阅读说明：本技术 一种基于图像域偏移特征的地面运动目标参数估计方法 (Ground moving object parameter estimation method based on image domain deviation characteristics ) 是由 丁泽刚 王岩 董泽华 张驰 于 2021-05-12 设计创作，主要内容包括：本发明提供一种基于图像域偏移特征的地面运动目标参数估计方法,属于雷达信号处理技术领域,该方法包括以下步骤：对各通道回波数据进行预处理,对预处理后的图像进行动目标检测,获得目标在图像域的位置；将设定的测速范围划分速度搜索网格,对网格中每个速度值计算动目标在图像域的偏移量；基于所述目标在图像域的位置和所述偏移量,计算各速度所对应的目标原始位置；获取所述目标原始位置对应的高程数据；基于所述高程数据,通过最大似然法估计目标速度。(The invention provides a ground moving target parameter estimation method based on image domain deviation characteristics, which belongs to the technical field of radar signal processing and comprises the following steps: preprocessing echo data of each channel, and detecting a moving target of a preprocessed image to obtain the position of the target in an image domain; dividing a set speed measurement range into speed search grids, and calculating the offset of a moving target in an image domain for each speed value in the grids; calculating the original positions of the targets corresponding to the speeds based on the positions of the targets in the image domain and the offset; acquiring elevation data corresponding to the target original position; based on the elevation data, a target velocity is estimated by a maximum likelihood method.)

1. A method for estimating parameters of a ground moving object based on image domain offset characteristics is characterized by comprising the following steps:

preprocessing echo data of each channel, and detecting a moving target of a preprocessed image to obtain the position of the target in an image domain;

dividing a set speed measurement range into speed search grids, and calculating the offset of a moving target in an image domain for each speed value in the grids; calculating the original positions of the targets corresponding to the speeds based on the positions of the targets in the image domain and the offset;

acquiring elevation data corresponding to the target original position;

based on the elevation data, a target speed is estimated.

2. The method for estimating parameters of a ground moving object based on image domain shift features as claimed in claim 1, wherein the preprocessing is:

firstly, carrying out SAR imaging processing on echo data of each channel;

secondly, registering each image;

and thirdly, removing the flat ground phase and compensating the ground elevation phase to finish data preprocessing.

3. The method for estimating parameters of a ground moving object based on image domain deviation characteristics according to claim 2, wherein the set speed measurement range is divided into speed search grids, and the deviation amount of the moving object in the image domain is calculated for each speed value in the grids; based on the position of the target in the image domain and the offset, the process of calculating the original position of the target corresponding to each speed is as follows:

dividing a speed grid into a set speed measuring range, and calculating the offset of a moving target in an image domain for each speed value v in the grid;

wherein f is_d＝2vcosθ/λ，Theta is the radar angle of ground clearance, v_pIs the platform flying speed, R₀Is the target distance radar slant distance, lambda is the wavelength;

the corresponding possible original position of the target is [ R ]₀(v),Y(v)]＝[R₀′-Δr(v),Y′-Δy(v)]Wherein [ R ]₀′,Y′]Is the position of the object in the image domain.

4. The method for estimating parameters of a ground moving object based on image domain shift features as claimed in claim 2, wherein the specific process of estimating the object speed by the maximum likelihood method based on the elevation data is as follows:

wherein R 'represents a covariance matrix of clutter, R'^-1Is the inverse of matrix R ', X' represents the vector of the multi-channel echo signals, a (v)^HIs the conjugate of a (v), h (R)₀(v) Y (v)) represents the true position elevation of the target, h (R)₀', Y') indicates the target image field position elevation,indicating the elevation phase resulting from the elevation difference,indicates the velocity phase, M is 1,2 … M-1.

5. The method of claim 1, wherein estimating the object velocity based on the elevation data is estimating the object velocity using a maximum likelihood method.

Technical Field

The invention belongs to the technical field of radar signal processing, and particularly relates to a ground moving target parameter estimation method based on image domain deviation characteristics.

Background

Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) technology is mainly used for detecting and detecting Ground Moving targets and estimating parameters. Since the advent, it has played an irreplaceable role in the fields of military reconnaissance and battlefield situation awareness, marine traffic surveillance, and road-to-vehicle traffic monitoring. On one hand, the SAR system with the mixed baseline configuration has the potential of simultaneous multitask, and can simultaneously perform interferometric height measurement, wide-range height-component imaging and GMTI. On the other hand, in order to meet platform stealth and aerodynamic performance requirements, a hybrid baseline usually exists in a multi-channel SAR-GMTI system adopting distributed forming installation.

Azimuth multi-channel SAR systems can implement GMTI, but the antenna aperture is too large. The distributed SAR-GMTI system can divide a traditional multi-channel large-aperture antenna into a plurality of small-aperture antennas to be distributed and shaped and installed at each part of a machine body, and reduces the influence of SAR-GMTI equipment on the stealth performance and the air performance of a platform. However, the distributed SAR-GMTI system inter-channel large vertical track baseline. In the channel arranged along the ideal flight path, the interference phase of the static ground clutter between the front channel and the rear channel is zero, and the phase difference exists between the two channels of the moving target. Under a mixed baseline configuration, on one hand, stationary clutter can generate an elevation interference phase; on the other hand, the moving target will also couple the elevation phase in the motion phase. The traditional SAR-GMTI method is difficult to estimate parameters due to two reasons, so that the mixed baseline SAR ground moving target parameter estimation method needs to be further researched.

Disclosure of Invention

In order to solve the problems, the invention provides a ground motion target parameter estimation method based on image domain migration characteristics, which effectively solves the problem that the target parameters of an SAR-GMTI system are difficult to estimate under a mixed baseline configuration.

The technical scheme for realizing the invention is as follows:

a ground moving object parameter estimation method based on image domain offset characteristics comprises the following steps:

preprocessing echo data of each channel, and detecting a moving target of a preprocessed image to obtain the position of the target in an image domain;

dividing a set speed measurement range into speed search grids, and calculating the offset of a moving target in an image domain for each speed value in the grids; calculating the original positions of the targets corresponding to the speeds based on the positions of the targets in the image domain and the offset;

acquiring elevation data corresponding to the target original position;

based on the elevation data, a target velocity is estimated by a maximum likelihood method.

Preferably, the pretreatment of the invention is:

firstly, carrying out SAR imaging processing on echo data of each channel;

secondly, registering each image;

and thirdly, removing the flat ground phase and compensating the ground elevation phase to finish data preprocessing.

Preferably, the set speed measurement range is divided into speed search grids, and the offset of the moving target in the image domain is calculated for each speed value in the grids; based on the position of the target in the image domain and the offset, the process of calculating the original position of the target corresponding to each speed is as follows:

dividing a speed grid into a set speed measuring range, and calculating the offset of a moving target in an image domain for each speed value v in the grid;

wherein f is_d＝2vcosθ/λ，Theta is the radar angle of ground clearance, v_pIs the platform flying speed, R₀Is the target distance radar slant distance, lambda is the wavelength;

the corresponding possible original position of the target is [ R ]₀(v),Y(v)]＝[R′₀-Δr(v),Y′-Δy(v)]Wherein, [ R'₀,Y′]Is aimed atThe position of the image field.

Preferably, the specific process of estimating the target speed by the maximum likelihood method based on the elevation data of the present invention is as follows:

wherein R 'represents a covariance matrix of clutter, R'^-1Is the inverse of matrix R ', X' represents the vector of the multi-channel echo signals, a (v)^HIs the conjugate of a (v), h (R)₀(v) Y (v) represents the target true location elevation, h (R'₀Y') represents the target image field position elevation,indicating the elevation phase resulting from the elevation difference,indicates the velocity phase, M is 1,2 … M-1.

Advantageous effects

The invention provides a ground moving target parameter estimation method based on image domain offset characteristics, which divides a set speed measurement range into speed search grids, calculates the offset of a moving target in an image domain for each speed value in the grids, calculates the target original position corresponding to each speed based on the position and the offset of the target in the image domain, directly obtains the actual position elevation of the target by utilizing the relation between the target speed and the image domain offset, simplifies the speed elevation two-dimensional search process into speed dimension search, thereby reducing the search amount, finally adopts a maximum likelihood estimation method to estimate the target speed, and realizes efficient mixed baseline SAR ground moving target parameter estimation.

Drawings

FIG. 1 is a flowchart of a method for estimating parameters of a ground moving object based on image domain shift characteristics according to the present invention;

FIG. 2 is a schematic diagram illustrating a process of updating inverse filter coefficients in a segmented manner according to the present invention;

fig. 3 is a diagram illustrating the relationship between the maximum amplitude ratio of the false target to the real target, the ratio of the memory of the proposed method to the memory of the conventional method, and the depth of focus.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, all other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort fall within the scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

As shown in fig. 1, an embodiment of the present application provides a method for estimating parameters of a ground moving object based on image domain offset characteristics, including the following steps:

s1: preprocessing data of each channel of the SAR;

the specific process of the pretreatment comprises the following steps: firstly, respectively carrying out SAR imaging processing on each channel data, wherein the traditional R-D algorithm is adopted for imaging in the embodiment; secondly, registering each image; and thirdly, removing the flat ground phase and compensating the ground elevation phase to finish data preprocessing.

The pixel unit signal vector containing the moving object on the image can be expressed as:

X＝T+C+N

wherein T ═ σ_Texp(jφ_T)a(h_T-h_C,v_T)，C＝σ_Cexp(jφ_C) a (0,0), N is white Gaussian noise,σ_T、φ_T、σ_C、φ_Crepresenting the amplitude and phase, h, of moving targets and clutter, respectively_T、h_CRespectively representing the terrain height, v, of the position of the moving target and the clutter_TRepresenting the moving target speed.

Elevation phase:

wherein h is the ground height, x_mFor the horizontal and vertical track base line, z_mIs a vertical track base line, theta is a radar ground clearance angle, R₀The target distance radar slant distance is adopted, lambda is the wavelength, and the corner mark m represents the channel number;

velocity phase:

wherein v is_xFor horizontal and vertical track direction velocity, x_TAs horizontal and vertical track direction coordinates, d_mTo be the length of the base line along the track, v_pIs the platform airspeed.

S2: clutter in the image is suppressed by utilizing self-adaptive processing, moving target detection of the preprocessed image is realized, and the position of a target in an image domain is obtained;

the method comprises the following steps:

the array weight vector for suppressing clutter is

w＝μR^-1v

Theoretically v should be equal to a (h)_T-h_C,v_T) However, since the target altitude speed is unknown, [1, 0., 0 ] is used here]^TInstead. The covariance matrix is estimated from the independent co-distributed samples,l is the number of independent identically distributed samples, s_iIs an array vector of the samples and is,is an array vector s of samples_iMu represents a normalization constant, and after suppression, CFAR detection is performed to obtain the target image domain position [ R'₀,Y′]。

S3: dividing a set speed measurement range into speed search grids, and calculating the original positions of the targets corresponding to the speeds based on the positions of the targets in the image domain;

the specific process of the step is as follows:

dividing a speed grid into a set speed measuring range, and calculating the offset of a moving target in an image domain for each speed value v in the grid;

wherein f is_dSince the running speed of the radar-mounted platform (airplane) is much higher than that of a moving target on the ground at 2vcos θ/λ, v is_p＞＞v_y，The corresponding possible original position of the target is [ R ]₀(v),Y(v)]＝[R′₀-Δr(v),Y′-Δy(v)]。

According toExternal DEM (digital elevation model) data is used for reading the possible position ground elevation, namely, each speed in the speed grid corresponds to a position coordinate, and the position corresponds to an elevation value h (R)₀(v),Y(v))。

S4: target speed by maximum likelihood estimation

Wherein R 'represents a covariance matrix of clutter, R'^-1Is the inverse of matrix R ', X' represents the vector of the multi-channel echo signals, a (v)^HIs the conjugate of a (v), h (R)₀(v) Y (v) represents the target true location elevation, h (R'₀Y') represents the target image field position elevation,indicating the elevation phase resulting from the elevation difference,indicates the velocity phase, M is 1,2 … M-1.

Fig. 2 visually shows the maximum likelihood estimation parameter search process. All points in the search grid of fig. 2 need to be searched if the offset of the moving object in the image domain is not considered. The offset of the moving target in the image domain is considered, the actual position elevation of the target is directly obtained, the speed elevation two-dimensional searching process is simplified into speed dimension searching, and therefore the searching amount is reduced, and therefore only one-dimensional searching is needed to be conducted on the point marked by the circle. The search volume is greatly reduced. Further, assume that the velocity ambiguity range determined by the antenna configuration is (-15m/s,15 m/s), the target velocity is 10m/s, the elevation is-7 m, in FIG. 2, the two velocity dimensions are represented by two bold points with coordinates of 10m/s and-20 m/s and the same height, if the offset of the moving object in the image domain is not taken into account, since the two points have the same steering vector, the output results are the same, i.e., there are ambiguity points for parameter estimation, while a one-dimensional search is performed taking into account the offset of the moving object in the image domain, the elevation of a sampling point at the target fuzzy speed of-20 m/s is 17m, and is inconsistent with the real elevation of the target, so that the mismatching of the guide vector is caused, and the output amplitude is reduced.

The effect of the present invention is further explained by the scene simulation test. The radar is directly viewed from the side, the noise-to-noise ratio is 35dB, and the signal-to-noise ratio is 7 dB. The target axial velocity was 12.5m/s, corresponding to a radial velocity of 11,81 m/s. The simulation parameters are shown in the table:

the number of sampling points of the whole scene is 2048 × 4096. The distance sampling frequency is 240MHz, the corresponding sampling point spacing is 0.625m, and the azimuth sampling spacing is 0.425 m. The moving object coordinates (x, y) ═ 61365m,0, which corresponds to the 533 th distance element and the 2049 th orientation element.

After moving object detection, the distance units and the orientation units of the object in the image domain are 531 and 1046. Fig. 3 shows the resulting amplitude curve, searched according to the method proposed in the present application, with a peak at 11.81 m/s. And (4) obtaining corresponding target distance and azimuth offset as 2.4 distance units and 1003.1 azimuth units, and obtaining the real coordinates of the target after position re-correction.

Due to the nonlinearity of the channel configuration, if and only if the ground elevation corresponding to the fuzzy speed is consistent with the elevation corresponding to the real speed, the guide vectors at the two sampling points are completely consistent to form two peak values with the same height. At the fuzzy speed of the target, 13.69m/s, the difference between the target fuzzy speed and the target real position elevation is large, so that the deviation of the guide vector is large, and the obtained amplitude is low. It can also be seen from fig. 3 that the velocity ambiguity problem can be overcome by the method of the present invention because the amplitude at the ambiguity velocity-13.69 m/s is significantly lower than the amplitude at the true velocity due to the difference in elevation.

Therefore, the invention provides a ground moving target parameter estimation method based on image domain offset characteristics, which divides a set speed measurement range into speed search grids, calculates the offset of a moving target in an image domain for each speed value in the grids, calculates the original target position corresponding to each speed based on the position and the offset of the target in the image domain, reduces the search amount by utilizing the relation between the target speed and the image domain offset, and finally estimates the target speed by adopting a maximum likelihood estimation method to realize efficient mixed baseline SAR ground moving target parameter estimation.

The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it will be understood by those skilled in the art that various changes and modifications may be made herein without departing from the spirit and scope of the invention as defined in the appended claims.