CN113447925B - Ground moving object parameter estimation method based on image domain deviation characteristics - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9094—Theoretical aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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Abstract
The invention provides a ground moving target parameter estimation method based on image domain offset 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.
Description
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 offset 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 multitasking, and can simultaneously perform interferometric height measurement, wide-range high-resolution 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 shaped and installed at each part of a machine body in a distributed mode, 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 channels 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 offset 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 features 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;
and estimating the target speed by a maximum likelihood method based on the elevation data.
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 d ＝2vcosθ/λ，
Theta is the radar angle of ground clearance, v p Is the platform flying speed, R 0 Is 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 the position of the object in the image domain.
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' -1 Is the inverse of the matrix R ', X' represents the vector of the multi-channel echo signals, a (v) H Is the conjugate of a (v), h (R) 0 (v) Y (v)) represents a target true position elevation, h (R' 0 Y') represents the target image field position elevation,
indicating the elevation phase resulting from the elevation difference,
the velocity phase is shown as M =1,2 \8230am-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, based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection 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 T exp(jφ T )a(h T -h C ,v T )，C＝σ C exp(jφ C ) a (0, 0), N is white Gaussian noise,
σ T 、φ T 、σ C 、φ C representing the amplitude and phase, h, of moving targets and clutter, respectively T 、h C Respectively representing the terrain height, v, of the position of the moving target and the clutter T Representing the moving target speed.
Elevation phase:
wherein h is the ground height, x m For the horizontal and vertical track base line, z m Is a vertical track base line, theta is a radar ground clearance angle, R 0 The 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 x For horizontal and vertical track direction velocity, x T As horizontal and vertical track direction coordinates, d m To be the length of the base line along the track, v p Is 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 -1 v
Theoretically v should be equal to a (h) T -h C ,v T ) However, since the target altitude speed is unknown, [1,0,. ] 0,0 ] is used here] T Instead of this. The covariance matrix is estimated from the independent co-distributed samples,
l is the number of independent identically distributed samples, s i Is an array vector of the samples and,
is an array vector s of samples i Mu 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 d =2vcos θ/λ, v is a moving speed of the radar-mounted platform (airplane) which is much higher than a moving target on the ground p ＞＞v y ，
The corresponding possible original position of the target is [ R ] 0 (v),Y(v)]＝[R′ 0 -Δr(v),Y′-Δy(v)]。
And reading the possible position ground elevation according to external DEM (digital elevation model) data, 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' -1 Is the inverse of matrix R ', X' represents the vector of the multi-channel echo signals, a (v) H Is the conjugate of a (v), h (R) 0 (v) Y (v)) represents a target true position elevation, h (R' 0 Y') represents the target image field position elevation,
indicating the elevation phase resulting from the elevation difference,
the velocity phase is shown as M =1,2 \8230am-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. In addition, assuming that the velocity ambiguity range determined by the antenna configuration is (-15 m/s,15 m/s), the velocity of the target is 10m/s, and the elevation is-7 m, in fig. 2, two bold points represent two velocity dimension coordinates of 10m/s and-20 m/s, and have the same height, if the offset of the moving target in the image domain is not considered, the two points are traversed during the search process, because the two points have the same steering vector, the output result is the same, namely, fuzzy points of parameter estimation exist, and when the one-dimensional search is carried out by considering the offset of the moving target in the image domain, the elevation of the sampling point at the target ambiguity velocity of-20 m/s is 17m, which is inconsistent with the real elevation of the target, the steering vector is mismatched, and the output amplitude is reduced, so the method also has the ambiguity resolution capability.
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 7dB. The target was 12.5m/s along the X-axis with a corresponding radial velocity of 11,81m/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 0.425m. The coordinates of the moving target (x, y) = (61365m, 0), which correspond to the 533 th distance unit and the 2049 th azimuth unit.
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 11.81m/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 of-13.69 m/s, the difference between the target and the actual position elevation of the target 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 of-13.69 m/s is significantly lower than the amplitude at the true velocity due to the elevation difference.
Therefore, the invention provides a ground motion target parameter estimation method based on image domain offset characteristics, which comprises the steps of dividing a set speed measurement range into speed search grids, calculating the offset of a moving target in an image domain for each speed value in the grids, calculating the original target position corresponding to each speed based on the position and the offset of the target in the image domain, reducing the search amount by utilizing the relation between the target speed and the offset of the image domain, and finally estimating the target speed by adopting a maximum likelihood estimation method to realize efficient mixed baseline SAR ground motion target parameter estimation.
The present invention is capable of other embodiments, and various changes and modifications can be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (2)
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;
estimating a target speed based on the elevation data;
the pretreatment comprises the following steps: firstly, carrying out SAR imaging processing on echo data of each channel; secondly, registering each image; thirdly, removing the flat ground phase and compensating the ground elevation phase to finish data preprocessing;
dividing the set speed measurement range into speed search grids, and calculating the offset of the moving target in the image domain 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 d ＝2vcosθ/λ，
Theta is the radar angle of ground clearance, v p Is the platform flying speed, R 0 Is the target distance radar slant distance, lambda is the wavelength;
the corresponding original position of the target is [ R ] 0 (v),Y(v)]＝[R′ 0 -Δr(v),Y′-Δy(v)]Wherein, [ R' 0 ,Y′]Is the position of the object in the image domain;
the specific process of estimating the target speed by the maximum likelihood method based on the elevation data is as follows:
wherein R 'represents a covariance matrix of clutter, R' -1 Is the inverse of the matrix R ', X' represents the vector of the multi-channel echo signals, a (v) H Is the conjugate of a (v), h (R) 0 (v) Y (v)) represents the target true position elevation, h (R' 0 Y') represents the target image field position elevation,
indicating the elevation phase resulting from the elevation difference,
denotes the velocity phase, m =1,2…M-1。
2. The method according to claim 1, wherein the estimating the velocity of the object based on the elevation data is estimating the velocity of the object by using a maximum likelihood method.
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