Self-adaptive estimation method for window length in SAR self-focusing

Abstract

The invention provides a self-adaptive estimation method for window length in SAR (synthetic aperture radar) self-focusing, which comprises the following steps of: S1, traversing an image distance dimension, and finding an amplitude maximum value Am of each distance unit to form a first array A (m), m representing a distance unit number; s2, amplitude maximum values Am in the first array A (m) are ranked from large to small, a ranked second array A '(m) is formed, a first distance dimension position array corresponding to the second array A' (m) is R (m), and a calculation formula of a distance dimension position Rm included in the first distance dimension position array R (m) is Rm = i * delta R, i is the distance unit number m corresponding to the maximum amplitude value in the second array A '(m), and delta R is the distance resolution of the image; s3, performing clustering processing according to the first distance dimension position array R (m) to obtain a clustered second distance dimension position array R '(m'); and S4, selecting the image of the second distance dimension position array R '(m') in the image, and estimating the azimuth dimension window length according to the image amplitude. According to the invention, the self-focusing performance is improved, and the imaging effect is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of image processing, and in particular relates to an adaptive estimation method for the autofocusing window length in missile-borne synthetic aperture radar (SAR) imaging. Background Art

[0002] For Synthetic Aperture Radar (SAR) imaging, the phase gradient autofocus algorithm is a commonly used autofocus algorithm. This algorithm does not require prior information and is suitable for application scenarios such as missile-borne synthetic aperture radar imaging, which have less prior information. For the phase gradient autofocus algorithm, it is necessary to select the autofocus window length. Inappropriate selection of window lengths in different scenes and different targets may lead to problems such as defocusing. Selecting a suitable window length can improve the autofocus performance to a certain extent.

[0003] At the same time, the existing SAR autofocusing imaging methods have limitations in dealing with the azimuth defocus caused by the residual phase in large front oblique conditions, the existence of multiple strong point scattering in the same distance unit, the focusing problem of the uniform imaging area, and the sensitivity of the selection of the autofocusing window length. Summary of the invention

[0004] The purpose of the present invention is to provide a method for adaptively estimating the window length in SAR autofocusing, so as to solve the problem that the autofocusing performance in SAR imaging depends on the window length, and further realize adaptive estimation of the window length in the absence of prior information on the window length, thereby significantly improving the autofocusing performance, and being particularly suitable for missile-borne radars.

[0005] To achieve the above object, the present invention provides a method for adaptively estimating window length in SAR autofocusing, which comprises:

[0006] S1. Traverse the image distance dimension and find the maximum amplitude A of each distance unit m Form a first array A(m), where m represents the distance unit number;

[0007] S2. The maximum amplitude value A in the first array A(m) m Sort from large to small to form a sorted second array A′(m), the first distance dimension position array corresponding to the second array A′(m) is R(m), and the distance dimension position R contained in the first distance dimension position array R(m) m The calculation formula is R m =i×ΔR, i is the distance unit number m corresponding to the maximum amplitude in the second array A′(m), ΔR is the distance resolution of the image;

[0008] S3, performing clustering processing according to the first distance dimension position array R(m) to obtain a clustered second distance dimension position array R′(m′);

[0009] S4. Select an image of the second distance dimension position array R′(m′) in the image, and estimate the azimuth dimension window length according to the image amplitude.

[0010] Preferably, the step S3 of performing clustering processing according to the first distance dimension position array R(m) to obtain the clustered second distance dimension position array R′(m′) comprises the following steps:

[0011] S31, set the initial value of comparison variable k to 0, m = k + 1, where R k＝0 =0;

[0012] S32. Calculate R k Value and R m value, and compare R m Value and R k value;

[0013] S33, if | R m -R k |<ΔR, then R m The value is invalid, otherwise R m The value is valid;

[0014] S34, if m<M-2, then m is increased by 1, and steps S32 to S33 are repeated, otherwise, the process goes to step S34;

[0015] S35, if k < M-1, then k is increased by 1, and steps S31 to S34 are repeated, otherwise, the process goes to step S35; wherein M is the determination threshold;

[0016] S36, for the distance dimension position R in the first distance dimension position array R(m), m Perform statistics and keep R m The value is the effective distance dimension position and is recorded as R m' , remove R m The values ​​are invalid distance dimension positions, forming the second distance dimension position array R′(m′).

[0017] Preferably, the decision threshold M is proportional to the complexity of the image, and the decision threshold M is inversely proportional to the operation rate.

[0018] Preferably, the value of the determination threshold M is 16.

[0019] Preferably, the step S4 of selecting the image of the second distance dimension position array R′(m′) in the image and estimating the azimuth dimension window length according to the image amplitude comprises the following steps:

[0020] S41, extract the distance dimension position R in the second distance dimension position array R′(m′) m' The corresponding orientation image S n , forming an azimuth dimension image array S(n), wherein n is the azimuth dimension position, the value of n is 0 to N-1, N is the number of azimuth dimension units, and the value of m' starts from 0;

[0021] S42, find the orientation-dimensional image S in the orientation-dimensional image array S(n). n The maximum value of , and record the maximum value as n′;

[0022] S43, set j=-1;

[0023] S44, Compare S n' With S n'+j ,like Then record the first azimuth position n at this time d (m') = n' + j, go to step S45, otherwise j minus 1 and repeat step S44; where P is a fixed threshold used to limit the window width;

[0024] S45, set j=1;

[0025] S46, Comparison S n' With S n'+j ,like Then record the second azimuth position n at this time u (m') = n' + j, go to step S47, otherwise j is increased by 1 and step S46 is repeated;

[0026] S47, m' is increased by 1, and steps S41 to S46 are repeated until m'=16;

[0027] S48. Calculate n u (m')-n d (m')|, and calculate n u (m')-n d (m')|standard deviation std;

[0028] S49, the n u (m')-n d The maximum value of (m')| and |n u (m')-n d (m')|, and the standard deviation std is added to obtain the estimated azimuth dimension window length max(n u (m')-n d (m'))+std.

[0029] Preferably, the calculation formula of the standard deviation std is:

[0030]

[0031] Preferably, the value of the fixed threshold P is 5dB.

[0032] The advantages of the present invention over the prior art are as follows: the adaptive estimation method for window length in SAR autofocusing provided by the present invention improves the autofocusing performance by adaptively estimating the autofocusing window length; at the same time, the autofocusing process does not rely on prior parameters, and compared with the prior method, the decoupling of the self-focusing performance and the window length selection is achieved, which can effectively improve the self-focusing performance and is suitable for application scenarios with fewer prior parameters such as missile-borne imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of the adaptive estimation method of window length in SAR autofocusing of the present invention;

[0034] Figure 2 The self-focusing image in the embodiment of the present invention without using the adaptive estimation method of the window length in the SAR self-focusing of the present invention;

[0035] Figure 3 This is a self-focusing image using the adaptive estimation method of window length in SAR self-focusing of the present invention in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the attached embodiment of the present invention Figure 1 ~Attached Figure 3 , the technical solutions, structural features, objectives achieved and effects in the embodiments of the present invention are described in detail.

[0037] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0038] It should be noted that, in the present invention, relational terms such as first and second, etc. 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 the elements explicitly listed, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0039] In order to improve the autofocusing performance of SAR imaging, the present invention provides an adaptive estimation method for the window length in SAR autofocusing, such as Figure 1 As shown, the adaptive estimation method includes:

[0040] S1. Traverse the image distance dimension, which includes m distance units, and find the maximum amplitude A of each distance unit. m Form a first array A(m), where m represents the distance unit number;

[0041] S2. The maximum amplitude value A in the first array A(m) m Sort from large to small to form a sorted second array A′(m), the first distance dimension position array corresponding to the second array A′(m) is R(m), and the distance dimension position R contained in the first distance dimension position array R(m) m The calculation formula is R m =i×ΔR, i is the distance unit number m corresponding to the maximum amplitude in the second array A′(m), ΔR is the distance resolution of the image; by arranging the maximum amplitude from large to small, it is helpful to quickly identify potential target areas, reduce noise interference, and thus improve the efficiency of the clustering algorithm.

[0042] S3. Perform clustering processing according to the first distance dimension position array R(m) to obtain a clustered second distance dimension position array R′(m′) to reduce image complexity and improve computational efficiency.

[0043] S4. Select an image of the second distance dimension position array R′(m′) in the image, and estimate the azimuth dimension window length according to the image amplitude.

[0044] Specifically, the clustering process according to the first distance dimension position array R(m) described in step S3 to obtain the clustered second distance dimension position array R′(m′) specifically includes the following steps:

[0045] S31, set the initial value of comparison variable k to 0, m = k + 1, where R k＝0 =0;

[0046] S32. Calculate R k Value and R m value, and compare R m Value and R k value;

[0047] S33, if | R m -R k |<ΔR, then R m The value is invalid, otherwise R m The value is valid;

[0048] S34, if m<M-2, then m is increased by 1, and steps S32 to S33 are repeated, otherwise, the process goes to step S34;

[0049] S35, if k < M-1, then k is increased by 1, and steps S31 to S34 are repeated, otherwise, the process goes to step S35; wherein M is the determination threshold;

[0050] S36, for the distance dimension position R in the first distance dimension position array R(m), m Perform statistics and keep R m The value is the effective distance dimension position and is recorded as R m' , remove R m The values ​​are invalid distance dimension positions, forming the second distance dimension position array R′(m′).

[0051] The value of the determination threshold M is mainly related to the operation rate and image complexity. Specifically, the determination threshold M is proportional to the image complexity, and the determination threshold M is inversely proportional to the operation rate. In this embodiment, taking both the scene and the operation rate into consideration, the value of the determination threshold M is 16, which not only ensures the wide application scene, but also ensures the timeliness of the operation efficiency.

[0052] Further, the image of the second distance dimension position array R′(m′) in the selected image in step S4 is estimated according to the image amplitude. Specifically, the following steps are included:

[0053] S41, extract the distance dimension position R in the second distance dimension position array R′(m′) m' The corresponding orientation image S n , forming an azimuth dimension image array S(n), wherein n is the azimuth dimension position, the value of n is 0 to N-1, N is the number of azimuth dimension units, and the value of m' starts from 0;

[0054] S42, find the orientation-dimensional image S in the orientation-dimensional image array S(n). n The maximum value of , and record the maximum value as n′;

[0055] S43, setting j=-1, used to traverse the left part of the azimuth-dimensional image array S(n);

[0056] S44, Compare S n' With S n'+j ,like Then record the first azimuth position n at this time d (m') = n' + j, go to step S45, otherwise j minus 1 and repeat step S44; where P is a fixed threshold used to limit the window width;

[0057] S45, setting j=1, used to traverse the right part of the azimuth-dimensional image array S(n);

[0058] S46, Comparison S n' With S n'+j ,like Then record the second azimuth position n at this time u (m') = n' + j, go to step S47, otherwise j is increased by 1 and step S46 is repeated;

[0059] S47, m' is increased by 1, and steps S41 to S46 are repeated until m'=16;

[0060] S48, Calculate |n u (m')-n d (m')|, and calculate |n u (m')-n d (m')|standard deviation std;

[0061] S49, the n u (m')-n d The maximum value of (m')| and |n u (m')-n d (m')|, and the standard deviation std is added to obtain the estimated azimuth dimension window length max(n u (m')-n d (m'))+std.

[0062] The fixed threshold P is related to the image resolution and is used to limit the window width, which can be selected according to actual needs. In this embodiment, the fixed threshold P is set to 5 dB to improve the self-focusing performance.

[0063] Furthermore, the calculation formula of the standard deviation std is:

[0064]

[0065] In this embodiment, the u (m')-n d The maximum value of (m')| and |n u (m')-n d The self-focusing window length is obtained by adding the standard deviation std of (m')|.

[0066] In a specific embodiment, if Figure 2 As shown, Figure 2 is a two-dimensional image after autofocusing obtained by using a fixed window length, the horizontal axis represents the azimuth dimension, and the vertical axis represents the distance dimension. Using the adaptive estimation method for window length in SAR autofocusing provided by the present invention, the two-dimensional image after autofocusing is as follows Figure 3As shown, comparison Figure 2 and Figure 3 The two-dimensional image in Figure 3 The self-focusing effect is better than Figure 2 The present invention has been applied to a certain type of missile-borne imaging and has obtained good imaging results.

[0067] In summary, the adaptive estimation method of window length in SAR autofocusing provided by the present invention improves the autofocusing performance by adaptively estimating the autofocusing window length; at the same time, it does not rely on prior parameters in the process of autofocusing. Compared with the existing method, it realizes the decoupling of self-focusing performance and window length selection, which can effectively improve the self-focusing performance and is suitable for application scenarios with fewer prior parameters such as missile-borne imaging.

[0068] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. An adaptive estimation method for window length in SAR autofocusing, characterized in that: include: S1. Traverse the image distance dimension and find the maximum amplitude A of each distance unit m Form a first array A(m), where m represents the distance unit number; S2. The maximum amplitude value A in the first array A(m) m Sort from large to small to form a sorted second array A′(m), the first distance dimension position array corresponding to the second array A′(m) is R(m), and the distance dimension position R contained in the first distance dimension position array R(m) m The calculation formula is R m =i×ΔR, i is the distance unit number m corresponding to the maximum amplitude in the second array A′(m), ΔR is the distance resolution of the image; S3, performing clustering processing according to the first distance dimension position array R(m) to obtain a clustered second distance dimension position array R′(m′); S4. Select an image of the second distance dimension position array R′(m′) in the image, and estimate the azimuth dimension window length according to the image amplitude.

2. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 1, characterized in that: The step S3 of clustering the first distance dimension position array R(m) to obtain the clustered second distance dimension position array R′(m′) comprises the following steps: S31, set the initial value of comparison variable k to 0, m = k + 1, where R k＝0 =0; S32. Calculate R k Value and R m value, and compare R m Value and R k value; S33, if | R m -R k |<ΔR, then R m The value is invalid, otherwise R m The value is valid; S34, if m<M-2, then m is increased by 1, and steps S32 to S33 are repeated, otherwise, the process goes to step S34; S35, if k < M-1, then k is increased by 1, and steps S31 to S34 are repeated, otherwise, the process goes to step S35; Among them, M is the judgment threshold; S36, for the distance dimension position R in the first distance dimension position array R(m), m Perform statistics and keep R m The value is the effective distance dimension position and is recorded as R m' , remove R m The values ​​are invalid distance dimension positions, forming the second distance dimension position array R′(m′).

3. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 2, characterized in that: The decision threshold M is proportional to the image complexity, and the decision threshold M is inversely proportional to the operation rate.

4. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 3, characterized in that: The value of the determination threshold M is 16.

5. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 2, characterized in that: The step S4 of selecting the image of the second distance dimension position array R′(m′) in the image and estimating the azimuth dimension window length according to the image amplitude comprises the following steps: S41, extract the distance dimension position R in the second distance dimension position array R′(m′) m' The corresponding orientation image S n , forming an azimuth dimension image array S(n), wherein n is the azimuth dimension position, the value of n is 0 to N-1, N is the number of azimuth dimension units, and the value of m' starts from 0; S42, find the orientation-dimensional image S in the orientation-dimensional image array S(n). n The maximum value of , and record the maximum value as n′; S43, set j=-1; S44, Compare S n' With S n'+j ,like Then record the first azimuth position n at this time d (m') = n' + j, go to step S45, otherwise j minus 1 and repeat step S44; where P is a fixed threshold used to limit the window width; S45, set j=1; S46, Comparison S n' With S n'+j ,like Then record the second azimuth position n at this time u (m') = n' + j, go to step S47, otherwise j is increased by 1 and step S46 is repeated; S47, m' is increased by 1, and steps S41 to S46 are repeated until m'=16; S48. Calculate n u (m')-n d (m')|, and calculate |n u (m')-n d (m')|standard deviation std; S49, the said |n u (m')-n d The maximum value of (m')| and |n u (m')-n d (m')|, and the standard deviation std is added to obtain the estimated azimuth dimension window length max(n u (m')-n d (m'))+std.

6. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 5, characterized in that: The calculation formula of the standard deviation std is:

7. The adaptive estimation method for window length in SAR autofocusing as claimed in claim 5, characterized in that: The value of the fixed threshold P is 5dB.

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