spectrum function; Determining a final Doppler center offset frequency based on the estimated value of the Doppler center offset frequency; The image to be processed is subjected to azimuth offset correction based on the Doppler center offset frequency to obtain an offset-free image.

6. The method according to any one of claims 1 to 5, characterized in that: After performing phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image, the method further includes: The non-defocused image is subjected to sidelobe suppression processing based on a spatial apodization method to obtain an image without sidelobe effects.

7. A moving target image defocusing processing device, characterized in that: include: An acquisition module is used to acquire an image to be processed; the image to be processed is an image of a moving target obtained by a synthetic aperture radar; A correction module, used for performing azimuth offset correction on the image to be processed to obtain a first image without offset; A determination module, used for determining iterative parameters involved in an iterative equation for compensating a phase matrix; The initial value of the iteration parameter is related to the first image without offset; A solution module, used for iterating the iteration parameters using a BFGS algorithm to solve the iteration equation to obtain a target compensation phase matrix; A compensation module, used for performing phase error compensation on the non-offset image using the target compensation phase matrix to obtain a non-defocused image; The iterative equation includes: i (i+1) (h,k)=θ (i) (h,k)+a a (i) ·d a (i) +a r (i) ·d r (i) In the formula, the value range of i is an integer not less than 0; θ (i) (h,k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i-th iteration calculation, θ (i+1) (h,k) is the phase error at the hth azimuth frequency point and the kth distance frequency point in the i+1th iteration calculation; α a (i) is the optimal step length in the azimuth direction during the i-th iteration calculation, α r (i) The optimal step length for the distance in the i-th iteration calculation; d a (i) is the azimuth iteration direction during the i-th iteration calculation, d r (i) The distance is in the iteration direction when calculating the i-th iteration; The iterative parameters involved in the iterative equation for determining the compensation phase matrix include: Determining parameters related to the iteration direction according to a calculation method of the iteration direction; the calculation method of the iteration direction includes: calculating the iteration direction using the product of an approximate Hessian matrix and a sharpness gradient; Determining parameters related to the sharpness gradient according to the calculation method of the sharpness gradient; the calculation method of the sharpness gradient includes: determining the image background intensity of the image according to the image parameters of the image, determining the image sharpness of the image according to the image background intensity, and calculating the image sharpness gradient according to the image sharpness and the image background intensity; The compensated phase matrix, the approximate Hessian matrix in the range direction, the approximate Hessian matrix in the azimuth direction, the image background intensity, the image sharpness and the image sharpness gradient are determined as iteration parameters.

8. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 6.

Images