A contrast-based vehicle-mounted SAR imaging and motion compensation method

Abstract

The application discloses a contrast-based vehicle-mounted SAR imaging and motion compensation method, relates to the technical field of vehicle-mounted synthetic aperture radar imaging, and realizes vehicle-mounted SAR motion compensation in the absence of a navigation device; the method comprises the following steps: acquiring vehicle-mounted SAR original data, and realizing coarse imaging of the vehicle-mounted SAR by using a vehicle-mounted Omega-K algorithm; according to the definition of a standard deviation intensity contrast function, a contrast-based point selection strategy and a WPGA kernel are designed; according to the equivalent bunching principle, sub-aperture division is carried out on a full-aperture vehicle-mounted SAR coarse precision image; the designed WPGA method is used to realize sub-aperture SAR image self-focusing through aperture-by-aperture iterative optimization; finally, sub-aperture splicing is carried out to obtain a final vehicle-mounted SAR image. The application analyzes the characteristics of vehicle-mounted signals, and the contrast-optimized WPGA method can improve the robustness of self-focusing, realizes wide swath and high-precision vehicle-mounted SAR imaging, and provides technical support for the development of vehicle-mounted SAR technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle-mounted SAR imaging, and particularly relates to a vehicle-mounted SAR imaging and motion compensation method based on contrast. BACKGROUND

[0002] Nowadays, more and more vehicles are equipped with automatic driving or driver assistance functions. As one of the core components of the advanced driving assistance system (ADAS), the automobile millimeter wave radar has become the focus of scholars and manufacturers due to its advantages such as all-weather, miniaturization, high integration and key transmission capabilities. The automobile millimeter wave radar working in the W band (76 to 81 GHz) is widely used to obtain the radial distance, speed and angular position measurement values of the targets in the vehicle environment. However, the automobile radars of large-scale manufacturers must trade off between the angular resolution (usually higher than 1 degree), the ranging, the bandwidth and the field of view, which makes them face challenges in high-resolution environment perception and mapping applications in automatic driving.

[0003] Many studies attempt to utilize synthetic aperture radar (SAR) technology to improve the azimuth resolution and improve the accuracy of automobile environment perception. The operation of SAR is in line with the mechanical principle of the automobile system, and the automobile SAR image similar to the bird's eye view (BEV) map of the laser radar can also be used for target detection. The principle of SAR is to utilize the relative motion between the radar and the detected target to synthesize a virtual aperture along the motion trajectory. By comprehensively analyzing the frequency of the radar echo signal and accurately eliminating the distance-Doppler coupling caused by the virtual aperture, the azimuth resolution is finally improved. The range resolution is only determined by the signal bandwidth, while the azimuth resolution (usually <1 degree) is determined by the synthesized angle.

[0004] In principle, SAR requires navigation accuracy better than the wavelength (4 millimeters in the W band), but this requirement is for the relative motion within the synthesized aperture, which can reach several tens of centimeters. However, motion compensation is still crucial in automobile SAR imaging. The complexity of the automobile scene and the high mobility of the vehicle will cause the radar to be easily affected by bumps and vibrations during the motion. This will introduce additional motion errors, which will eventually lead to image defocusing.

[0005] Early motion compensation methods are based on external devices such as global navigation satellite system (GNSS) and inertial measurement unit (IMU) to obtain motion parameters (trajectory, velocity, etc.) that meet the accuracy requirements. Subsequently, motion compensation and high-resolution imaging are carried out based on these motion parameters. However, with the increase of imaging resolution, the accuracy of current car-level navigation systems cannot meet the imaging requirements. Therefore, motion compensation is still a key problem for car SAR imaging. Autofocus methods have been verified on traditional SAR platforms (such as spaceborne and airborne). These methods can be divided into two categories: parametric methods and non-parametric methods. Parametric methods usually model the motion error and perform parameter estimation and motion compensation based on the model properties. Non-parametric methods can also be divided into two categories: image optimization-based methods and strong scattering point-based methods. Among them, the standard phase gradient autofocus (PGA), as an effective strong scattering point-based method, includes five main steps: 1) range cell selection; 2) cyclic shift; 3) windowing; 4) phase gradient estimation; 5) iterative phase correction.

[0006] At the same time, compared with the traditional SAR imaging scene, there are many differences in the car SAR imaging scene: 1) The car SAR system usually operates in strip mode, using a wide beam and a frequency-modulated continuous wave (FMCW) signal. This requires techniques such as sub-aperture (SA) division for imaging and autofocus to be suitable for car scenes; 2) The car SAR imaging mode is near-field imaging, and the car environment is complex, with strong clutter that interferes more strongly with the signal; 3) The target of the strong point scene of car SAR imaging is relatively concentrated and has a certain sparseness in the range dimension. SUMMARY

[0007] The purpose of the present application is to provide a contrast-based vehicle-mounted SAR imaging and motion compensation method, which improves the imaging stability of the vehicle-mounted SAR system in the vehicle-mounted scene with isolated strong points, and further optimizes the quality of the vehicle-mounted SAR image.

[0008] Technical scheme: In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0009] A contrast-based vehicle-mounted SAR imaging and motion compensation method, comprising:

[0010] SAR imaging is performed on the original FMCW signal of the vehicle-mounted SAR using the Omega-K algorithm;

[0011] Sub-aperture division is performed on the full-aperture coarse-precision SAR image;

[0012] According to the definition of standard deviation intensity contrast function, the phase error is estimated sub-aperture by sub-aperture based on the contrast of WPGA method, and then the sub-aperture phase error is fitted by polynomial, the phase error is compensated by the structure characteristics of phase, and the high-precision SAR image of sub-aperture is obtained.

[0013] The sub-aperture splicing is performed to obtain the full-aperture high-precision SAR image.

[0014] Further, in the SAR imaging of the vehicle-mounted SAR original FMCW signal by using the Omega-K algorithm, after the residual video phase compensation and intra-pulse motion compensation of the SAR original FMCW signal, the SAR imaging is performed by using the Omega-K algorithm to obtain the full-aperture coarse-precision SAR image.

[0015] Further, the SAR imaging of the signal compensated by the residual video phase and intra-pulse motion is performed by using the Omega-K algorithm to obtain the full-aperture coarse-precision SAR image, which includes multiplication with a reference function multiplier and Stolt interpolation, and the reference function multiplier H rfm (K r ,K x ) is expressed as wherein K r , K x and R0 represent the distance wave number, the azimuth wave number and the reference distance respectively, The Stolt interpolation kernel is expressed as wherein K y = K r -K rc represents the distance wave number, K rc = 4π / λ represents the reference distance wave number, and λ represents the signal wavelength.

[0016] Further, in the sub-aperture division of the full-aperture coarse-precision SAR image, the sub-aperture length is ηL sub , η (1≤η≤2) is the overlap rate, and L sub satisfies: wherein L smin and L sref represent the minimum equivalent beam length and the reference equivalent beam length respectively, and a = 4μtan(θ m / 2)ΔR. ΔR and θ m represent the distance imaging range and the signal beam width respectively, and μ (μ≤0.05) represents the effective beam proportion.

[0017] Further, in the polynomial fitting of the sub-aperture phase error, the sub-aperture phase error after the polynomial fitting is expressed as wherein K rc ​denotes the reference distance wavenumber, and N p denote the coefficients and the order of the polynomial fit, respectively.

[0018] Further, in the phase error compensation by the structure characteristics of the phase, according to the phase structure characteristics, the slant distance error ΔR stolt (K x ) is expressed as where k i denotes the coefficients of the polynomial fit; the sub-aperture phase error compensation function is expressed as where, denotes the azimuth motion error after the polynomial fit, denotes the range motion error after the polynomial fit.

[0019] Further, the standard deviation intensity contrast function C(m) of the vehicle-mounted SAR characteristics is defined as

[0020]

[0021] where M and N are the number of range and azimuth sampling points, m and n denote the range and azimuth indexes, respectively, and I(m,n) is the element of the selected sub-aperture SAR image at the mth row and the nth column.

[0022] Further, the contrast-based WPGA method includes: contrast-based point selection, circle shift, windowing, calculation of contrast-based weight, estimation of phase error gradient using a WPGA kernel, accumulation of phase error, and iterative phase correction; wherein the contrast-based point selection process is: calculating C(m) of each range unit, setting a threshold σ, selecting the range unit with C(m)≥σC max , C max is the maximum contrast; the contrast-based weight is The WPGA kernel is expressed as

[0023]

[0024] where arg[·] is a complex amplitude function, denotes the estimated phase error, I * (m,n-1) denotes the conjugate of the element of the selected sub-aperture image at the mth row and the (n-1)th column.

[0025] The application also provides a computer system, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to implement the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method.

[0026] The application further provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method.

[0027] Beneficial effects: The application analyzes the characteristics of a vehicle-mounted FMCW signal, designs a sub-aperture self-focusing module, and applies it to a vehicle-mounted FMCW-SAR system. Meanwhile, for a vehicle-mounted imaging scene with isolated strong points, more and more suitable target distance units can be selected by using contrast, and the WPGA kernel is further weighted by using contrast, which can improve the robustness of the PGA method in this scene, further optimize the SAR image quality, and obtain wide and high-precision vehicle-mounted SAR imaging, thereby providing technical support for the application of vehicle-mounted SAR technology. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0029] Figure 1 The flowchart of the embodiments of the present application is shown in the figure.

[0030] Figure 2 The transmit waveform diagram of the vehicle-mounted FMCW-SAR system is shown in the figure.

[0031] Figure 3 The geometric model diagram of the vehicle-mounted SAR is shown in the figure.

[0032] Figure 4 The equivalent beamforming mode diagram of the vehicle-mounted SAR is shown in the figure.

[0033] Figure 5 The sub-aperture division strategy diagram of the vehicle-mounted SAR is shown in the figure.

[0034] Figure 6 The sub-aperture division diagram of the vehicle-mounted SAR is shown in the figure.

[0035] Figure 7 The sub-aperture splicing diagram of the vehicle-mounted SAR is shown in the figure.

[0036] Figure 8 The vehicle-mounted SAR example diagram is shown in the figure, wherein (a) is a radio frequency system (TIA WR2243) of the vehicle-mounted SAR; and (b) is a mounting position of the vehicle-mounted SAR.

[0037] Figure 9 The optical image and the full-aperture coarse-precision vehicle-mounted SAR image of the experimental scene are shown in the figure.

[0038] Figure 10 are experimental effect comparison diagrams, wherein (a) is an original SAR image; (b) is an SAR image processed by a standard PGA method based on energy selection points; (c) is an SAR image processed by a standard PGA method based on contrast selection points; (d) is an SAR image processed by a WPGA method based on energy selection points and a contrast weighted kernel; and (e) is an SAR image processed by the method of the embodiment of the present application;

[0039] Figure 11 is a real width image of the metal street lamp in region A;

[0040] Figure 12 is a profile image of region A in (a)-(e), wherein (a)-(e) respectively correspond to Figure 10 is a profile image of region A in (a)-(e) of the present application; and (f) is Figure 10 is a profile image of region A in (a)-(e) of the present application; and (f) is Figure 10 is an azimuth profile image of the metal street lamp in region A in (e) of the present application.

[0041] Figure 13 are experimental effect comparison diagrams, wherein (a) is an SAR image processed by a PWE-PGA method based on contrast selection points; and (b) is an SAR image processed by a WPGA method based on contrast selection points and a variance weighted kernel;

[0042] Figure 14 is a profile image of region A in (a)-(b), wherein (a)-(b) respectively correspond to Figure 13 is a profile image of region A in (a)-(b) of the present application; and (c) is Figure 13 is a profile image of region A in (a)-(b) of the present application; and (c) is Figure 13 is an azimuth profile image of the metal street lamp in region A in (e) of the present application. Figure 10 DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0044] The embodiment of the present application provides a contrast-based vehicle-mounted SAR imaging and motion compensation method, and a specific flow of the method is as shown in Figure 1 The method specifically comprises the following steps:

[0045] ​Step 1, vehicle-mounted SAR coarse imaging, in this embodiment, after residual video phase compensation and intra-pulse motion compensation are performed on a SAR original frequency modulation continuous wave (FMCW) signal, an Omega-K algorithm is used to perform SAR imaging; specifically including:

[0046] (1.a) Establish a system-level FMCW-SAR signal model, Figure 2 is the transmission waveform of the vehicle-mounted FMCW-SAR system, p is the chirp cycle time, the signal frequency decreases to the initial frequency f l in the idle time T s , and then frequency modulation is performed in the frequency modulation duration T f ; since an analog-to-digital converter (ADC) needs a start-up time, that is, an effective start-up time T i of the ADC, the ADC sampling time T s should be less than T f , the effective signal bandwidth is B r , and the effective initial frequency of the FMCW signal is f c , which can be expressed as f c = f s + γT i , where γ = B r / T s is the chirp frequency modulation rate.

[0047] Therefore, the form of the received signal of the system-level FMCW-SAR is

[0048]

[0049] where A is the signal amplitude, τ and t a represent the fast time and the slow time, respectively, T is the signal transmission time width, σ(x n , y0, z0) is the target complex reflection coefficient, (x n , y0, z0) represents the X, Y, and Z axis coordinates of the target in the Cartesian coordinate system, rect[(τ-Δτ) / T-1 / 2] and g(t a ) are the distance dimension envelope of the received signal and the antenna pattern, respectively, f c and γ represent the carrier frequency and the frequency modulation rate of the transmitted signal, respectively, and Δτ is the target two-way time delay; at this time, the signal is in the range wavenumber domain-azimuth time domain.

[0050] The geometric model of the vehicle-mounted SAR is shown in Figure 3 , assuming that the vehicle works in strip mode, the motion distance along the X axis is vT a , v is the reference speed of the vehicle, T a is the synthetic aperture time, the radar reference height is H, and the beam width is θ m, reference slant range is R0, reference point is P(x0, y0, z0), instantaneous squint angle is θ, SAR instantaneous position is C(x a ,y a ,z a ), considering a target Q(x n ,y0,z0) at any point on the reference slant range, instantaneous slant range R(R0, X) is expressed as

[0051]

[0052] wherein, X = vt a , Δx, Δy, Δz are motion errors, and ΔR(R0, X) is slant range error introduced by motion errors. Therefore, two-way time delay Δτ is expressed as Δτ = 2R(R0, X) / c, and c is light speed.

[0053] (1.b) performing residual video phase compensation and intra-pulse motion compensation on the received signal. The signal after residual video phase compensation is expressed as

[0054] s(K r ,X) = P(K r )·g(X)·exp[jK r R(R0,X)], (3)

[0055] wherein, K r = 4π[f c + γ)τ - T l - T i ] / c represents distance wave number, P(K r ) and g(X) are distance wave number domain envelope and azimuth antenna pattern; and intra-pulse motion compensation function H im (τ, K x ) is expressed as:

[0056] H im (τ, K x ) = exp(jK x vτ), (4) wherein K x represents azimuth wave number. Two-dimensional wave number spectrum of the signal after compensation is expressed as

[0057]

[0058] wherein, P(K r ) represents distance wave number domain envelope in distance dimension, and G(K x ) is wave number domain envelope in azimuth dimension,

[0059] (1.c) SAR imaging of the signal obtained in step (1.b) using the Omega-K algorithm to obtain a full-aperture coarse accuracy SAR image, including multiplication by a reference function multiplier and Stolt interpolation; in particular, the reference function multiplier H rfm (K r ,K x ) is represented as

[0060]

[0061] Here, H rfm (K r ,K x ) is multiplied directly with the signal S(K r ,K x ), and then Stolt interpolation is performed for each range cell, the Stolt interpolation kernel being represented as where K y = K r -K rc represents the range wavenumber, and K rc represents the reference range wavenumber. Finally, a two-dimensional IFFT is performed on the signal to achieve focusing, to obtain a full-aperture coarse accuracy SAR image.

[0062] Step 2, sub-aperture division, in this embodiment, the full-aperture coarse accuracy SAR image is divided into sub-apertures using the principle of equivalent beamforming; this specifically includes:

[0063] (2.a) according to the ratio μ (μ≤0.05) of the sub-aperture imaging range and the effective beamforming area, the vehicle-mounted SAR equivalent beamforming mode is shown in Figure 4 , S is the imaging area, R min and R max represent the minimum and maximum distances of imaging, the orange area S spot is the equivalent beamforming area within the sub-aperture L m , L smin and L sref respectively represent the minimum and reference equivalent beamforming aperture length; the sub-aperture division strategy is shown in Figure 5 , the sub-aperture length range is calculated to obtain

[0064]

[0065] where L sub is the sub-aperture length, a = 4μtan(θ m / 2)ΔR, ΔR represents the range imaging range;

[0066] (2.b) selecting a suitable value L sub, set the overlap rate η (1≤η≤2), divide the full-aperture coarse-precision SAR image in step 1 into n a sub-apertures with a sub-aperture length of ηL sub , to obtain n a sub-aperture coarse-precision SAR images, and a sub-aperture division schematic diagram is shown in Figure 6 .

[0067] Step 3, sub-aperture SAR self-focusing, wherein according to the definition of the standard deviation intensity contrast function, the phase error is estimated sub-aperture by sub-aperture by using the contrast-based WPGA method, and then the sub-aperture phase error is polynomially fitted, the phase error is compensated through the structural characteristics of the phase, and a sub-aperture high-precision SAR image is obtained; specifically including:

[0068] (3.a) in the sub-aperture image in step 2, the phase error is estimated by using the contrast-based WPGA method, to obtain a sub-aperture phase error;

[0069] The contrast-based WPGA method comprises: contrast-based point selection, circle shift, windowing, calculation of contrast-based weight, estimation of phase error gradient by using a WPGA kernel, accumulation of phase error and iterative phase correction;

[0070] Specifically, the contrast-based point selection strategy is represented as C(m)≥σC max , wherein σ∈(0, 1] and C max respectively represent a threshold value and a maximum contrast, and C(m) is defined as

[0071]

[0072] , wherein M and N are the number of distance and azimuth sampling points, m and n respectively represent distance and azimuth indexes, and I(m, n) is the mth row and nth column element of the selected sub-aperture SAR image; here, distance units with C(m)≥σC max are selected, and in the embodiment, the minimum selection number is set to 50 and the maximum selection number is set to M / 2.

[0073] The contrast-based weight is represented as

[0074]

[0075] The WPGA kernel is represented as

[0076]

[0077] , wherein arg[·] is a complex amplitude function, represents the estimated phase error, and I *(m, n-1) represents the conjugate of the element of the (m, n-1)th row and the (n-1)th column of the selected sub-aperture image.

[0078] The obtained phase gradient is accumulated to obtain a phase error vector, and a central value is subtracted to obtain a sub-aperture phase error.

[0079] (3.b) polynomial fitting is performed on the sub-aperture phase error, and sub-aperture phase error compensation is performed by using the phase structure characteristics of the signal to obtain n a sub-aperture high-precision SAR images;

[0080] The polynomial fitting of the sub-aperture phase error is represented as

[0081]

[0082] wherein, is the sub-aperture phase error after polynomial fitting, and N p respectively represent a coefficient and an order of polynomial fitting, K x i represents the i-th power of K x , ΔR res (K x ) is a residual range error; here, N p is set to 10;

[0083] The phase structure characteristics of the signal are represented as

[0084]

[0085] wherein, ΔR stolt (K x ) is a range error introduced by Stolt interpolation, k i represents a coefficient of polynomial fitting;

[0086] The sub-aperture phase error compensation function H e (K y , K x ) is represented as

[0087]

[0088] Herein, represents a range direction motion error after polynomial fitting, represents a range direction motion error after polynomial fitting. The process of phase error compensation is to multiply the sub-aperture image data described in step 2 by H e (K y , K x ) after performing azimuth dimension FFT, and then to perform azimuth dimension IFFT to obtain sub-aperture high-precision SAR images.

[0089] Step 4, sub-aperture stitching is carried out according to the sub-aperture division principle of step 2, and a full-aperture high-precision SAR image is obtained, and a sub-aperture stitching schematic diagram is as shown in Figure 7 .

[0090] The effectiveness of the present application can be further illustrated by the following experiment.

[0091] (I) Data and experimental scene description

[0092] The radio frequency system of the vehicle-mounted SAR is selected as TI AWR2243 radar development board, as shown in (a) of Figure 8 , and the installed position is as shown in (b) of Figure 8 . The vehicle-mounted SAR collects data without navigation equipment such as inertial navigation system, and the vehicle-mounted SAR experimental parameters are shown in Table 1. The size of the finally obtained vehicle-mounted SAR raw echo data is 1024*182000 (range*azimuth). The experimental scene is selected as an office building scene, and there are strong scattering points in the scene.

[0093] Table 1 Vehicle-mounted SAR experimental parameters

[0094]

[0095] (II) SAR coarse imaging results

[0096] The optical image of the experimental scene and the full-aperture coarse-precision SAR image are as shown in Figure 9 , and the scene size is about 15m*100m (range*azimuth), wherein the SAR image is divided into 9 red square regions, each region corresponds to an optical image; it can be seen from Figure 9 that many details: regions A and E are two metal street lamps, region B is a sign behind the street lamp, region C is a floor-to-ceiling window of an office building, and region D is a van driving in the opposite direction. Although the Omega-K algorithm can perform full-aperture vehicle-mounted SAR imaging, the image still has defocusing, which is caused by motion error, and needs to be self-focused.

[0097] (III) SAR self-focusing results

[0098] In the present experiment, R min , μ, σ, η are set as 0.5m, 0.05, 0.6 and 1.5 respectively, after sub-aperture division, 167 sub-aperture coarse-precision SAR images are obtained. After image optimization by using the self-focusing method, the full-aperture high-precision vehicle-mounted SAR image is obtained after sub-aperture stitching.

[0099] 1. Comparison of selection strategies

[0100] The results of different selection point strategies are compared, the comparison methods include the energy-based selection point strategy and the variance-based selection point strategy, and the comparison methods include the standard PGA method and the method of the embodiment of the application; Figure 10 The comparison results are given, wherein, Figure 10 In the figures, (a) is an original SAR image; (b) is an SAR image processed by the standard PGA method based on the energy selection point; (c) is an SAR image processed by the standard PGA method based on the contrast selection point; (d) is an SAR image processed by the WPGA method based on the energy selection point and the contrast weighted kernel; and (e) is an SAR image processed by the method of the embodiment of the application. It can be seen that the self-focusing method is effective in the vehicle-mounted scene, and the performance of the method of the embodiment of the application is optimal.

[0101] For clearer comparison, the region A in the figure Figure 10 is analyzed, the region A is a metal street lamp, and the true width of the region A is 100 mm, as shown in the figure Figure 11 .The profile of the region A is shown in (a)-(e) in the figure Figure 12 , wherein, Figure 12 In the figures, (a) is the profile of the region A in (a) of the figure Figure 10 ; (b) is the profile of the region A in (b) of the figure Figure 10 ; (c) is the profile of the region A in (c) of the figure Figure 10 ; (d) is the profile of the region A in (d) of the figure Figure 10 ; and (e) is the profile of the region A in (e) of the figure Figure 10 . It can be seen that the original image is obviously out of focus, and the method of the embodiment of the application is closest to the true value; Figure 12 (f) in the figure Figure 10 is the azimuth profile of the metal street lamp in the region A, and the reason why there are two peak values is that the shape of the street lamp is a rectangular column, and the radar scattering cross section of the edge is larger. It can be seen that the self-focusing effect of the method of the embodiment of the application is optimal.

[0102] Quantitative analysis is performed, and the performance indicators of the self-focusing method are counted, including entropy, contrast and calculation time, and the results are shown in Table 2. It can be seen that, under the loss of certain calculation efficiency, the performance indicators of the method of the embodiment of the application are optimal, and the selection point strategy based on the contrast has obvious advantages in the vehicle-mounted scene.

[0103] Table 2 Performance indicators of the self-focusing method

[0104] Method Contrast Entropy Subgraph computation time Original image 7.0964 1.1677 \ Standard PGA method based on energy seed 8.6017 1.0157 0.4898s Standard PGA method based on contrast seed 9.6640 0.8635 0.6505s WPGA method based on energy seed and contrast weighted kernel 9.2738 0.9777 0.5508s Method of the invention 10.035 0.7886 0.6578s

[0105] 2, Comparison of PGA kernels

[0106] The results of different PGA kernels are compared, and the selected point strategy is a contrast-based selected point strategy, and the contrast methods include a PWE-PGA method and a WPGA method based on variance; Figure 13 Fig. (a)-(b) shows the contrast results, wherein (a) is a SAR image processed by the PWE-PGA method based on contrast selected points; (b) is a SAR image processed by the WPGA method based on contrast selected points and variance weighted kernels; and Figure 10 It can be seen from (e) that the PGA kernel performance of the embodiment method is better than that of the PWE-PGA method and the WPGA method based on variance;

[0107] Similarly, the metal street lamps in the region A are not connected, Figure 14 Fig. (a) is Figure 13 Fig. (a) is the outline of the region A, Figure 14 Fig. (b) is Figure 13 Fig. (b) is the outline of the region A in (b), and the performances of the two PGA kernels are not much different, but the width of the metal street lamp is not close to the true value, and the performance is weaker than that of the embodiment method; Figure 14 Fig. (c) is Figure 13 and Figure 10 Fig. (e) is the profile of the metal street lamp in the region A, and the advantages of the embodiment method can also be obviously seen therefrom; the performance indicators of the two self-focusing methods are counted, as shown in Table 3, and by comparing Table 2 and Table 3, the performance advantages of the embodiment method can be obviously seen.

[0108] Table 3: Performance indicators of the PWE-PGA method and the WPGA method based on variance

[0109] Method Contrast Entropy Subgraph computation time PWE-PGA method based on contrast seed 8.8442 1.0302 0.4582s WPGA method based on contrast seed and variance weighted kernel 9.2571 0.9220 0.4943s

[0110] In summary, the embodiment method provides a contrast-based vehicle-mounted SAR imaging and motion compensation method for a vehicle-mounted imaging scene with isolated strong points, more and more suitable target distance units can be selected by using contrast, and the WPGA kernel is further weighted by using contrast, the algorithm robustness is improved, the vehicle-mounted SAR image quality is optimized, and the wide and high-precision imaging of the vehicle-mounted SAR can be realized.

[0111] The embodiment method further provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is executed by the processor to realize the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method.

[0112] The embodiment of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method.

[0113] Program code for carrying out the methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the steps of the methods of the present application to be implemented. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server. The present application is not limited by the details of the method set forth herein, further modifications and / or additions of method steps, and / or further modifications and / or additions of program code can be possible, by one skilled in the art, without departing from the scope of the present application.

[0114] The preferred embodiments of the present application have been described in detail. It should be understood that modifications and variations can be made by those skilled in the art without departing from the spirit and scope of the present application. Accordingly, it is intended that the present application cover all such modifications and variations of the present application that are within the scope of the claims.

Claims

1. A contrast-based vehicular SAR imaging and motion compensation method, characterized in that, The method comprises the following steps: SAR imaging is performed on the original FMCW signal of the vehicle-mounted SAR by using an Omega-K algorithm; sub-aperture division is performed on the full-aperture coarse-precision SAR image; a WPGA method based on contrast is used to estimate the phase error of each sub-aperture according to the definition of a standard deviation intensity contrast function, and then the phase error of each sub-aperture is fitted by using a polynomial to compensate the phase error according to the structural characteristics of the phase, so as to obtain a sub-aperture high-precision SAR image; wherein the standard deviation intensity contrast function C(m) of the vehicle-mounted SAR is defined as wherein M and N are the number of distance and azimuth sampling points, m and n represent the distance and azimuth indexes respectively, and I(m, n) is the element of the mth row and nth column of the selected sub-aperture SAR image; The contrast-based WPGA method comprises: contrast-based point selection, circle shift, windowing, calculation of contrast-based weight, estimation of phase error gradient by using WPGA kernel, accumulation of phase error and iterative phase correction; wherein the contrast-based point selection process is: calculating C(m) of each distance unit, setting threshold σ, selecting distance unit with C(m)≥σC max , C max being maximum contrast; the contrast-based weight is The WPGA kernel is represented as where arg[•] is the complex argument function, denotes the estimated phase error, I * (m, n - 1) denotes the conjugate of the element of the selected sub-aperture image in the mth row and the n - 1th column. sub-aperture splicing is performed to obtain a full-aperture high-precision SAR image. 2.The contrast-based vehicular SAR imaging and motion compensation method of claim 1, wherein, In the step of performing SAR imaging on the original FMCW signal of the vehicle-mounted SAR by using an Omega-K algorithm, after residual video phase compensation and intra-pulse motion compensation are performed on the original FMCW signal of the SAR, the Omega-K algorithm is used to perform SAR imaging to obtain a full-aperture coarse-precision SAR image.

3. The contrast-based vehicular SAR imaging and motion compensation method of claim 2, wherein, The signals compensated for residual video phase and intra-pulse motion are SAR imaged using the Omega-K algorithm to obtain full-aperture coarse-fine accuracy SAR images, including multiplication by a reference function multiplier H rfm (K r ,K x ) is expressed as where K r , K x and R0 represent the range wavenumber, azimuth wavenumber and reference range respectively, The Stolt interpolation kernel is expressed as where K y = K r -K rc is the range wavenumber, K rc = 4π / λ is the reference range wavenumber and λ is the signal wavelength.

4. The contrast-based vehicular SAR imaging and motion compensation method of claim 1, wherein, The sub-aperture length is ηL sub , η, 1 ≤ η ≤ 2 is an overlap rate, L sub satisfies: wherein, L smin and L sref respectively represent a minimum equivalent beam length and a reference equivalent beam length, a = 4μtan(θ m / 2)ΔR, ΔR and θ m respectively represent a distance imaging range and a signal beam width, μ, μ ≤ 0.05 represents an effective beamforming ratio.

5. The contrast-based vehicular SAR imaging and motion compensation method of claim 1, wherein, In the polynomial fitting of the sub-aperture phase error, the polynomial fitted sub-aperture phase error is expressed as where K x represents an azimuthal wave number, K rc represents a reference distance wave number, and N p respectively represent a coefficient and an order of polynomial fitting, K x i represents an i-th power of K x .

6. The contrast-based vehicular SAR imaging and motion compensation method of claim 5, wherein, In the phase error compensation by the structural characteristics of the phase, according to the phase structural characteristics, the slant distance error ΔR introduced by Stolt interpolation stolt (K x ) is expressed as where k i represents the coefficient of polynomial fitting; the sub-aperture phase error compensation function is expressed as where K y represents the distance wave number, K rc represents the reference distance wave number, represents the azimuth direction motion error after polynomial fitting, represents the range direction motion error after polynomial fitting.

7. A computer system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to realize the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method according to any one of claims 1-6.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the contrast-based vehicle-mounted SAR imaging and motion compensation method according to any one of claims 1-6.

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