Strabismus synthetic aperture sonar imaging method based on dynamic slant-range space-variant model

Abstract

The invention discloses a squint synthetic aperture sonar imaging method based on a dynamic slant-range space-variant model, and relates to the technical field of synthetic aperture sonar imaging, and the method comprises the steps: constructing a dynamic slant-range model; inputting the converted data into the dynamic oblique variation model to obtain a residual space-variant distance bending amount, and compensating the space-variant distance bending amount by using a first filter; according to the dynamic slope distance model, expanding the azimuth time offset into an azimuth frequency modulation factor; based on the azimuth frequency modulation factor and the azimuth time offset, designing a second filter by using an improved extended nonlinear frequency modulation scaling algorithm, and filtering the corrected data by using the second filter; and carrying out Fourier transform on the filtered data, compressing the transformed data by using a third filter, and then carrying out inverse Fourier transform to obtain a focused synthetic aperture sonar image. According to the invention, the imaging performance and stability of the imaging algorithm in a low signal-to-noise ratio and strong multipath interference environment can be improved.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic signal processing and synthetic aperture sonar imaging technology, specifically to a slant-look synthetic aperture sonar imaging method based on a dynamic slant range spatial variation model. Background Technology

[0002] Synthetic Aperture Sonar (SAS) is a high-resolution underwater imaging device based on synthetic aperture technology and signal processing techniques. It creates a virtual long aperture through the movement of the sonar platform, and combined with pulse compression technology, it can achieve high-resolution imaging of seabed topography, buried targets, and other objects. SAS systems mainly operate in two modes based on the beam pointing angle: front-view and oblique-view. The oblique-view mode, with its greater observation flexibility and ability to achieve out-of-area imaging, demonstrates significant advantages in fields such as military reconnaissance and seabed resource exploration. Particularly for detecting complex seabed topography and concealed targets, the oblique-view SAS system provides a more comprehensive observation perspective and richer target information.

[0003] In the field of strabismus SAS imaging technology, the Nonlinear Frequency Modulation Scale (NLCS) algorithm and its extension (ENLCS) algorithm are currently mature high-precision imaging methods. These algorithms, by establishing accurate geometric models and performing phase compensation, can theoretically achieve high-resolution imaging under strabismus conditions. However, in the actual marine environment, the echo signals received by the strabismus SAS system are affected by a number of adverse factors: First, a large angle of view leads to increased spatial variability in range migration; second, point targets at different azimuth positions have different Doppler modulation frequencies, i.e., there is a spatial variation problem in azimuth modulation frequency; in addition, traditional fixed geometric models are difficult to accurately describe the slant range variation under a wide mapping zone. When processing strabismus SAS data, the existing ENLCS algorithm usually uses Taylor expansion to approximate the instantaneous slant range of point targets and relies on fixed geometric models such as simple circular models to describe the slant range relationship of different point targets within the same range cell. This method will significantly increase the model approximation error when the sonar platform moves at a high speed, the angle of view is large, or the mapping zone is wide, resulting in a decrease in focusing performance in the azimuth direction (especially in the edge areas of the scene), and the secondary phase error exceeds the allowable range, ultimately affecting the imaging quality. Summary of the Invention

[0004] To address the issue of image edge defocusing caused by insufficient geometric model accuracy in the aforementioned slant-view synthetic aperture sonar imaging algorithm when processing data from large slant angles and high-speed moving platforms, this invention provides a slant-view synthetic aperture sonar imaging method based on a dynamic slant-range spatially variable model. This invention primarily constructs a dynamic slant-range model including a quadratic correction term, significantly improving the accuracy of slant-range calculation. Through spatially variable range migration correction and azimuth frequency modulation factor expansion equalization, it effectively compensates for azimuth spatially variable errors. Ultimately, it achieves uniform focusing across the entire scene, with the secondary phase error strictly controlled within the required imaging threshold, thus solving the defocusing problem at scene edges inherent in traditional methods.

[0005] The technical means employed in this invention are as follows:

[0006] A slant-look synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model includes the following steps: The raw echo data is acquired using a slant-look synthetic aperture sonar. A dynamic slope range model is constructed, which is an improvement on the circle model. The dynamic slope range model is used to correlate the instantaneous slope range of the target point with the slope range of the reference point. The original echo data is preprocessed and subjected to Keystone transformation. Based on the dynamic slant range model, the residual air-range curvature is determined. A first filter based on an improved nonlinear frequency modulation scaling algorithm is used to compensate for the residual air-range curvature to obtain the corrected data. Based on the dynamic slant range model, the azimuth time offset is extended into an azimuth frequency modulation factor; Based on the azimuth frequency modulation factor and azimuth time offset, an improved extended nonlinear frequency modulation scaling algorithm is used to design a second filter, which is then used to filter the corrected data. The filtered data is subjected to Fourier transform, and the transformed data is compressed using a third filter. The compressed data is then subjected to inverse Fourier transform to obtain a focused synthetic aperture sonar image.

[0007] Furthermore, the calculation formula for the dynamic oblique variation model is as follows:

[0008] in, The instantaneous slant distance of the target point. The slope distance of the reference point. For the sonar platform's moving speed, It is an oblique perspective. This represents the azimuth and time offset of the target point relative to the reference point.

[0009] Furthermore, the perturbation function of the first filter is:

[0010] in, Let be the perturbation function of the first filter. For distance frequency, The time after Keystone transformation. The imaginary unit, The third disturbance coefficient, The fourth-order perturbation coefficient is... The speed of sound.

[0011] Furthermore, the formula for calculating the azimuth frequency modulation factor is as follows: ; in, The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. These are the coefficients for the second expansion.

[0012] Furthermore, the expression for the second filter is:

[0013] in, This is the phase correction function for the second filter. For azimuth frequency, This represents the azimuth time offset. The imaginary unit, The azimuth frequency modulation factor. The coefficient is cubic. The spatial variation coefficient of the third phase, It has a fourth-order coefficient.

[0014] Furthermore, the cubic coefficients, quartic coefficients, and spatially variable coefficients of the cubic phase are obtained by solving the phase matching equations.

[0015] Furthermore, the formulas for the phase-matching equations are as follows:

[0016] in, The coefficient is cubic. The coefficient is a fourth-order coefficient. The spatial variation coefficient of the third phase, This represents the maximum azimuth time offset at the scene edge. The expansion coefficient is the first expansion factor. These are the coefficients of the second expansion. The azimuth frequency is tuned to the reference point.

[0017] Furthermore, the third filter is an azimuth matched filter, and the expression for the third filter is:

[0018] in, The imaginary unit, The output of the third filter, For azimuth frequency, The azimuth frequency is tuned to the reference point.

[0019] Furthermore, the method further includes: analyzing and evaluating the azimuth secondary phase error, wherein the formula for calculating the secondary phase error is:

[0020] in, This is a quadratic phase error. The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. These are the coefficients of the second expansion. The time for synthesizing the aperture is denoted as .

[0021] Compared with the prior art, the present invention has the following advantages: 1. This invention constructs a dynamic slant range model that includes a quadratic correction term, which accurately characterizes the distance curvature difference of targets at different azimuth positions, effectively compensates for the high-order approximation error of the traditional linear model, and thus significantly improves the technical effect of slant range calculation accuracy.

[0022] 2. This invention achieves consistent focusing of target points at the center and edge of the scene by using spatially varied distance migration correction and azimuth frequency modulation factor extension equalization, effectively compensating for azimuth spatial variation errors.

[0023] 3. This invention achieves uniform focusing across the entire scene, with secondary phase error strictly controlled within the imaging requirement threshold, thus solving the defocusing problem at the scene edges in traditional methods.

[0024] Based on the above reasons, this invention can be widely applied in fields such as underwater acoustic signal processing and synthetic aperture sonar, and is suitable for underwater detection and mapping scenarios in shallow seas and hydrologically variable environments with significant noise backgrounds and complex multipath effects. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic flowchart of a slant-look synthetic aperture sonar imaging method based on a dynamic slant-range spatially variable model according to the present invention.

[0027] Figure 2 This is a geometric schematic diagram of the circular model used in this invention to construct a dynamic slant range spatial variation relationship model.

[0028] Figure 3 This is a comparison chart of the slope range calculation results of the traditional slope range relationship model and the dynamic model of this invention under different azimuth offsets.

[0029] Figure 4 This is a comparison chart of the slope range calculation errors of the traditional slope range relationship model and the dynamic model of this invention under different azimuth offsets.

[0030] Figure 5 This is a schematic diagram illustrating the focusing effect of the traditional method.

[0031] Figure 6 This is a schematic diagram illustrating the focusing effect of the method of the present invention.

[0032] Figure 7 This is a schematic diagram of the diffusion function using the traditional method.

[0033] Figure 8 This is a schematic diagram comparing the orientational cross-sections of the conventional method and the method of the present invention.

[0034] Figure 9 This is a schematic diagram of the diffusion function of the method of the present invention.

[0035] Figure 10 This is a bar chart comparing the comprehensive performance indicators of the present invention and traditional methods.

[0036] Figure 11 This is a comparison chart of the azimuth frequency modulation of the traditional method and the method of the present invention when the platform speed is increased to 2.0 m / s.

[0037] Figure 12 This is a comparison chart of the azimuth frequency modulation of the traditional method and the method of the present invention when the platform speed is increased to 3.2 m / s.

[0038] Figure 13This is a comparison chart of the azimuth frequency modulation of the traditional method and the method of the present invention when the platform speed is increased to 4.5 m / s.

[0039] Figure 14 This is a comparison curve of the second phase error (QPE) generated by the traditional method and the method of the present invention in the entire azimuth scene when the platform speed is increased to 2.0 m / s.

[0040] Figure 15 This is a comparison curve of the second phase error (QPE) generated by the traditional method and the method of the present invention in the entire azimuth scene when the platform speed is increased to 3.2 m / s.

[0041] Figure 16 This is a comparison curve of the second phase error (QPE) generated by the traditional method and the method of the present invention in the entire azimuth scene when the platform speed is increased to 4.5 m / s. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0044] The present invention is a slant-sight synthetic aperture sonar imaging method based on a dynamic slant-range spatially variable model. Its core lies in constructing a dynamic slant-range model that includes a quadratic correction term, and on this basis, performing spatially variable error compensation and parameter equalization, ultimately achieving high-quality focused imaging across the entire scene.

[0045] like Figure 1As shown, this invention provides a slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model, comprising the following steps: S1. Acquire raw echo data, which is obtained by oblique-view synthetic aperture sonar.

[0046] S2. Construct a dynamic slant range model, which is an improvement on the circular model. The dynamic slant range model is used to correlate the instantaneous slant range of the target point with the slant range of the reference point.

[0047] The traditional circular model assumes that the sonar platform moves at a constant speed along an ideal straight line during the synthetic aperture time, and approximates the platform's trajectory as an arc with the closest point between the target point and the platform's trajectory as the center, thereby establishing a functional relationship between the instantaneous slant range of the target and the slant range of the reference point.

[0048] The dynamic slant range model is an improved model based on the circular geometry model. The geometric principles are as follows: Figure 2 The model is presented in the form of a circle, which describes the center slant range relationship between any target point and a reference point within the same distance cell in the imaging scene. The radius of the circle model is related to the instantaneous motion state and geometric relationship of the sonar platform, and is used to derive the instantaneous slant range of target point B. Slope distance from reference point A The relationship.

[0049] The calculation formula for the dynamic oblique variation model is:

[0050] in, The instantaneous slant distance of the target point. The slope distance of the reference point. For the sonar platform's moving speed, It is an oblique perspective. This represents the azimuth and time offset of the target point relative to the reference point.

[0051] Azimuth time offset The beam center crossing time difference between the target point and the reference point can be determined in imaging processing based on the Doppler center frequency estimation result, or from the azimuth position difference between the target point and the reference point. and platform speed according to The result is obtained through conversion.

[0052] In practice, the sonar platform's navigation system and sensors first acquire environmental parameters in real time, such as the platform's velocity ν, oblique angle θ, and platform depth. Then, the center oblique distance of any target point within the same distance cell relative to the reference point is calculated using the aforementioned formula. The construction of this dynamic model provides a precise geometric basis for all subsequent processing steps.

[0053] like Figure 3 and Figure 4 As shown, traditional methods use linear relationships. Approximations performed using traditional methods result in significant errors at large oblique angles. However, this invention, by introducing a quadratic correction term, reduces errors in azimuth offset. The slope distance error at ±2.5 s was reduced from 1.8 m in the traditional method to 0.3 m, improving the accuracy by about 83%.

[0054] S3. The original echo data is preprocessed and subjected to Keystone transformation. The transformed data is then input into the dynamic skew-transformer model to obtain the residual air-transformer distance curvature. The air-transformer distance curvature is compensated using the first filter to obtain the corrected data. The first filter is constructed based on the improved nonlinear frequency modulation side table algorithm.

[0055] Based on the dynamic slant range spatial variation relationship model, range migration correction processing is performed on the echo signal of slant-looking synthetic aperture sonar. The processing includes azimuth spatial variation correction for spatial variation residual range migration.

[0056] Based on the slant range relationship, spatially varied residual range migration correction is achieved by introducing a fourth-order perturbation function with respect to azimuth and time. The perturbation function of the first filter is:

[0057] in, Let be the perturbation function of the first filter. For distance frequency, The time after Keystone transformation. The imaginary unit, The third disturbance coefficient, The fourth-order perturbation coefficient is... The speed of sound.

[0058] In practice, the received raw echo signal is first preprocessed. Then, the preprocessed echo signal undergoes Keystone transform to correct for linear distance migration. The transform formula is as follows: Next, the residual spatially varying range curvature is calculated based on the dynamic slant range model. To accurately compensate for this residual curvature, the aforementioned fourth-order perturbation filter is constructed and applied. This filter performs point-by-point phase compensation of the signal in the range-frequency domain and azimuth-time domain, effectively eliminating range migration differences between point targets at different azimuth positions. After this processing, the two-dimensional coupling characteristics of the echo signal are significantly improved, laying a good foundation for subsequent azimuth focusing.

[0059] S4. Based on the dynamic slant range model, the azimuth time offset is extended into an azimuth frequency modulation factor.

[0060] azimuth frequency modulation factor Based on dynamic slant range relationship, unfold to azimuth time offset The quadratic term, the formula for calculating the azimuth frequency modulation factor, is: ; in, The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. The coefficients are quadratic expansion coefficients, which can be determined by simultaneously solving the dynamic slant range model and the definition of the Doppler parameters. and The specific value.

[0061] In practice, based on the dynamic slant range relationship, the azimuth frequency modulation factor is expanded to the azimuth time offset. The quadratic term, where the expansion coefficients and It can be derived from the dynamic slant distance model.

[0062] S5. Based on the azimuth frequency modulation factor and azimuth time offset, an improved extended nonlinear frequency modulation scaling algorithm is used to design a second filter, which is then used to filter the corrected data.

[0063] Azimuth frequency modulation equalization is achieved through an improved extended nonlinear frequency modulation scaling algorithm. This algorithm uses a fourth-order filter to filter the signal and utilizes the slant range relationship to equalize the extended azimuth frequency modulation factor. The expression for the second filter is:

[0064] in, This is the phase correction function for the second filter. For azimuth frequency, This represents the azimuth time offset. The imaginary unit, The azimuth frequency modulation factor. The coefficient is cubic. The spatial variation coefficient of the third phase, It has a fourth-order coefficient.

[0065] The cubic, quartic, and cubic phase coefficients are obtained by solving the phase-matching equations. In practice, an improved Extended Nonlinear Frequency Modulation Scaling (ENLCS) algorithm is used to design the equalization filter. In actual implementation, the filter coefficients are determined by solving the phase-matching equations. , and The optimal value is obtained. This filter processes the signal in the azimuth frequency domain by analyzing each azimuth position. Construct the corresponding filter and perform frequency domain multiplication to achieve equalization processing of azimuth frequency modulation across the entire scene. For example... Figures 11-16 As shown, the method of the present invention is closer to the true value than the traditional linear expansion.

[0066] Specifically, the phase matching equations are constructed based on the ENLCS algorithm principle, with the third and fourth phase errors of the scene edge points being zero as the boundary condition, and take the following form:

[0067] in, The coefficient is cubic. The coefficient is a fourth-order coefficient. The spatial variation coefficient of the third phase, This represents the maximum azimuth time offset at the scene edge. The expansion coefficient is the first expansion factor. These are the coefficients of the second expansion. The azimuth frequency is tuned to the reference point.

[0068] S6. Perform a Fourier transform on the filtered data, compress the transformed data using the third filter, and perform an inverse Fourier transform on the compressed data to obtain a focused synthetic aperture sonar image.

[0069] The specific implementation process is as follows: First, the signal processed by equalization in step S3 is subjected to an azimuth-to-Fourier transform to convert the signal to the azimuth frequency domain. Then, the azimuth frequency is adjusted in the azimuth frequency domain based on the reference point. A matched filter is designed for azimuth compression. After matched filtering, the signal is converted back to the time domain using an inverse azimuth Fourier transform, achieving azimuth energy focusing. The resulting sonar image shows each point target with a sharp focal point. In practical systems, the aforementioned Fourier transform and inverse transform are efficiently implemented using a Fast Fourier Transform algorithm.

[0070] In a preferred embodiment of the present invention, the method further includes: analyzing and evaluating the azimuth secondary phase error, whereby the secondary phase error is the residual error after processing by the second filter. The formula for calculating the secondary phase error is:

[0071] in, This is a quadratic phase error. The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. These are the coefficients of the second expansion. This refers to the time required to synthesize the pore size. For example... Figures 11-16 As shown, the QPE of the method of the present invention is controlled below the π / 4 threshold throughout the entire azimuth scene, which fully meets the requirements of high-resolution imaging, while the QPE of the traditional method significantly exceeds the limit in the edge region.

[0072] Example Implementation examples Figures 3-16 The comparative analysis presented verifies the actual effectiveness of the method of the present invention.

[0073] Simulation parameter settings: The simulation parameters are set as follows: platform speed ν is 2.0 m / s, 3.2 m / s, and 4.5 m / s respectively; oblique angle θ = 45°; center frequency f. c =150 kHz, speed of sound c=1500 m / s, reference slant distance R0=400 m, synthesis aperture time T a =5.74 s. Nine point targets were placed in the imaging scene in a 3×3 grid distribution, including the scene center point and edge points.

[0074] Slant range accuracy comparison analysis. Under the conditions of platform speed ν=3.2 m / s and slant angle θ=45°, the traditional linear model has a higher accuracy in azimuth offset t. c The slant distance error at ±2.5 s reaches 1.8 m, while the error of the dynamic model of the present invention is only 0.3 m, which improves the accuracy by about 83%. Figure 3 The comparison of slope distance calculation results under the three models is presented intuitively. Figure 4 The quantitative analysis showed the error differences.

[0075] Focusing effect comparison and analysis. Under harsh imaging conditions (ν=4.5 m / s, θ=50°, t... c =3.0 s), the focusing effect of traditional methods on edge point targets is severely degraded. Figure 5 The traditional method showed a significant broadening of the focal point, with a 3dB main lobe width reaching 45 ms, representing a broadening of approximately 150% compared to the theoretical limit. Figure 6 The results show that the focusing effect of the method of the present invention is close to the theoretical limit, with a main lobe width of only 15.8 ms and a broadening of less than 10%. Figure 7 and Figure 9 This demonstrates a comparison of the diffusion function between the present invention and conventional methods. Figure 8 The azimuth profile comparison further quantifies this difference.

[0076] Phase error system analysis. For example... Figure 11 , Figure 12 and Figure 13 As shown, at different platform speeds, the method of the present invention exhibits significant advantages over traditional methods in azimuth frequency control. Figure 14 , Figure 15 and Figure 16 The comparison of the second phase error is shown: when the platform speed is 2.0 m / s, 3.2 m / s, and 4.5 m / s, the QPE of the traditional method exceeds the π / 4 threshold in the edge region. In particular, when ν=4.5 m / s, the maximum QPE of the traditional method reaches 0.42π, while the QPE of the method of this invention is always controlled within 0.18π.

[0077] Comprehensive performance evaluation. (Reference) Figure 10 The bar chart comparing the comprehensive performance indicators shows that the method of this invention is significantly superior to the traditional method in all key indicators. Specific data are as follows: the main lobe width is improved by approximately 65% ​​(3dB), the peak sidelobe ratio is improved by approximately 3.2 dB, the integral sidelobe ratio is improved by approximately 2.2 dB, the second phase error is reduced by approximately 57%, and the overall focus quality index is improved by 27 points.

[0078] Practical Application Verification. In a real-world sea trial, the method of this invention was used to process oblique-looking SAS echo data from a certain sea area. Test conditions were: platform was a certain type of AUV, sonar system center frequency was 150 kHz, bandwidth was 30 kHz; oblique angle was set to 50°, and platform speed was 3 knots. The test target was a standard target artificially placed on the seabed. Actual imaging results showed that in images obtained using the method of this invention, the focal point diameter of spherical targets was ≤15 cm, and the image resolution reached 0.15 m × 0.15 m. In contrast, in images obtained using traditional methods, target edges were blurred, and the estimated resolution was ≥25 cm. This practical effect is comparable to… Figure 4 The simulation results shown are highly consistent.

[0079] Device Implementation: The method of this invention can be implemented using a dedicated processing device, which includes one or more processors and a memory. The memory stores computer-executable instructions, which, when executed by the processor, cause the processor to perform the steps of the above-described method. The device can be integrated into the signal processing unit of a synthetic aperture sonar system, or it can be used as a standalone post-processing device communicating with the synthetic aperture sonar system. In practical applications, this device can be deployed on various platforms such as underwater unmanned submersibles, towed sonar systems, or fixed underwater monitoring systems.

[0080] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0081] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for slant-looking synthetic aperture sonar imaging based on a dynamic slant-range spatially varying model, characterized in that, Includes the following steps: The raw echo data is acquired using a slant-look synthetic aperture sonar. A dynamic slope range model is constructed, which is an improvement on the circle model. The dynamic slope range model is used to correlate the instantaneous slope range of the target point with the slope range of the reference point. The original echo data is preprocessed and subjected to Keystone transformation. Based on the dynamic slant range model, the residual air-range curvature is determined. A first filter based on an improved nonlinear frequency modulation scaling algorithm is used to compensate for the residual air-range curvature to obtain the corrected data. Based on the dynamic slant range model, the azimuth time offset is extended into an azimuth frequency modulation factor; Based on the azimuth frequency modulation factor and azimuth time offset, an improved extended nonlinear frequency modulation scaling algorithm is used to design a second filter, which is then used to filter the corrected data. The filtered data is subjected to Fourier transform, and the transformed data is compressed using a third filter. The compressed data is then subjected to inverse Fourier transform to obtain a focused synthetic aperture sonar image.

2. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The calculation formula for the dynamic oblique variation model is as follows: in, The instantaneous slant distance of the target point. The slope distance of the reference point. For the sonar platform's moving speed, It is an oblique perspective. This represents the azimuth and time offset of the target point relative to the reference point.

3. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The perturbation function of the first filter is: in, Let be the perturbation function of the first filter. For distance frequency, The time after Keystone transformation. The imaginary unit, The third disturbance coefficient, The fourth-order perturbation coefficient is... The speed of sound.

4. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The formula for calculating the azimuth frequency modulation factor is: ； in, The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. These are the coefficients for the second expansion.

5. The slant-sight synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The expression for the second filter is: in, This is the phase correction function for the second filter. For azimuth frequency, This represents the azimuth time offset. The imaginary unit, The azimuth frequency modulation factor. The coefficient is cubic. The spatial variation coefficient of the third phase, It has a fourth-order coefficient.

6. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 5, characterized in that, The cubic coefficients, quartic coefficients, and spatially variable coefficients of the cubic phase are obtained by solving the phase matching equations.

7. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 6, characterized in that, The formula for the phase-matching equations is: in, The coefficient is cubic. The coefficient is a fourth-order coefficient. The spatial variation coefficient of the third phase, This represents the maximum azimuth time offset at the scene edge. The expansion coefficient is the first expansion factor. These are the coefficients of the second expansion. The azimuth frequency is tuned to the reference point.

8. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The third filter is an azimuth matched filter, and its expression is: in, The imaginary unit, The output of the third filter, For azimuth frequency, The azimuth frequency is tuned to the reference point.

9. The slant-looking synthetic aperture sonar imaging method based on a dynamic slant-range spatially varying model according to claim 1, characterized in that, The method further includes: analyzing and evaluating the azimuth secondary phase error, wherein the formula for calculating the secondary phase error is: in, This is a quadratic phase error. The azimuth frequency modulation factor. azimuth frequency is tuned to the reference point. The expansion coefficient is the first expansion factor. This represents the azimuth time offset. These are the coefficients of the second expansion. The time for synthesizing the aperture is denoted as .

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