SAR image fine focusing method based on fractional Fourier transform

Abstract

The invention discloses an SAR image fine focusing method based on fractional Fourier transform. Relates to the field of radar remote sensing technology application, in particular to the technical field of SAR image focusing processing. The technical problem that a motion compensation method based on inertial navigation data and a self-focusing method based on phase errors, which are commonly used in SAR image motion compensation methods, cannot meet the requirements of fine and rapid focusing is solved. The method comprises the following steps: S1, carrying out distance direction partitioning on a defocused SAR image to obtain a plurality of distance blocks; s2, calculating fractional Fourier transform of a strong point target in each distance block; s3, calculating a kurtosis characteristic of a fractional order Fourier transform result; s4, the maximum value of the kurtosis features is searched in a one-dimensional mode, and the optimal rotation order of each distance block is obtained; and S5, performing fractional Fourier transform on the SAR image based on the optimal rotation order of each distance block to obtain a focused SAR image.

Description

Technical Field

[0001] The present invention relates to the application field of radar remote sensing technology, and in particular to the technical field of SAR image focusing processing. Background Art

[0002] Synthetic Aperture Radar (SAR) is not affected by clouds, rain or fog, and can observe the earth all day and all weather. It is widely used in ground object classification and target detection. When SAR is installed on a moving platform, its motion state deviates from the ideal uniform linear motion. At this time, if the received SAR echo is imaged directly, the imaging result will be defocused, and the defocused SAR image will affect the subsequent SAR image interpretation. Therefore, how to perform motion compensation on the defocused SAR image to achieve focusing is of great research significance.

[0003] SAR image motion compensation methods are mainly divided into two categories, including motion compensation methods based on inertial navigation data and self-focusing methods based on phase error. The motion compensation method based on inertial navigation data requires the use of inertial navigation and global positioning systems installed on the SAR platform to achieve motion compensation by measuring the real position information and motion information of the SAR platform. However, there are cumulative errors in the motion information measured by the inertial navigation device, and when the resolution of the SAR image is high, the measurement accuracy of the inertial navigation device can no longer meet the imaging requirements. The self-focusing method based on phase error uses a high-order phase polynomial error model to solve the coefficients of the phase error polynomial from the defocused SAR image, and then compensates the phase error to the defocused SAR image. However, this type of self-focusing method based on phase error is time-consuming and has limited focusing performance.

[0004] Therefore, at this stage, a SAR image fine focusing method is needed that can achieve fast SAR image focusing and finely focus on different objects in the SAR image. Summary of the invention

[0005] In order to solve the technical problem that the conventional motion compensation method based on inertial navigation data and the self-focusing method based on phase error in the SAR image motion compensation method cannot meet the requirements of fine and fast focusing, the present invention provides a SAR image fine focusing method based on fractional Fourier transform, and the method comprises the following steps:

[0006] S1, dividing the defocused SAR image into blocks in the range direction to obtain a number of range blocks;

[0007] S2, calculate the fractional Fourier transform of the strong point target in each range block;

[0008] S3, calculating the kurtosis characteristics of the fractional Fourier transform result;

[0009] S4, one-dimensional search for the maximum value of the kurtosis feature to obtain the best rotation order for each distance block;

[0010] S5. Performing fractional Fourier transform on the SAR image based on the optimal rotation order of each range block to obtain a focused SAR image.

[0011] The beneficial effects of the method of the present invention are:

[0012] It can achieve fast SAR image focusing and finely focus on different objects in SAR images. First, the SAR image is divided into blocks in the range direction, and typical strong point targets in each block are selected to calculate the fractional Fourier transform of the range gate where the strong point targets are located in each block. Secondly, the kurtosis feature of the fractional Fourier transform result is calculated, and the optimal rotation order where the maximum value of the kurtosis feature is searched in one dimension is used. Based on the kurtosis feature, the two-dimensional search can be converted into a one-dimensional search with higher estimation accuracy. Finally, the fractional Fourier transform of the SAR image of each range block is performed under the optimal rotation order to achieve fine focusing of the SAR image. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of the method according to an embodiment of the present invention;

[0014] Figure 2 The forest area, building area and corner reflector area are selected to display the enlarged effect;

[0015] Figure 3 This is a comparison chart of the analysis results of three selected corner reflector point targets. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The motion compensation method based on inertial navigation data requires the use of inertial navigation and global positioning systems installed on the SAR platform to achieve motion compensation by measuring the real position information and motion information of the SAR platform. However, there is a cumulative error in the motion information measured by the inertial navigation device, and when the resolution of the SAR image is high, the measurement accuracy of the inertial navigation device can no longer meet the imaging requirements. The phase error-based self-focusing method uses a high-order phase polynomial error model to solve the coefficients of the phase error polynomial from the defocused SAR image, and then compensates the phase error to the defocused SAR image. However, this type of phase error-based self-focusing method is time-consuming and has limited focusing performance.

[0018] The SAR image fine focusing method based on fractional Fourier transform proposed in this embodiment first improves the processing speed of the focusing method of this embodiment by performing distance-wise block division on the defocused SAR image and adopting a coarse search followed by a fine search strategy. Secondly, by calculating the kurtosis feature of the fractional Fourier transform, the two-dimensional peak search is converted into a one-dimensional peak search, thereby improving the estimation accuracy of the optimal rotation order and further reducing the computational complexity of the method described in this embodiment.

[0019] like Figure 1 The flowchart of the method described in this embodiment includes the following specific steps:

[0020] Step 1: Divide the defocused SAR image into blocks and calculate the fractional Fourier transform of the strong point targets in the blocks.

[0021] A defocused SAR image f(m,n) of size Na×Nr is divided into 100 blocks in the range direction, where m represents the mth azimuth gate and n represents the nth azimuth gate. Based on the bi-norm, the range gate n where the strong point target is located is selected in each range block k (k=1,2,…,100). k :

[0022]

[0023] For each range block of the defocused SAR image, the range gate f(:,n k ) is transformed into the fractional Fourier transform (FrFT):

[0024]

[0025] in Indicates that in the rotation order α k The fractional Fourier transform operation is performed under g k is the result of fractional Fourier transform, and its size is Na×Nf. In this embodiment, the rotation order step size of the rough search FrFT is set to 0.05, and the search range is [-1,1], so Nf is 400, and Nf is the size of the SAR image.

[0026] Where f(:,n k ) is defined as

[0027]

[0028] where g k f(:,n k ), where α is the fractional Fourier transform result of f(:,n k ) is the rotation angle of the fractional Fourier transform, which ranges from [-Π,Π], n is an integer.

[0029] Step 2: Calculate the kurtosis characteristics of the fractional Fourier transform results and search for the optimal rotation order.

[0030] Calculate the fractional Fourier transform result g of each range gate above k The kurtosis characteristic is:

[0031] T k =Kurt[g k ];

[0032] Among them, T k g k The kurtosis of is Nf=400, and Kurt(·) represents the calculation of the kurtosis feature, which is defined as:

[0033]

[0034] where μ is g k , E[·] represents the mathematical expectation operation.

[0035] To T k Perform a one-dimensional peak search to obtain the best rotation order for the rough search Calculate g k The kurtosis can not only improve the estimation accuracy of the optimal rotation order, but also transform the two-dimensional peak search for the optimal rotation order into a one-dimensional search, thus reducing the computational complexity.

[0036] The best rotation order is roughly searched Centered on Perform fractional Fourier transform on the range gate of each range block in the range with a step size of 0.001, then calculate the kurtosis of the fractional Fourier transform result, and search the maximum value of the kurtosis in one dimension to obtain the best rotation order for fine search. By calculating the optimal rotation order by using a strategy of first coarse search and then fine search, the time consumption of the method described in this embodiment can be reduced.

[0037] Step 3: Perform fractional Fourier transform on the SAR image based on the optimal rotation order of each range block.

[0038] For each range bin k (k = 1, 2, ..., 100) all range gates According to the best rotation order search After performing fractional Fourier transform, the focused SAR image is obtained. for:

[0039] exist and In the column, “:” means the elements of all rows included in this column.

[0040] The method described in this embodiment divides the distance direction into blocks, selects typical strong point targets for fractional Fourier transform, and then converts the two-dimensional search into a one-dimensional search by calculating the kurtosis characteristics of the fractional Fourier transform results, thereby improving the processing speed of the method. In order to further improve the estimation accuracy and speed of the method, the optimal rotation order is searched coarsely and finely, and the focusing of high-resolution and wide-band SAR images has high practicality.

[0041] The effectiveness of the patented method is verified by using the measured sports car SAR data. The sports car data scene size is 11828×8000, and the Phase Gradient Autofocus (PGA) method is selected as the comparison method, where the number of iterations of the PGA method is 15.

[0042] Table 1 shows the time comparison results of different focusing methods. It can be seen that the PGA method takes 538.02 seconds, which is time-consuming, while the method of this patent only takes 123.84 seconds, which greatly reduces the SAR image focusing time.

[0043] Table 1:

[0044] method PGA method The method of this embodiment time 538.02(s) 123.84(s)

[0045] like Figure 2 As shown in , the forest area, building area and corner reflector area are selected respectively to demonstrate the magnification effect, wherein (a1) is the focusing effect of the PGA method when the forest area is selected; (b1) is the focusing effect of the method described in this embodiment when the forest area is selected; (a2) is the focusing effect of the PGA method when the building area is selected; (b2) is the focusing effect of the method described in this embodiment when the building area is selected; wherein (a3) ​​is the focusing effect of the PGA method when the corner reflector area is selected; (b3) is the focusing effect of the method described in this embodiment when the corner reflector area is selected; it can be seen that, compared with the targets in (a1), (a2) and (a3), the method described in this embodiment can focus on the tree crowns, buildings and corner reflectors well. Figure 2 As shown in (b3), three corner reflectors are circled in red from top to bottom. The three corner reflectors become point targets after being focused by the method described in this embodiment.

[0046] Figure 3are the analysis results of three selected corner reflector point targets, wherein (a1), (b1), (c1) and (d1) are respectively the upsampling result of the corner reflector point target in the focused SAR image of the PGA method under one of the corner reflector point targets, the upsampling result of the corner reflector point target in the focused SAR image of the method described in this embodiment, the azimuth slice of the corner reflector point target in the focused SAR image of the PGA method, and the azimuth slice of the corner reflector point target in the focused SAR image of the method described in this embodiment, and in (a1) and (b1), the abscissa represents the distance unit and the ordinate represents the azimuth unit, and in (c1) and (d1), the abscissa is the azimuth unit and the ordinate is the logarithmic amplitude;

[0047] (a2), (b2), (c2) and (d2) are respectively the upsampling result of the corner reflector point target in the focused SAR image of the PGA method under another corner reflector point target, the upsampling result of the corner reflector point target in the focused SAR image of the method described in this embodiment, the azimuth slice of the corner reflector point target in the focused SAR image of the PGA method and the azimuth slice of the corner reflector point target in the focused SAR image of the method described in this embodiment, and in (a2) and (b2), the abscissa represents the distance unit and the ordinate represents the azimuth unit, and in (c2) and (d2), the abscissa is the azimuth unit and the ordinate is the logarithmic amplitude;

[0048] (a3), (b3), (c3) and (d3) are respectively the upsampling result of the corner reflector point target in the focused SAR image of the third corner reflector point target by the PGA method, the upsampling result of the corner reflector point target in the focused SAR image by the method described in this embodiment, the azimuth slice of the corner reflector point target in the focused SAR image by the PGA method and the azimuth slice of the corner reflector point target in the focused SAR image by the method described in this embodiment, and in (a3) ​​and (b3), the horizontal axis represents the distance unit and the vertical axis represents the azimuth unit, and in (c3) and (d3), the horizontal axis is the azimuth unit and the vertical axis is the logarithmic amplitude.

[0049] The focusing performance of the three corner reflectors is quantitatively evaluated based on the peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR) and main lobe width (IRW) indicators. As shown in Table 2, the PGA method has a very poor focusing effect on the three corner reflectors. The indicators of the three corner reflectors after focusing by the method described in this embodiment meet practical requirements, and the main lobe width indicator reaches 0.2m.

[0050] Table 2:

[0051]

Claims

1. A SAR image fine focusing method based on fractional Fourier transform, characterized in that: The method comprises the following steps: S1, dividing the defocused SAR image into blocks in the range direction to obtain a number of range blocks; S2, calculate the fractional Fourier transform of the strong point target in each range block; S3, calculating the kurtosis characteristics of the fractional Fourier transform result; S4, one-dimensional search for the maximum value of the kurtosis feature to obtain the best rotation order for each distance block; S5. Performing fractional Fourier transform on the SAR image based on the optimal rotation order of each range block to obtain a focused SAR image.

2. The SAR image fine focusing method based on fractional Fourier transform according to claim 1, characterized in that: The defocused SAR image is divided into several range blocks. Then, the range gate n where the strong point target is located is selected in each range block based on the bi-norm. k , k represents the number of the distance block.

3. The SAR image fine focusing method based on fractional Fourier transform according to claim 2, characterized in that: The fractional Fourier transform of the strong point target in each range block is calculated as follows: The range gate in each range block of the defocused SAR image is transformed by fractional Fourier transform.

4. The SAR image fine focusing method based on fractional Fourier transform according to claim 3 is characterized in that: When performing fractional Fourier transform, the rotation order step size is set to 0.05 and the search range is set to [-1, 1].

5. The SAR image fine focusing method based on fractional Fourier transform according to claim 4, characterized in that: The kurtosis characteristic of the fractional Fourier transform result is calculated by: Get, where T k Represents the kurtosis characteristic of the fractional Fourier transform result, g k represents the fractional Fourier transform of the strong point target in each range block, k represents the number of the range block, and μ is g k , E[·] represents the mathematical expectation operation.

6. The SAR image fine focusing method based on fractional Fourier transform according to claim 5, characterized in that: Step S4 is specifically as follows: To T k Perform a one-dimensional peak search to obtain the best rotation order for the rough search The best rotation order is roughly searched Centered on The range gate of each range block is transformed by fractional Fourier transform with a step size of 0.001, and then the kurtosis of the fractional Fourier transform result is calculated. The maximum value of the kurtosis is searched in one dimension to obtain the optimal rotation order of each range block.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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