Rapid time domain imaging method for micro platform synthetic aperture radar

Abstract

The invention relates to the technical field of radar imaging, in particular to a rapid time domain imaging method for a micro-platform synthetic aperture radar, and the method comprises the steps: constructing a coarse-resolution rectangular coordinate system grid based on the geometric features of an observation scene; carrying out local coarse focusing processing by adopting a sub-aperture segmentation strategy to form a plurality of coarse images; performing equal-interval zero-padding up-sampling operation on each coarse image so as to reserve high-frequency information and recover the spectrum integrity of sub-aperture imaging, and obtaining a plurality of coarse images after up-sampling; carrying out image coherence superposition on two adjacent upsampled coarse images by using a coherence superposition criterion to realize effective accumulation of azimuth resolution, and updating coefficients related to coherence superposition; and carrying out next-level image coherence stacking based on the updated coefficients related to coherence stacking, carrying out full-resolution image reconstruction step by step, and finally obtaining a high-resolution imaging result image. According to the method, rapid time domain imaging of the miniature platform synthetic aperture radar is successfully realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of radar imaging technology, and in particular to a fast time-domain imaging method for a miniature synthetic aperture radar. Background Art

[0002] Compared to traditional airborne and spaceborne SAR systems, micro-platform SAR (Synthetic Aperture Radar) offers significant advantages in deployment flexibility, mobility, and system cost-effectiveness. By utilizing the synthetic aperture formed by a mobile platform, micro-platform SAR systems achieve high resolution in azimuth, making them particularly suitable for near-surface high-resolution observation scenarios such as urban environmental monitoring, complex terrain mapping, and emergency response.

[0003] Current micro-platform SAR systems generally operate in a wide-beam mode, resulting in target echo signals exhibiting a coherent superposition of scattering characteristics across a wide range of angles. This places higher demands on imaging algorithms. Technicians typically employ frequency-domain algorithms such as RDA (Range Doppler Algorithm) and RMA (Range Migration Algorithm), as well as time-domain algorithms such as Back Projection Algorithm (BPA) for micro-platform SAR imaging.

[0004] The core concept of RDA is to address range migration in radar echoes through pulse compression technology and to achieve azimuth focusing using Doppler frequency shift information. The core concept of RMA is to accurately correct range migration through phase compensation and Stolt interpolation in the wavenumber domain (two-dimensional frequency domain). BPA generates high-resolution images by coherently stacking radar echo data pixel by pixel. Unlike RDA and RMA, BPA does not assume uniform linear motion of the platform and is highly robust to complex trajectories and motion errors. It is particularly suitable for motion scenarios with non-ideal platforms, such as vehicle-mounted SAR and drone-mounted SAR.

[0005] However, the imaging algorithm of frequency domain processing faces two technical bottlenecks in the wide beam imaging scenario of micro-platform SAR. First, the non-stationary phase characteristics contained in the wide beam echo signal are difficult to be effectively characterized by the plane wave approximation model, which causes the imaging algorithm of frequency domain processing to produce residual phase errors during the motion compensation process, thereby affecting the imaging quality. Second, the space-varying motion error caused by the unstructured platform trajectory cannot be globally corrected in a unified frequency domain operator, which seriously restricts the theoretical limit of the image azimuth resolution. The imaging algorithm of time domain processing circumvents the model mismatch problem by point-by-point coherent accumulation, but its inherent O(N 3The calculation complexity of the traditional BPA (N represents the imaging grid dimension) seriously restricts the engineering practicability. The improved scheme of the fast BPA realizes a certain degree of acceleration through the block processing or data compression strategy, but still faces the dilemma of the operation efficiency and the imaging quality when processing wide-beam large-scene data, and cannot meet the imaging requirements of the micro-platform SAR. SUMMARY

[0006] Therefore, the embodiment of the present application proposes a fast time-domain imaging method for a micro-platform synthetic aperture radar, aiming to improve the calculation rate of the traditional BPA through a sub-aperture hierarchical processing mechanism, while keeping the imaging quality equivalent to that of the traditional BPA in terms of the key indicators such as the -3dB main lobe width and the integrated side lobe ratio, and successfully realizing the fast time-domain imaging of the micro-platform synthetic aperture radar.

[0007] To achieve the above-mentioned purpose, the embodiment of the present application proposes a fast time-domain imaging method for a micro-platform synthetic aperture radar, which comprises the following steps: constructing a coarse-resolution rectangular coordinate system grid based on the geometric characteristics of an observed scene; performing local coarse focusing processing by using a sub-aperture segmentation strategy to form a plurality of coarse images; performing an equal-interval zero-padding upsampling operation on each coarse image to retain high-frequency information and restore the spectral integrity of sub-aperture imaging, to obtain a plurality of upsampled coarse images; performing image coherent superposition on adjacent two upsampled coarse images by using a coherent superposition criterion to realize effective accumulation of the azimuth resolution, and updating a coefficient related to the coherent superposition; performing image coherent superposition of the next level based on the updated coefficient related to the coherent superposition, and gradually performing full-resolution image reconstruction, to finally obtain a high-resolution imaging result image.

[0008] To achieve the above-mentioned purpose, the embodiment of the present application also proposes a fast time-domain imaging system for a micro-platform synthetic aperture radar, which comprises: a grid construction module configured to construct a coarse-resolution rectangular coordinate system grid based on the geometric characteristics of an observed scene; a coarse image generation module configured to perform local coarse focusing processing by using a sub-aperture segmentation strategy to form a plurality of coarse images; an equal-interval zero-padding upsampling module configured to perform an equal-interval zero-padding upsampling operation on each coarse image to retain high-frequency information and restore the spectral integrity of sub-aperture imaging, to obtain a plurality of upsampled coarse images; a coherent superposition module configured to perform image coherent superposition on adjacent two upsampled coarse images by using a coherent superposition criterion to realize effective accumulation of the azimuth resolution, and update a coefficient related to the coherent superposition; and a full-resolution reconstruction module configured to instruct the coherent superposition module to perform image coherent superposition of the next level based on the updated coefficient related to the coherent superposition, to gradually perform full-resolution image reconstruction, to finally obtain a high-resolution imaging result image and output the high-resolution imaging result image.

[0009] In order to achieve the above-mentioned purpose, an embodiment of the present application also proposes an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a fast time domain imaging method for a micro-platform synthetic aperture radar as described above.

[0010] In order to achieve the above-mentioned purpose, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement a fast time domain imaging method for a micro-platform synthetic aperture radar as described above.

[0011] Optionally, a coarse-resolution rectangular coordinate grid is constructed based on the geometric characteristics of the observation scene, including:

[0012] The echo signal of the micro-platform synthetic aperture radar is demodulated and range compressed. The signal obtained after processing is expressed by the formula:

[0013] s(x,r)=Asinc[K B (rr p )]exp(-jK C r p );

[0014] K B =K max -K min ;

[0015] K C =(K max +K min ) / 2;

[0016] Where A is a complex constant, which is determined by the complex target reflectivity and free space propagation attenuation coefficient at point p and does not affect the imaging process. r represents the range axis of the micro-platform synthetic aperture radar, and K B is the distance-dimensional wavenumber bandwidth, K max is the maximum wave number, K min is the minimum wave number, K C is the central wave number, sinc(·) is the Sinker function formed after distance compression, and s(x,r) represents the signal obtained after processing;

[0017] BPA performs coherent accumulation of echo signals in the entire aperture, and the obtained complex image pixel I(x i ,y i ) is expressed by the formula:

[0018]

[0019] where r start represents the start distance of the imaging area, r end represents the end distance of the imaging area, r i represents the distance between each pixel point of the complex image and the micro-platform synthetic aperture radar, and L is the length of s(x, r);

[0020] The two-dimensional wave number spectrum range K x and K y of the image obtained according to the stationary phase theorem after the two-dimensional Fourier transform are calculated by the following formula:

[0021] θ = arcsin[(x-x i ) / r i ];

[0022] K ,ax sinθ min ≤ K x ≤ K max sinθ max ;

[0023] K min cosθ s ≤ K y ≤ K max cosθ s ;

[0024] where θ s represents the radar beam squint angle, θ min and θ max respectively represent the minimum value and the maximum value of the squint angle between the pixel point and the micro-platform synthetic aperture radar;

[0025] The boundary values δ x and δ y of the two-dimensional sampling interval of the BP image are calculated by the following formula:

[0026] δ x ≤ 1 / [2K max sin(θ b / 2)];

[0027] δ y ≤ 1 / K b cosθ s ;

[0028] where θ b represents the radar beam width;

[0029] The radar wave bandwidth is calculated by the sub-aperture length, the size before zero padding, i.e. the size of the largest grid interval before zero padding, and then the actual grid interval is set to be γ times the size before zero padding according to the radar requirements.

[0030] Optionally, a local coarse focusing processing is performed by using a sub-aperture segmentation strategy to form a plurality of coarse images, including:

[0031] The two-way distance of each grid point to each radar position is calculated and converted into a time delay, and the signal value of the corresponding position is extracted from the processed signal;

[0032] The compensation phase is calculated according to the distance value of each grid point to each radar position by the following formula:

[0033] ρ=exp(-jK C r i );

[0034] Wherein, ρ is the compensation phase;

[0035] The signal values of different radar positions of the same grid point are multiplied by the corresponding compensation phase values respectively and then coherently superimposed to finally form a plurality of coarse images.

[0036] Optionally, an equal-interval zero-padding upsampling operation is performed on each coarse image to retain high-frequency information and restore the spectral integrity of the sub-aperture imaging, to obtain a plurality of upsampled coarse images, including:

[0037] The azimuth dimension of the coarse image is upsampled by inserting zero values, and the azimuth signal of a certain distance unit of the upsampled coarse image is expressed by the formula:

[0038]

[0039] x i =0,1,2,…,N-1;

[0040] Wherein, I zero (x i ′,y0) is the azimuth signal of a certain distance unit of the upsampled coarse image, y0 represents the index of a certain distance unit of the upsampled coarse image, and N-1 is the new signal length;

[0041] The azimuth Fourier transform of I zero (x i ′,y0) is performed by the following formula:

[0042]

[0043] Wherein, n is both an interpolation factor and a compression factor, due to the periodicity of the discrete Fourier transform, the periodic extension of the wave number spectrum of the upsampling coarse image is also compressed to n times of the original.

[0044] Optionally, n = 2, that is, the wave number spectrum of the upsampling coarse image is 2 times of the original.

[0045] Optionally, the coherent stacking criterion is applied to the adjacent two upsampling coarse images to realize effective accumulation of the azimuth resolution, and the coefficients related to the coherent stacking are updated, including:

[0046] The wave number spectrum of the upsampling coarse image is compressed, that is, multiplied by the wave number spectrum compression factor in the time domain, and the azimuth wave number spectrum of the sub-aperture is in the vicinity of zero frequency and periodic zero frequency after compression.

[0047] The wave number spectrum compression factor is calculated by the following formula:

[0048] F C =exp(-jK C r i )×exp(-jK C y i );

[0049] Wherein, F C represents the wave number spectrum compression factor.

[0050] The wave number spectrum of each sub-aperture is low-pass filtered to remove the wave number spectrum in the vicinity of the periodic zero frequency, and the correct sub-aperture wave number spectrum is obtained, and the low-pass filtering is realized by a low-pass filter.

[0051] Half zero is added to the correct sub-aperture wave number spectrum around zero frequency, so that the data length is doubled, and after the azimuth inverse Fourier transform, the data is returned to the time domain, and the azimuth upsampling is completed.

[0052] The wave number spectrum of the upsampling echo signal is decompressed based on the decompression coefficient to restore the wave number spectrum, and the decompressed sub-aperture image is obtained.

[0053] The decompression coefficient is calculated by the following formula:

[0054] F′ C =exp(jK C r i )×exp(jK C y i );

[0055] Wherein, F′ C represents the decompression coefficient.

[0056] The coherent superposition is performed on every two decompressed sub-aperture images to realize effective accumulation of azimuth resolution, to obtain a superposed image, and to update the wave number spectrum compression factor and the decompression coefficient.

[0057] Optionally, the next-stage image coherent superposition is performed based on the updated coefficient related to the coherent superposition, and full-resolution image reconstruction is performed step by step, to finally obtain a high-resolution imaging result image, including:

[0058] Based on the updated wave number spectrum compression factor and the decompression coefficient, the wave number spectrum compression, low-pass filtering, azimuth upsampling, wave number spectrum decompression, coherent superposition, and updating of the wave number spectrum compression factor and the decompression coefficient are repeated, full-resolution image reconstruction is performed step by step, until only one image is left, that is, the final high-resolution imaging result image is obtained.

[0059] The application provides a fast time-domain imaging method for a micro-platform synthetic aperture radar. In view of the technical problem that the calculation amount of a traditional fast BPA in a rectangular coordinate system is greatly affected by a grid interval, a coarse-resolution rectangular coordinate system grid is constructed based on geometric features of an observed scene, a sub-aperture segmentation strategy is used for local coarse focusing processing, a plurality of coarse images are formed, and an equal-interval zero-padding upsampling operation is performed on each coarse image, so that the equivalent interval of the grid is increased, the calculation amount during coarse image generation is reduced, and the total calculation amount is effectively reduced. After the grid is zero-padded, a periodic spectrum exists in the azimuth wave number spectrum, and therefore the periodic spectrum is compressed and gathered to zero frequency or a periodic zero frequency by using a wave number spectrum compression operation. After a series of operations, the coherent superposition criterion is used to perform image coherent superposition on two adjacent upsampling coarse images, the azimuth resolution is effectively accumulated, the coefficient related to the coherent superposition is updated, the next-stage image coherent superposition is performed based on the updated coefficient related to the coherent superposition, full-resolution image reconstruction is performed step by step, and finally a high-resolution imaging result image is obtained. The method increases the calculation rate of the traditional BPA, maintains the imaging quality of the traditional BPA in key indicators such as a -3dB main lobe width and an integral sidelobe ratio, and successfully realizes fast time-domain imaging of the micro-platform synthetic aperture radar. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the application or the related art, the drawings needed to be used in the description of the embodiments of the application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0061] Figure 1is a flow chart of a fast time domain imaging method for a micro-platform synthetic aperture radar provided in an embodiment of the present application;

[0062] Figure 2 is a schematic diagram of a specific algorithm flow provided in an embodiment of the present application;

[0063] Figure 3 is a schematic diagram of an equal-interval zero-padding operation provided in an embodiment of the present application;

[0064] Figure 4 is a schematic diagram of changes in images before and after upsampling provided in an embodiment of the present application;

[0065] Figure 5 is a schematic diagram of changes in images after wave number spectrum compression, low-pass filtering and wave number spectrum decompression provided in an embodiment of the present application;

[0066] Figure 6 is a schematic diagram of changes in images after coherent superposition provided in an embodiment of the present application;

[0067] Figure 7 is a high-resolution imaging result and an algorithm comparison diagram provided in an embodiment of the present application;

[0068] Figure 8 is a schematic diagram of a measured data result provided in an embodiment of the present application;

[0069] Figure 9 is a structural schematic diagram of a fast time domain imaging system for a micro-platform synthetic aperture radar provided in another embodiment of the present application;

[0070] Figure 10 is a structural schematic diagram of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0071] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that, in the embodiments of the present application, many technical details are presented in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be implemented even without these technical details and based on various changes and modifications of the following embodiments. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the embodiments can be combined and referenced with each other on the premise of no contradiction.

[0072] First, RDA, RMA and BPA are introduced in detail.

[0073] RDA is a classic signal processing algorithm in SAR imaging. Its core concept is to address the range migration problem in radar echoes through pulse compression techniques and utilize Doppler frequency shift information to achieve azimuth focusing. The general steps are as follows: First, pulse compression is performed on the linear frequency modulation echo signal received by the radar, and a matched filter is used to improve range resolution. Next, a range migration model is established based on the relative motion between the radar and the target. Interpolation or phase multiplication is performed in the range-Doppler domain to ensure that target energy is aligned within the same range unit. Finally, using the approximate Doppler frequency domain signal model of the echo signal, matched filtering is performed in azimuth to achieve azimuth focusing.

[0074] RDA has fast calculation speed, small calculation amount and simple processing, but it is only applicable to far-field situations. In near-field situations, its approximate model will fail, the focusing effect will deteriorate, and it cannot meet the needs of vehicle-mounted SAR.

[0075] RMA, also known as the wavenumber domain algorithm or Ω-K algorithm, is a high-precision frequency-domain processing algorithm for synthetic aperture radar imaging. Its core concept is to accurately correct for range migration through phase compensation and Stolt interpolation in the wavenumber domain (two-dimensional frequency domain). The general steps are as follows: First, the raw radar echo data is converted from the time domain (range-azimuth) to the two-dimensional frequency domain (wavenumber domain) using a two-dimensional Fast Fourier Transform (FFT), while preserving the phase information in both range and azimuth. Next, in the wavenumber domain, the space-varying phase error caused by radar platform motion is compensated by multiplying it by a reference function (reference phase), preliminarily correcting for range curvature. Stolt interpolation is then used to map the signal from a non-uniform wavenumber domain grid to a uniform grid to eliminate range curvature, thereby accurately correcting for range migration. Finally, a two-dimensional inverse Fourier transform is used to convert the data into the time domain to generate a focused SAR image.

[0076] RMA offers fast computational speed and moderate computational complexity. However, when applied to near-field conditions, the wide beamwidth causes a nonlinear enhancement in the range migration curve of the echo signal. This invalidates the linear mapping assumption of Stolt interpolation, severely impacting the focusing effect. Furthermore, the motion instability of vehicle-mounted SAR introduces time-varying phase errors, disrupting the phase consistency of the wavenumber-domain signal. This makes Stolt interpolation incapable of accurately correcting for range migration, ultimately leading to image defocus or geometric distortion.

[0077] BPA is a time-domain processing algorithm used in SAR imaging. It generates high-resolution images by coherently stacking radar echo data pixel by pixel. Unlike frequency-domain algorithms (RDA and RMA), BPA does not assume uniform linear motion of the platform and is robust to complex trajectories and motion errors. It is particularly suitable for scenarios with non-ideal platform motion (such as vehicle-mounted and drone-mounted SAR). The general steps are as follows: First, pulse compression (matched filtering) is performed on the raw echo data to improve range resolution. Next, the target area is divided into a regular two-dimensional pixel grid. Distance calculation, time delay correction and phase compensation are performed, and coherent stacking is performed to complete pixel-by-pixel back-projection. Once all pixels have been traversed, the SAR image is output.

[0078] BPA has high robustness to motion errors, no approximation, and supports multiple geometric models such as large squint and front view. However, its computational complexity is high, and the currently proposed BPA cannot meet the needs of vehicle-mounted SAR.

[0079] To overcome the trade-off between computational efficiency and imaging quality, one embodiment of the present application proposes a fast time-domain imaging method for a micro-platform synthetic aperture radar, which is applied to an electronic device, where the electronic device can be a terminal or a server. This embodiment and the following embodiments are described using a server as an example. The implementation details of the fast time-domain imaging method for a micro-platform synthetic aperture radar proposed in this embodiment are described in detail below. The following content is merely provided for ease of understanding and is not required for implementing this solution.

[0080] The specific process of the fast time domain imaging method for micro-platform synthetic aperture radar proposed in this embodiment can be as follows: Figure 1 The details of the entire algorithm are shown in Figure 2 As shown, the method includes:

[0081] Step 11: Construct a coarse-resolution rectangular coordinate grid based on the geometric features of the observation scene.

[0082] In the specific implementation, the server first needs to construct a coarse-resolution rectangular coordinate grid based on the geometric characteristics of the observation scene, that is, to perform sub-aperture division and grid interval selection.

[0083] In one example, when the server constructs a coarse-resolution rectangular coordinate grid based on the geometric features of the observation scene, it first needs to demodulate and perform range compression processing on the echo signal of the micro-platform synthetic aperture radar. The resulting signal is expressed by the formula:

[0084] s(x,r)=Asinc[K B (rr p )]exp(-jK C rp );

[0085] K B =K max -K min ;

[0086] K C =(K max +K min ) / 2;

[0087] where A is a complex constant determined by the complex target reflectivity of point p and the free space propagation attenuation coefficient, which does not affect the imaging process, r represents the range axis of the micro-platform synthetic aperture radar, K B is the distance dimension wave number bandwidth, K max is the maximum wave number, K min is the minimum wave number, K C is the central wave number, and sinc(·) is the sinc function formed after distance compression, and s(x, r) represents the signal obtained after processing.

[0088] Subsequently, the BPA coherently accumulates the echo signal in the entire aperture, and the complex image pixel point I(x i ,y i ) obtained is expressed by the formula as follows:

[0089]

[0090] where r staTt represents the starting distance of the imaging area, r end represents the ending distance of the imaging area, r i represents the distance between the micro-platform synthetic aperture radar and each pixel point of the complex image, and L is the length of s(x, r).

[0091] Next, the two-dimensional wave number spectrum range K x and K y of the image obtained according to the stationary phase theorem after two-dimensional Fourier transform, and the squint angle θ between the pixel point and the micro-platform synthetic aperture radar are calculated by the following formula:

[0092] θ=arcsin[(x-x i ) / r i ];

[0093]

[0094] K min cosθ s ≤K y ≤K max cosθ s ;

[0095] where θ s represents the radar beam squint angle, θ min and θ max respectively represent the minimum and maximum values of the squint angle between the pixel point and the micro-platform synthetic aperture radar.

[0096] After that, the boundary value δ x and δ y of the two-dimensional sampling interval of the BP image is calculated by the following formula:

[0097] δ x ≤1 / [2K max sin(θ b / 2)];

[0098] δ y ≤1 / K b cosθ s ;

[0099] where θ b represents the radar beam width.

[0100] Finally, the radar wave number bandwidth is calculated by the sub-aperture length, the size before zero padding, that is, the size of the largest grid interval before zero padding, and then the actual grid interval is set to be γ times the size before zero padding according to the radar requirements.

[0101] Step 12, local coarse focusing processing is performed by using the sub-aperture segmentation strategy to form multiple coarse images.

[0102] In a specific implementation, after the server completes the sub-aperture division and grid interval selection, local coarse focusing processing is performed by using the sub-aperture segmentation strategy to form multiple coarse images.

[0103] In one example, the server first needs to calculate the two-way distance from each grid point to each radar position and convert it into time delay, and extract the signal value corresponding to the position from the processed signal.

[0104] Next, the compensation phase is calculated according to the distance value from each grid point to each radar position by the following formula:

[0105] ρ=exp(-jK C r i );

[0106] where ρ is the compensation phase.

[0107] Finally, the signal values of different radar positions of the same grid point are respectively multiplied by the corresponding compensation phase values and coherently superimposed to finally form multiple coarse images.

[0108] Step 13, equal-interval zero-padding upsampling operation is performed on each coarse image to retain high-frequency information and restore spectral integrity of sub-aperture imaging, to obtain a plurality of upsampling coarse images.

[0109] In a specific implementation, after obtaining the plurality of coarse images, the server needs to perform equal-interval zero-padding upsampling operation on each coarse image to retain high-frequency information and restore spectral integrity of sub-aperture imaging, so as to obtain a plurality of upsampling coarse images.

[0110] In one example, the equal-interval zero-padding operation can be as shown in FIG. 13. Figure 3 The server upsamples the azimuth dimension of the coarse image by inserting zero values, and sets the interpolation factor as n. The azimuth signal of a certain range unit of the upsampling coarse image is expressed by the following formula:

[0111]

[0112] x i = 0, 1, 2, …, N-1;

[0113] wherein, I zero (x i ', y0) is the azimuth signal of a certain range unit of the upsampling coarse image, y0 represents the index of a certain range unit of the upsampling coarse image, and N-1 is the new signal length.

[0114] Next, the azimuth Fourier transform is performed on I zero (x i ', y0) by the following formula:

[0115]

[0116] wherein, n is both the interpolation factor and the compression factor. Due to the periodicity of the discrete Fourier transform, the period of the wave number spectrum of the upsampling coarse image is also compressed to n times of the original.

[0117] In one example, n = 2, that is, the wave number spectrum of the upsampling coarse image is 2 times of the original.

[0118] In one example, the change of the image before and after upsampling can be as shown in FIG. 14. Figure 4

[0119] Step 14, image coherence stacking is performed on the adjacent two upsampling coarse images by using the coherent stacking criterion to realize effective accumulation of azimuth resolution, and the coefficients related to the coherent stacking are updated.

[0120] ​In a specific implementation, after the equal-interval zero-padding upsampling operation is completed, the server applies the coherent stacking criterion to the adjacent two upsampling coarse images to perform image coherent stacking, realizes effective accumulation of azimuth resolution, and updates the coefficients related to coherent stacking. The coherent stacking mentioned here is a relatively broad concept, and the specific process includes wave number spectrum compression, low-pass filtering, azimuth upsampling, wave number spectrum decompression, and coherent stacking.

[0121] In one example, the server first needs to compress the wave number spectrum of the upsampling coarse image, that is, multiply the wave number spectrum by a wave number spectrum compression factor in the time domain. After the wave number spectrum of the sub-aperture is compressed, it is near the zero frequency and the periodic zero frequency, as shown in Figure 5 The wave number spectrum is successfully compressed to near the zero frequency and the periodic zero frequency.

[0122] The wave number spectrum compression factor can be calculated by the following formula:

[0123] F C =exp(-jK C r i )×exp(-jK C y i );

[0124] Wherein, F C represents the wave number spectrum compression factor.

[0125] Next, the server needs to perform low-pass filtering on the wave number spectrum of each sub-aperture to remove the wave number spectrum near the periodic zero frequency, thereby obtaining the correct sub-aperture wave number spectrum. The low-pass filtering is realized through a low-pass filter, as shown in Figure 5 After low-pass filtering, the 0 value part in the original time domain image is no longer 0.

[0126] The passband frequency of low-pass filtering is determined by the following formula:

[0127] f p ∈[-F x / 2,F x / 2];

[0128] Wherein, f p represents the passband frequency of low-pass filtering.

[0129] Subsequently, the correct sub-aperture wave number spectrum is supplemented with a half of zero around the zero frequency, so that the data length becomes twice the original length. After inverse Fourier transform in the azimuth direction, the data returns to the time domain, completes the azimuth upsampling, and the upsampling multiple is 2.

[0130] The wave number spectrum of the upsampling echo signal is decompressed based on the decompression coefficient, so that the wave number spectrum is restored, and the decompressed sub-aperture image is obtained, as shown in Figure 5As shown, after the wave number spectrum decompression, the wave number spectrum is recovered and the frequency domain range becomes 4 times of the original one, which is caused by the 2 times zero insertion and 2 times upsampling.

[0131] The decompression coefficient is calculated by the following formula:

[0132] F′ C = exp(jK C r i ) x exp(jK C y i );

[0133] wherein F′ C represents the decompression coefficient.

[0134] Finally, the server coherently stacks every two decompressed sub-aperture images, as shown in FIG. 6B. The azimuth bandwidth of the two sub-aperture images is stacked, and the image interval becomes half of the original one, which realizes the effective accumulation of the azimuth resolution, thereby obtaining the stacked image and updating the wave number spectrum compression factor and the decompression coefficient. Figure 6

[0135] Step 15, based on the updated coefficients related to the coherent stacking, the next level of image coherent stacking is performed, and the full resolution image reconstruction is performed step by step, and finally the high resolution imaging result image is obtained.

[0136] In a specific implementation, the server will perform the next level of image coherent stacking based on the updated coefficients related to the coherent stacking, and perform the full resolution image reconstruction step by step, and finally obtain the high resolution imaging result image.

[0137] In one example, based on the updated wave number spectrum compression factor and decompression coefficient, the server starts to repeat the wave number spectrum compression, low-pass filtering, azimuth upsampling, wave number spectrum decompression, coherent stacking, and updating the wave number spectrum compression factor and decompression coefficient operations, and performs the full resolution image reconstruction step by step, until only one image is left, that is, the final high resolution imaging result image is obtained.

[0138] In one example, the final high resolution imaging result image is as shown in FIG. 6C. Figure 7

[0139] ​​The embodiment provides a fast time domain imaging method for a micro-platform synthetic aperture radar, and aims at the technical problem that the calculation amount of a traditional fast BPA in a rectangular coordinate system is greatly affected by a grid interval.

[0140] The step division of the above method is only for the purpose of clear description, and in actual implementation, one step can be combined or some steps can be split and decomposed into multiple steps, as long as the same logical relationship is included, and all fall within the protection scope of the application. Adding an insignificant modification or introducing an insignificant design in an algorithm or a flow, but not changing the core design of the algorithm and the flow, all fall within the protection scope of the application.

[0141] In one embodiment, in order to verify the performance of the method (hereinafter referred to as CZBPA) provided in the application, theoretical simulation and real measured track SAR data experiments are performed. The selected comparison algorithms are RDA, BPA and a rectangular coordinate multi-stage backward projection algorithm (CFBPA).

[0142] The simulation experiment result is shown in Figure 7 As shown in the table, the two-dimensional sinc graph of the CZBPA is consistent with the BPA and the CFBPA while ensuring the same resolution, and the second sidelobe is about 2 dB higher than the BPA, but is still more than 10 dB lower than the main lobe, meeting the SAR imaging requirement. Figure 8As shown, real data collection is carried out on a wide roof by the rail SAR system. By comparing the imaging result maps, the imaging effect of RDA is poor, while that of CZBPA is consistent with that of BPA. The metal object in the roof background, the edge wall and the target sphere are all clearly visible. Under the same imaging effect, the imaging rate of CZBPA is 75 times that of BPA and 1.61 times that of CFBPA. Therefore, CZBPA is superior to the existing imaging algorithms in terms of algorithm speed.

[0143] The imaging effect of CZBPA meets the requirements, can cope with non-ideal motion routes and has good real-time imaging capability. Combined with hardware acceleration architecture (such as FPGA, GPU parallel computing), lightweight signal processing model and other technical paths, the imaging time is significantly shortened, and the image quality is effectively guaranteed. Its application prospect is particularly outstanding in the following scenarios:

[0144] First, the field of autonomous driving. CZBPA can realize sub-second ground reconstruction and obstacle detection. For example, when a vehicle is driving in heavy rain or dense fog, SAR can penetrate through the bad weather, combine with the real-time generated centimeter-level resolution image, quickly identify the road potholes, water accumulation areas or sudden obstacles (such as fallen trees), and analyze the speed vector of the surrounding vehicles through Doppler information, to provide reliable basis for emergency braking or lane changing decision.

[0145] Second, the field of military. An unmanned reconnaissance vehicle needs to be hidden in a battlefield environment to perform tasks. The vehicle-mounted SAR can update the image at a millisecond level to identify disguised targets (such as armored vehicles covered with vegetation) or temporary roadblocks in real time while moving.

[0146] Third, the field of disaster emergency rescue. A miniature platform SAR can quickly scan the collapsed building ruins, identify the vital signs of micro-movement signals (such as chest fluctuation caused by breathing) through penetration imaging, and locate the position of the survivors within a few seconds by combining with deep learning algorithms, thereby greatly improving the search and rescue efficiency within the "golden 72 hours".

[0147] Correspondingly, another embodiment of the present application proposes a fast time domain imaging system for a miniature platform synthetic aperture radar. The details of the fast time domain imaging system for a miniature platform synthetic aperture radar proposed in this embodiment are described below. The following content only provides related implementation details for easy understanding and is not necessary for implementing this embodiment. Figure 9 Fig. 1 is a structural schematic diagram of the fast time domain imaging system for a miniature platform synthetic aperture radar proposed in this embodiment, which comprises a grid construction module 21, a coarse image generation module 22, an equal-interval zero-padding upsampling module 23, a coherent superposition module 24 and a full-resolution reconstruction module 25.

[0148] The grid construction module 21 is configured to construct a coarse resolution rectangular coordinate system grid based on the geometric features of the observed scene.

[0149] A coarse image generation module 22 is configured to perform local coarse focusing processing by using a sub-aperture segmentation strategy to generate a plurality of coarse images.

[0150] An equal-interval zero-padding upsampling module 23 is configured to perform equal-interval zero-padding upsampling operation on each coarse image to preserve high-frequency information and recover spectral integrity of sub-aperture imaging, to obtain a plurality of upsampled coarse images.

[0151] A coherent superposition module 24 is configured to perform image coherent superposition on two adjacent upsampled coarse images according to a coherent superposition criterion to effectively accumulate azimuth resolution, and update a coefficient related to coherent superposition.

[0152] A full-resolution reconstruction module 25 is configured to instruct the coherent superposition module 24 to perform next-level image coherent superposition based on the updated coefficient related to coherent superposition, to gradually perform full-resolution image reconstruction, and finally obtain a high-resolution imaging result image and output the same.

[0153] It can be found that the embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and the embodiment can be implemented in cooperation with the above-mentioned method embodiment. The related technical details and technical effects mentioned in the above-mentioned method embodiment are still valid in the embodiment. In order to reduce repetition, they will not be described here. Correspondingly, the related technical details mentioned in the embodiment can also be applied to the above-mentioned method embodiment.

[0154] It is worth mentioning that each module involved in the embodiment is a logical module. In actual application, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present application, units not closely related to solving the technical problems proposed in the present application are not introduced in the embodiment, but this does not mean that there are no other units in the embodiment.

[0155] Another embodiment of the present application provides an electronic device, which can have a specific structure as shown in Figure 10 The electronic device includes at least one processor 31 and a memory 32 communicatively connected to the at least one processor 31. The memory 32 stores instructions executable by the at least one processor 31. The instructions are executed by the at least one processor 31 to enable the at least one processor 31 to perform a fast time-domain imaging method for a micro-platform synthetic aperture radar as described in the above-mentioned method embodiment.

[0156] The bus can include any number of interconnecting buses and bridges, allowing for a variety of configurations of peripheral devices, memory, and processors. The bus also can include various other circuits and devices such as power management, clock, and memory management circuits, which are well known in the art, and therefore will not be described in further detail. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single device or a plurality of devices, such as a plurality of receivers and transmitters, which provide for communication with various other devices over a transmission medium.

[0157] The processor is responsible for managing the bus and general processing, which can include the execution of software stored in memory. The processor can be implemented with one or more general-purpose and / or dedicated processors.

[0158] Another embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the fast time-domain imaging method for micro-platform synthetic aperture radar according to any one of the above method embodiments.

[0159] That is, those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by a program instructing relevant hardware, and the program is stored in a storage medium, including a plurality of instructions, to make a device (such as a single-chip microcomputer, a chip) or a processor execute all steps or part of steps of the fast time-domain imaging method for micro-platform synthetic aperture radar according to the above method embodiments. The foregoing storage medium includes, but is not limited to: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0160] Those skilled in the art can understand that the above embodiments are specific embodiments for implementing the present application, and in actual application, various changes can be made in form and details without departing from the spirit and scope of the present application.

Claims

1. A fast time-domain imaging method for micro-platform synthetic aperture radar, characterized in that: include: Construct a coarse-resolution rectangular coordinate grid based on the geometric characteristics of the observation scene; A sub-aperture segmentation strategy is used to perform local coarse focusing processing to form multiple coarse images; Performing an equally spaced zero-padding upsampling operation on each coarse image to retain high-frequency information and restore the spectral integrity of sub-aperture imaging, thereby obtaining multiple upsampled coarse images; Applying the coherent stacking criterion to two adjacent upsampled coarse images, the coherent stacking is performed to achieve effective accumulation of azimuth resolution and update the coefficients related to the coherent stacking. The next level of image coherence superposition is performed based on the updated coefficients related to coherent superposition, and full-resolution image reconstruction is performed step by step to finally obtain a high-resolution imaging result map.

2. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 1, characterized in that: Construct a coarse-resolution rectangular coordinate grid based on the geometric characteristics of the observation scene, including: The echo signal of the micro-platform synthetic aperture radar is demodulated and range compressed. The signal obtained after processing is expressed by the formula: s(x,r)=Asinc[K B (r-r p )]exp(-jK C r p ); K B =K max -K min ; K C =(K max +K min ) / 2; Where A is a complex constant, which is determined by the complex target reflectivity and free space propagation attenuation coefficient at point p and does not affect the imaging process. r represents the range axis of the micro-platform synthetic aperture radar, and K B is the distance-dimensional wavenumber bandwidth, K max is the maximum wave number, K min is the minimum wave number, K C is the central wave number, sinc(·) is the Sinker function formed after distance compression, and s(x,r) represents the signal obtained after processing; BPA performs coherent accumulation of echo signals in the entire aperture, and the obtained complex image pixel I(x i ,y i ) is expressed by the formula: Among them, r start Indicates the starting distance of the imaging area, r end Indicates the end distance of the imaging area, r i It represents the distance between the micro-platform synthetic aperture radar and each pixel of the complex image, and L is the length of s(x,r); The two-dimensional wavenumber spectrum range K of the image obtained by the stationary phase theorem after the two-dimensional Fourier transform is calculated by the following formula: x and K y , and the oblique angle θ between the pixel and the micro-platform synthetic aperture radar: θ=arcsin[(xx i ) / r i ]: K max sinθ min ≤K x ≤K max sinθ max ; K min cosθ s ≤K y ≤K max cosθ s ; Among them, θ s represents the radar beam slant angle, θ min and θ max They represent the minimum and maximum values ​​of the oblique angle between the pixel and the micro-platform synthetic aperture radar; The boundary value δ of the two-dimensional sampling interval of the BP image is calculated by the following formula: x and δ y : d x ≤1 / [2K max sin(θ b / 2)]; d y ≤1 / K b cosθ s ; Among them, θ b Indicates the radar beam width; The radar wave number bandwidth is calculated by the subaperture length, and the size before zero padding, that is, the maximum grid interval before zero padding, is calculated. Then, according to the radar requirements, the actual grid interval is set to γ ​​times the size before zero padding.

3. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 2, characterized in that: The sub-aperture segmentation strategy is used to perform local coarse focusing processing to form multiple coarse images, including: Calculate the two-way distance from each grid point to each radar position and convert it into time delay, and extract the signal value of the corresponding position from the processed signal; The compensated phase is calculated based on the distance value from each grid point to each radar position using the following formula: ρ=exp(-jK C r i ); Where, ρ is the compensation phase; The signal values ​​of different radar positions at the same grid point are multiplied by the corresponding compensated phase values ​​and then coherently superimposed to form multiple coarse images.

4. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 3, characterized in that: An equal-interval zero-padding upsampling operation is performed on each coarse image to preserve high-frequency information and restore the spectral integrity of sub-aperture imaging, resulting in multiple upsampled coarse images, including: The azimuth dimension of the coarse image is upsampled by inserting zero values. Assuming the interpolation factor is n, the azimuth signal of a certain distance unit of the upsampled coarse image is expressed by the formula: x i =0,1,2,…,N-1; Among them, I zero (x i ′ ,y0) is the azimuth signal of a certain distance unit of the upsampled coarse image, y0 represents the index of a certain distance unit of the upsampled coarse image, and N-1 is the new signal length; By the following formula, I zero (x i ′ ,y0) to perform azimuth Fourier transform: Among them, n is both an interpolation factor and a compression factor. Due to the periodicity of the discrete Fourier transform, the periodic extension of the wavenumber spectrum of the upsampled coarse image is also compressed to n times the original value.

5. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 4, characterized in that: n=2, that is, the wavenumber spectrum of the coarse image after upsampling becomes twice the original one.

6. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 5, characterized in that: The coherent stacking criterion is applied to two adjacent upsampled coarse images to achieve effective accumulation of azimuth resolution and update the coefficients related to coherent stacking, including: The upsampled coarse image is subjected to wavenumber spectrum compression, that is, the wavenumber spectrum is multiplied by the wavenumber spectrum compression factor in the time domain. After compression, the azimuthal wavenumber spectrum of the sub-aperture is near zero frequency and periodic zero frequency. The wavenumber spectrum compression factor is calculated by the following formula: F C =exp(-jK C r i )×exp(-jK C y i ); Among them, F C represents the wavenumber spectrum compression factor; The wavenumber spectrum of each sub-aperture is low-pass filtered to remove the wavenumber spectrum near the periodic zero frequency to obtain the correct sub-aperture wavenumber spectrum. The low-pass filtering is achieved by a low-pass filter; The correct sub-aperture wavenumber spectrum is padded with half of the zeros on both sides of the zero frequency, so that the data length becomes twice the original one. After the azimuth inverse Fourier transform is performed, it is returned to the time domain to complete the azimuth upsampling. Decompressing the wavenumber spectrum of the upsampled echo signal based on the decompression coefficient to restore the wavenumber spectrum and obtain a decompressed sub-aperture image; The decompression coefficient is calculated using the following formula: F′ C =exp(jK C r i )×exp(jK C y i ); Among them, F′ C represents the decompression coefficient; The decompressed sub-aperture images are coherently superimposed on each two images to achieve effective accumulation of azimuth resolution, obtain a superimposed image, and update the wavenumber spectrum compression factor and decompression coefficient.

7. The fast time domain imaging method for micro-platform synthetic aperture radar according to claim 6, characterized in that: Based on the updated coefficients related to coherent superposition, the next level of image coherence superposition is performed, and full-resolution image reconstruction is performed step by step, and finally a high-resolution imaging result map is obtained, including: Based on the updated wavenumber spectrum compression factor and decompression coefficient, the wavenumber spectrum compression, low-pass filtering, azimuthal upsampling, wavenumber spectrum decompression, coherent superposition, and updating of the wavenumber spectrum compression factor and decompression coefficient operations are repeated, and full-resolution image reconstruction is performed step by step until only one image remains, obtaining the final high-resolution imaging result image.

8. A fast time-domain imaging system for micro-platform synthetic aperture radar, characterized in that: include: A grid construction module is used to construct a coarse-resolution rectangular coordinate grid based on the geometric characteristics of the observation scene; A coarse image generation module is used to perform local coarse focusing processing using a sub-aperture segmentation strategy to form multiple coarse images; an equally spaced zero-padding upsampling module, configured to perform an equally spaced zero-padding upsampling operation on each coarse image to preserve high-frequency information and restore the spectral integrity of sub-aperture imaging, thereby obtaining a plurality of upsampled coarse images; A coherent stacking module is used to perform image coherent stacking on two adjacent upsampled coarse images using a coherent stacking criterion to achieve effective accumulation of azimuth resolution and update coefficients related to coherent stacking; The full-resolution reconstruction module is used to instruct the coherent superposition module to perform the next level of image coherent superposition based on the updated coefficients related to coherent superposition, perform full-resolution image reconstruction step by step, and finally obtain and output a high-resolution imaging result map.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a fast time domain imaging method for a micro-platform synthetic aperture radar as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it can implement a fast time-domain imaging method for a micro-platform synthetic aperture radar according to any one of claims 1 to 7.

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