Sidelobe Suppression Method and System for Two-Dimensional Radar Images

Abstract

The present invention discloses a method and system for sidelobe suppression of two-dimensional radar images. The present invention uses a two-dimensional cosine basis function with an adaptive weighting region to perform weighting in the wavenumber domain to achieve sidelobe suppression. The weighting region of the weighting function is determined by the pixel positions, and the wavenumber domain weighting is equivalently implemented through convolution in the image domain. By changing the coefficients of the two-dimensional cosine basis function, different sidelobe suppression effects can be obtained. By taking the minimum value of these weighting results pixel by pixel, multi-tapering can be achieved, so as to suppress the sidelobes without broadening the main lobe. The present invention equivalently implements multi-tapering by constraining the variation range of the coefficients of the two-dimensional cosine basis function and solving the minimum value of the weighted image amplitude with respect to the variation of the coefficients of the two-dimensional cosine basis function, thereby effectively suppressing the two-dimensional sidelobes without sacrificing the image resolution.

Description

Technical Field

[0001] The present invention relates to radar image processing technology, in particular to a sidelobe suppression method and system for two-dimensional radar images. Background Art

[0002] The matched filtering algorithm is the most commonly used imaging method in imaging radars. However, limited by the finite aperture length and signal bandwidth, sidelobes inevitably exist in the imaging results of the matched filtering imaging algorithm. The typical sidelobe level is about -13 dB. The existence of sidelobes will cause weak targets adjacent to strong scattering points to be obscured, affecting the image interpretation performance.

[0003] Currently, the commonly used sidelobe suppression methods mainly fall into three categories. The first category is target-by-target extraction, the second category is the sidelobe suppression method based on sub-image difference, and the third category is frequency domain windowing.

[0004] The first category of methods mainly includes the CLEAN algorithm (J.Tsao and B.D.Steinberg, "Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique," IEEE Trans. Antennas Propagat., vol. 36, no. 4, pp. 543-556, Apr. 1988, doi: 10.1109 / 8.1144.) and so on. This type of method successively extracts the strongest points in the image and removes them from the echo or image. Finally, the extracted targets are used to reconstruct the imaging result. This type of method uses iterative processing, and the processing efficiency is low.

[0005] The second category of methods mainly includes coherence factor weighting and so on (K.W. Hollman, K.W. Rigby and M.O’Donnell, "Coherence factor of speckle from a multi-row probe", in Proc. IEEE Ultrason. Symp. Int. Symp., vol. 2, pp. 1257-1260, Oct. 1999, doi: 10.1109 / ULTSYM.1999.849225.). This type of method calculates the weighting factor by using the difference of sidelobes on the sub-image, so as to achieve sidelobe suppression. However, affected by noise and the interaction between targets, this method will also suppress the main lobe of the target while suppressing the sidelobes.

[0006] The third category of methods is the most commonly used sidelobe suppression method.

[0007] Windowing can be divided into wavenumber domain windowing and aperture domain windowing.

[0008] Windowing to suppress sidelobes comes at the cost of broadening the main lobe of the target. The apodization technique can avoid broadening the main lobe while suppressing sidelobes. The apodization technique uses multiple window functions for weighting, and then takes the minimum value of the windowed results pixel by pixel to achieve sidelobe suppression without broadening the main lobe. The more window functions are used, the better the sidelobe suppression effect, but at the same time, it will also lead to a decrease in processing efficiency.

[0009] The spatially variant apodization (SVA) method proposed by Stankwitz et al. in the literature (H.C. Stankwitz, R.J. Dallaire and J.R. Fienup, "Nonlinear apodization for sidelobe control in SAR imagery," IEEE Trans. Aerosp. Electron. Syst., vol. 31, no. 1, pp. 267 - 279, Jan. 1995, doi: 10.1109 / 7.366309.) uses a cosine basis function as the wavenumber domain weighting function. The image domain response of the cosine basis function consists of three impulse functions. Therefore, the cosine basis function weighting can be equivalently implemented by a three-point convolver in the image domain. The SVA method equivalently implements multi-apodization by solving a constrained minimization problem.

[0010] The prerequisite for effective sidelobe suppression by wavenumber domain windowing and the SVA method is that the wavenumber spectrum can be effectively weighted. Under far-field conditions, the wavenumber spectrum does not have spatial variability, that is, the wavenumber spectra of all targets have the same support set. Therefore, a single weighting function can effectively weight the wavenumber spectra of all targets simultaneously. However, under near-field conditions, the wavenumber spectrum has spatial variability, that is, the position and size of the wavenumber spectrum are related to the target position. Therefore, there is no weighting function that can effectively weight the wavenumber spectra of different targets simultaneously, making the SVA method no longer applicable under near-field conditions.

[0011] To extend the SVA for sidelobe suppression in near-field radar images.

[0012] R. Zhu et al. (R. Zhu, J. Zhou and Q. Fu, "Spatially variant apodisation for sidelobe suppression in near range radar imagery," IET Radar, Sonar Navig., vol. 16, no. 6, pp. 986–999, 2022.) first multiplied a phase compensation factor in the image domain to overcome the spatial variability of the wavenumber spectrum. The compensated wavenumber spectrum has the same wavenumber spectrum center, and then a weighting function with a variable weighting region is adopted in the azimuth direction. However, this method only solves the azimuth sidelobe suppression and cannot suppress the two-dimensional sidelobes in the azimuth and range directions of the near-field radar image simultaneously.

[0013] Z. Ding et al. (Z. Ding, K. Zhu, Y. Wang, L. Li, M. Liu and T. Zeng, "Spatially Variant Sidelobe Suppression for Linear Array MIMO SAR 3-D Imaging," IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1-15, 2022, Art no. 5220915, doi: 10.1109 / TGRS.2022.3142104.) used pseudo-polar coordinates for imaging to overcome the spatial variability under near-field conditions. However, in the pseudo-polar coordinate system, the imaging spatial variability still exists, and this method does not consider the influence of the spatial variability of the wavenumber spectrum position, resulting in poor sidelobe suppression effect.

[0014] Under near-field conditions, windowing in the aperture domain can effectively suppress the sidelobes of all targets in the imaging results simultaneously. However, this method sacrifices the resolution of the imaging results while suppressing the sidelobes.

[0015] The existing technologies cannot effectively suppress the azimuth and range sidelobes of the near-field radar image simultaneously without sacrificing the image resolution. Summary of the Invention

[0016] The technical problem to be solved by the present invention is to provide a sidelobe suppression method and system for two-dimensional radar images to effectively suppress the azimuth and range sidelobes simultaneously without sacrificing the image resolution in view of the deficiencies of the existing technologies.

[0017] To solve the above technical problem, the technical solution adopted by the present invention is: a sidelobe suppression method for two-dimensional radar images, including the following steps:

[0018] S1. Obtain the image I to be suppressed of side lobes and the corresponding pixel coordinates;

[0019] S2. Multiply the pixel value I(x, y) of the image I to be suppressed of side lobes at each pixel coordinate (x, y) by the phase compensation factor E(x, y) to obtain the spatially variant calibrated image I′; I(x, y) represents the pixel value of the image I to be suppressed of side lobes at the pixel coordinate (x, y), x is the azimuth coordinate of the pixel, and y is the range coordinate of the pixel;

[0020] S3. For each pixel, calculate the corresponding weighted pixel value using the spatially variant calibrated image I′ and

[0021] S4. Use the weighted pixel values and of each pixel to calculate the pixel value g(x, y) after side lobe suppression of each pixel.

[0022] In the present invention, by establishing the correspondence between the wavenumber spectrum and the target position, and then using a two-dimensional cosine basis function with an adaptive weighted region to perform weighting in the wavenumber domain to achieve side lobe suppression, the weighted region of the weighting function is determined by the pixel position. If the image to be suppressed of side lobes is subjected to spatially variant calibration before weighting, so that the wavenumber spectrum centers of all targets are located at the coordinate origin, then the center of the effective weighted region of the two-dimensional cosine basis function is located at the coordinate origin; if the image to be suppressed of side lobes is not processed before weighting, then the center of the effective weighted region of the two-dimensional cosine basis function is determined by the pixel position. The weighting in the wavenumber domain is equivalently implemented by convolution in the image domain. By changing the coefficients of the two-dimensional cosine basis function, different side lobe suppression effects can be obtained. By taking the minimum value of these weighted results pixel by pixel, multi-tapering can be achieved, so as to achieve side lobe suppression without broadening the main lobe. In the present invention, by constraining the variation range of the coefficients of the two-dimensional cosine basis function and solving the minimum value of the weighted image amplitude varying with the coefficients of the two-dimensional cosine basis function, multi-tapering is equivalently achieved, and thus while effectively suppressing the two-dimensional side lobes, the image resolution is not sacrificed.

[0023] The calculation formula for the pixel value I′(x, y) of the spatially variant calibrated image I′ at the pixel coordinate (x, y) is: I′(x, y) = I(x, y)E(x, y); where E(x, y) is the phase compensation factor, or c represents the propagation speed of electromagnetic waves in space, γ represents the adjustment coefficient, taking 0 or 0.9 - 1.2; [f min , f max is the emission signal frequency range corresponding to the image to be suppressed of side lobes, f min represents the minimum emission signal frequency, f maxdenotes the maximum emission signal frequency, and L denotes the azimuth aperture length corresponding to the image to be sidelobe suppressed.

[0024] Calculate the weighted pixel value for each pixel coordinate (x, y) and The calculation formula is:

[0025]

[0026] where P1(x, y), P2(x, y) and P3(x, y) are obtained by interpolating the image I′ after space-variant calibration.

[0027] The calculation formulas for P1(x, y), P2(x, y) and P3(x, y) are:

[0028] If γ = 0,

[0029]

[0030]

[0031] If γ ≠ 0,

[0032]

[0033] B kx (x, y) is the azimuth wavenumber spectrum width corresponding to the pixel coordinate (x, y), B ky (x, y) is the range wavenumber spectrum width corresponding to the pixel coordinate (x, y), φ(x, y) is the wavenumber spectrum deflection angle corresponding to the pixel coordinate (x, y), k c (x, y) is the wavenumber spectrum center distance corresponding to the pixel coordinate (x, y).

[0034] or where

[0035] The calculation formula for the pixel value g(x, y) after sidelobe suppression at the pixel coordinate (x, y) is: g(x, y) = g R (x, y) + jg I (x, y); where Re{·} and Im{·} respectively represent the operations of taking the real part and the imaginary part of the variable, || represents taking the modulus of the variable, and min{} and max{} respectively represent taking the minimum value and the maximum value in the set.

[0036] As an inventive concept, the present invention also provides a sidelobe suppression system for two-dimensional radar images, including a memory, a processor, and a computer program stored on the memory; the processor executes the computer program to implement the steps of the above method.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: while suppressing the two-dimensional sidelobes, the present invention will not broaden the main lobe of the target, and can effectively improve the image quality. Description of the Drawings

[0038] Figure 1 It is a flowchart of the method in an embodiment of the present invention;

[0039] Figure 2 It is the image to be sidelobe-suppressed input in the simulation experiment of an embodiment of the present invention;

[0040] Figure 3 For an embodiment of the present invention Figure 2 The first result after sidelobe suppression;

[0041] Figure 4 For an embodiment of the present invention Figure 2 The second result after sidelobe suppression;

[0042] Figure 5 For the prior method Figure 2 The result after sidelobe suppression. Detailed Embodiments

[0043] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Embodiment 1

[0045] This embodiment provides a method for suppressing sidelobes of two-dimensional radar images, which specifically includes the following steps:

[0046] Step S1: Input the image to be sidelobe-suppressed and measurement parameters

[0047] Input the image I to be sidelobe-suppressed and the coordinates corresponding to each pixel point. I(x, y) represents the image pixel value of the image to be sidelobe-suppressed at the pixel point coordinates (x, y);

[0048] The transmission signal frequency range [fmin, fmax] corresponding to the input image I and the azimuth aperture length L. Here, fmin represents the minimum transmission signal frequency, and fmax represents the maximum transmission signal frequency.

[0049] Step S2: Spatial-variant calibration of wavenumber spectrum position

[0050] Multiply the pixel value I(x, y) of the input image I at each pixel coordinate (x, y) by the phase compensation factor E(x, y) to obtain the spatially-variant calibrated image denoted as I'. The value of I' at the pixel coordinate (x, y) is I'(x, y).

[0051] I'(x, y) = I(x, y)E(x, y) (1)

[0052] E(x, y) is calculated by the following formula:

[0053]

[0054] In the above formula, c represents the propagation speed of electromagnetic waves in space, which is a constant value and its approximate value can be taken; γ represents the adjustment coefficient, and 0 or 0.9 - 1.2 can be taken.

[0055] When γ = 0, for any pixel coordinate (x, y), E(x, y) is 1. At this time, it indicates that this step does not perform any processing on the side lobe suppression image I.

[0056] When γ ≠ 0, this step performs spatial-variant calibration on the input image.

[0057] Step S3: Calculate the wavenumber spectrum parameters corresponding to each pixel

[0058] Use the following formula to calculate the azimuth wavenumber spectrum width B corresponding to each pixel kx (x, y):

[0059]

[0060] Use the following formula to calculate the range wavenumber spectrum width B corresponding to each pixel ky (x, y):

[0061]

[0062] θ max (x, y) and θ min (x, y) are calculated by the following formulas respectively:

[0063]

[0064] Use the following formula to calculate the wavenumber spectrum deflection angle φ(x, y) corresponding to each pixel: ​​

[0065]

[0066] Calculate the center distance \(k\) of the wavenumber spectrum corresponding to each pixel using the following formula c (x, y):

[0067]

[0068] Step S4: Calculate the weighted pixel value of each pixel

[0069] Each pixel corresponds to 3 weighted pixel values and are calculated respectively by the following formulas:

[0070]

[0071]

[0072]

[0073] If \(\gamma = 0\), \(P1(x, y)\), \(P2(x, y)\) and \(P3(x, y)\) in equations (9) - (11) are interpolated and calculated from the image \(I'\) according to the following formulas respectively:

[0074]

[0075] If \(\gamma\neq0\), \(P1(x, y)\), \(P2(x, y)\) and \(P3(x, y)\) in equations (9) - (11) are obtained by interpolation from the image \(I'\) according to the following formulas respectively:

[0076]

[0077]

[0078] Step S5: Calculate the pixel value after sidelobe suppression for each pixel

[0079] For each pixel, calculate its pixel value \(g(x, y)\) after sidelobe suppression using the following formula:

[0080] g(x, y) = g R (x, y) + jg I (x, y) (18)

[0081] In the above formula, g R (x, y) and g I (x, y) represent the real part and the imaginary part respectively, and are calculated respectively by the following formulas:

[0082]

[0083]

[0084] In the above formula, Re{·} and Im{·} respectively represent the operations of taking the real part and the imaginary part of the variable; || represents taking the modulus of the variable; min{} and max{} respectively represent taking the minimum value and the maximum value in the set.

[0085] The process of this embodiment is as follows.

[0086]

[0087]

[0088]

[0089] The interpolation in the above step S4 can be implemented by sinc interpolation. Sinc function interpolation is a well-known technique. For the input image I, for the azimuth position of and the range position of of the pixel point, its pixel value is obtained by sinc interpolation The specific steps are as follows:

[0090]

[0091] In the above formula, Lx and Ly respectively represent the lengths of the interpolation kernel function in the azimuth and range directions, and sinc(x, y) is calculated by the following formula:

[0092]

[0093] In the above formula, δ x and δ y respectively represent the pixel intervals of the image I in the azimuth and range directions.

[0094] The interpolation in step S4 can also be implemented by other methods in addition to the sinc interpolation method. The difference between different methods lies in different precisions.

[0095] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and simulation examples.

[0096] Referring to Figure 1 , the specific implementation steps of the embodiments of the present invention are as follows:

[0097] Step S1: Input the image to be sidelobe suppressed and measurement parameters

[0098] The input image to be sidelobe suppressed is as Figure 2 shown. The number of azimuth pixels of this image is 201, the number of range pixels is 201, the azimuth pixel interval δ x = 1 mm, and the range pixel interval δ y= 1 mm, the azimuth aperture length is L = 0.4 m, the minimum transmit signal frequency fmin = 30 GHz, and the maximum transmit signal frequency fmax = 40 GHz.

[0099] Step S2: Wavenumber spectrum position spatial variability calibration

[0100] Multiply the pixel value I(x, y) of the image I to be sidelobe suppressed at (x, y) by the phase compensation factor E(x, y) calculated by equation (2) to obtain the pixel value I'(x, y) of the spatially calibrated image I' at (x, y), where γ in equation (2) is taken as 0.

[0101] Step S3: Calculate the wavenumber spectrum parameters corresponding to each pixel point.

[0102] Calculate the azimuth wavenumber width B kx (x, y);

[0103] Calculate the range wavenumber width B ky (x, y);

[0104] Calculate the wavenumber spectrum angle φ(x, y) corresponding to the pixel coordinates (x, y) according to equations (3), (5), and (6);

[0105] Calculate the wavenumber spectrum center range k c (x, y).

[0106] Step S4: Calculate the weighted pixel values for each pixel point.

[0107] Each pixel point corresponds to 3 weighted pixel values and

[0108] Calculate the first weighted value at the pixel coordinates (x, y) by interpolation according to equations (9) and (13)

[0109] Calculate the second weighted value at the pixel coordinates (x, y) by interpolation according to equations (10) and (12)

[0110] Calculate the third weighted value at the pixel coordinates (x, y) by interpolation according to equations (11) to (14)

[0111] Step S5: Calculate the sidelobe-suppressed pixel values for each pixel point.

[0112] For the pixel coordinates (x, y), the pixel value g(x, y) after sidelobe suppression is calculated by equation (18);

[0113] The g in equation (18) R (x, y) is calculated by equation (19);

[0114] The g in equation (18) I (x, y) is calculated by equation (20).

[0115] The sidelobe suppression result of the embodiment of the present invention is obtained from the above steps S1 - S5 as Figure 3 shown.

[0116] The sidelobe suppression result of the existing method is as Figure 5 shown.

[0117] Before imaging processing, the existing method first uses a Blackman window for weighting in the aperture domain to suppress azimuth sidelobes, and uses a Blackman window for weighting in the signal frequency domain to suppress range sidelobes.

[0118] Comparing the results of the existing method and the method of the present invention, it can be seen that the existing method will broaden the main lobe of the target while suppressing sidelobes, resulting in a decrease in the resolution of the image; while the method of the embodiment of the present invention will not broaden the main lobe of the target while suppressing two - dimensional sidelobes, and can effectively improve the image quality.

[0119] The following further describes another embodiment of the present invention in detail with reference to the accompanying drawings and simulation examples.

[0120] Refer to Figure 1 , the specific implementation steps of another embodiment of the present invention are as follows:

[0121] Step S1: Input the image to be sidelobe - suppressed and measurement parameters

[0122] The input image to be sidelobe - suppressed is as Figure 2 shown. The number of pixels in the azimuth direction of this image is 201, the number of pixels in the range direction is 201, the pixel interval δ in the azimuth direction x = 1mm, the pixel interval δ in the range direction y = 1mm, the azimuth aperture length is L = 0.4m, the minimum transmitted signal frequency fmin = 30GHz, and the maximum transmitted signal frequency fmax = 40GHz.

[0123] Step S2: Wavenumber spectrum position spatial variability calibration

[0124] Multiply the pixel value I(x, y) of the image I to be sidelobe-suppressed at (x, y) by the phase compensation factor E(x, y) calculated by equation (2) to obtain the pixel value I′(x, y) of the spatially variant calibrated image I′ at (x, y), where γ in equation (2) is taken as 1.

[0125] Step S3: Calculate the wavenumber spectrum parameters corresponding to each pixel point.

[0126] Calculate the azimuth wavenumber width B at the pixel point coordinates (x, y) according to equations (3), (5), and (6) kx (x, y);

[0127] Calculate the range wavenumber width B at the pixel point coordinates (x, y) according to equations (4), (5), and (6) ky (x, y);

[0128] Calculate the wavenumber spectrum angle φ(x, y) at the pixel point coordinates (x, y) according to equations (5), (6), and (7).

[0129] Step S4: Calculate the weighted pixel values of each pixel point.

[0130] Each pixel point corresponds to 3 weighted pixel values and

[0131] Calculate the first weighted value at the pixel point coordinates (x, y) by interpolation according to equations (9) and (16)

[0132] Calculate the second weighted value at the pixel point coordinates (x, y) by interpolation according to equations (10) and (15)

[0133] Calculate the third weighted value at the pixel point coordinates (x, y) by interpolation according to equations (11), (15), (16), and (17)

[0134] Step S5: Calculate the sidelobe-suppressed pixel values of each pixel point.

[0135] For the pixel point coordinates (x, y), calculate its sidelobe-suppressed pixel value g(x, y) by equation (18);

[0136] g in equation (18) R (x, y) is calculated by equation (19);

[0137] g in equation (18) I (x, y) is calculated by equation (20).

[0138] The sidelobe suppression result of the embodiment of the present invention obtained from the above steps S1 - S5 is as follows Figure 4 shown.

[0139] The sidelobe suppression result of the existing method is as follows Figure 5 shown.

[0140] Before imaging processing, the existing method first uses a Blackman window for weighting in the aperture domain to suppress azimuth sidelobes, and uses a Blackman window for weighting in the signal frequency domain to suppress range sidelobes. By comparing the results of the existing method and the method of the embodiment of the present invention, it can be seen that the existing method will broaden the main lobe of the target while suppressing sidelobes, resulting in a decrease in the resolution of the image; while the method of the embodiment of the present invention will not broaden the main lobe of the target while suppressing two - dimensional sidelobes, and can effectively improve the image quality.

[0141] In the embodiment of the present invention, with other calculation formulas unchanged, equation (2) can also be replaced by the following formula:

[0142]

[0143] With other calculation formulas unchanged, equation (3) can also be replaced by the following formula:

[0144]

[0145] With other calculation formulas unchanged, equations (4) and (8) can be respectively replaced by the following formulas:

[0146]

[0147] With other calculation formulas unchanged, equation (19) can also be replaced by the following formula:

[0148]

[0149] With other calculation formulas unchanged, equation (20) can also be replaced by the following formula:

[0150]

[0151] In step S4, P1(x, y), P2(x, y) and P3(x, y) are interpolated from the image I′ according to equations (15) - (17) by using interpolation, and can also be obtained by directly processing the echo signal.

[0152] Embodiment 2

[0153] Embodiment 2 of the present invention provides a system corresponding to Embodiment 1 above. The terminal device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, an industrial control computer, a signal processing board, etc., to execute the method of the above embodiment.

[0154] The terminal device of this embodiment includes a memory, a processor, and a computer program stored on the memory; the processor executes the computer program on the memory to implement the steps of the method in Embodiment 1 above.

[0155] In some implementations, the memory may be a high-speed random access memory (RAM: Random Access Memory), and may also include non-volatile memory, such as at least one disk memory.

[0156] In other implementations, the processor may be various types of general-purpose processors such as a central processing unit (CPU) or a digital signal processor (DSP), which is not limited here.

[0157] Embodiment 3

[0158] Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the steps of the method in Embodiment 1 above are implemented.

[0159] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above.

[0160] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present application may be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0161] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or steps for implementing the functions specified in multiple blocks.

[0163] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0164] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and variations.

Claims

1. A sidelobe suppression method for two-dimensional radar images, characterized in that: The following steps are involved: S1, obtaining the image I to be sidelobe suppressed and the corresponding pixel coordinates; S2, multiplying the pixel value I(x, y) of the image I to be sidelobe suppressed at each pixel coordinate (x, y) by the phase compensation factor E(x, y) to obtain the image I′ after space-variant calibration; I(x, y) represents the pixel value of the image I to be sidelobe suppressed at the pixel coordinate (x, y), x is the azimuth coordinate of the pixel, and y is the distance coordinate of the pixel; S3. For each pixel, use the space-variant calibrated image I' to calculate the corresponding weighted pixel value. and S4, using the weighted pixel value of each pixel and Calculate the pixel value g(x,y) after sidelobe suppression of each pixel; The calculation formula of the pixel value I′(x,y) of the image I′ after space-variable calibration at the pixel point coordinate (x,y) is: I′(x,y)=I(x,y)E(x,y); wherein E(x,y) is the phase compensation factor, or c represents the propagation speed of electromagnetic waves in space, γ represents the adjustment coefficient, which is 0 or 0.9 to 1.2; [f min ,f max ] is the frequency range of the transmitted signal corresponding to the image to be sidelobe suppressed, f min Indicates the minimum transmitted signal frequency, f max represents the maximum transmitted signal frequency, L represents the azimuth aperture length corresponding to the image to be sidelobe suppressed; The weighted pixel value at the pixel point coordinate (x, y) and The calculation formula is: Among them, P1(x, y), P2(x, y) and P3(x, y) are calculated by interpolation of the image I′ after space-variant calibration; The calculation formulas for P1(x,y), P2(x,y) and P3(x,y) are: If γ=0, If γ≠0, B kx (x, y) is the azimuthal wavenumber spectrum width corresponding to the pixel coordinate (x, y), B ky (x,y) is the distance wavenumber spectrum width corresponding to the pixel coordinate (x,y), φ(x,y) is the wavenumber spectrum deflection angle corresponding to the pixel coordinate (x,y), k c (x,y) is the center distance of the wavenumber spectrum corresponding to the pixel coordinate (x,y).

2. The sidelobe suppression method for two-dimensional radar images according to claim 1, characterized in that: P1(x,y), P2(x,y) and P3(x,y) are calculated by sinc interpolation from the space-variant calibrated image I′.

3. The sidelobe suppression method for two-dimensional radar images according to claim 1, characterized in that: or or or in 4. The sidelobe suppression method for two-dimensional radar images according to any one of claims 1 to 3, characterized in that: The calculation formula of the pixel value g(x,y) after sidelobe suppression at the pixel point coordinate (x,y) is: g(x,y) = g R (x,y)+jg I (x,y); where or or Re{·} and Im{·} represent the real and imaginary part operations of the variable respectively, || represents the modulus of the variable, min{} and max{} represent the minimum and maximum values ​​in the set respectively.

5. A sidelobe suppression system for two-dimensional radar images, comprising a memory, a processor, and a computer program stored in the memory; characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

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