A target tracking system based on multi-channel detection

Abstract

The present invention discloses a target tracking system based on multi-channel detection, belonging to the field of target tracking technology. It solves the problem in existing tracking systems of easily losing the tracked target, resulting in unstable tracking. The target tracking system includes an imaging unit, a target positioning unit, and a target tracking unit, which are electrically connected in sequence. The imaging unit includes a sub-aperture carrier removal and fast time matched filtering processing module, a module for compensating for additional Doppler centroid deviation caused by azimuth deviation of dual-channel antennas, and a convolution processing module. The target positioning unit includes a compensation module and a micro-motion target positioning module. The target tracking unit is used to track the target. The present invention can be used for target tracking and guidance on a rotating platform, achieving stable tracking of the target.

Description

Technical Field

[0001] The present invention relates to the technical field of target tracking, and in particular to a target tracking system based on multi-channel detection. Background Art

[0002] When tracking a target, the rotation of the target in the image often leads to target tracking loss. Existing target tracking algorithms cannot meet this application condition, which has a great impact on the performance of the guidance system. Existing technologies urgently need to be studied and improved. Summary of the Invention

[0003] In view of the above analysis, the present invention aims to provide a target tracking system based on multi-channel detection to solve the problem in the prior art that the tracking method easily loses the tracking target and causes tracking instability.

[0004] The purpose of the present invention is mainly achieved through the following technical solutions:

[0005] The present invention provides a target tracking system based on multi-channel detection, comprising an imaging unit, a target positioning unit and a target tracking unit electrically connected in sequence;

[0006] The imaging unit includes a sub-aperture carrier removal and fast time matched filtering processing module, a module for compensating for additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, and a convolution processing module;

[0007] The target positioning unit includes a compensation module and a micro-motion target positioning module;

[0008] The data output end of the sub-aperture carrier frequency removal and fast time matched filtering processing module is connected to the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the data output end of the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna is connected to the convolution processing module, the data output end of the convolution processing module is connected to the compensation module, and the data output end of the compensation module is connected to the micro-motion target positioning module;

[0009] The target tracking unit obtains continuous frame images from the target positioning unit and enters the tracking control process after determining the initial frame image that the target has captured;

[0010] During the tracking control process, subsequent frame images acquired after the initial frame image are rotated frame by frame to eliminate the rotation angle with the initial frame image; correlation filtering is performed on each rotated image frame by frame to detect the coordinates of the target point; and, in the one-by-one correlation filtering detection, the correlation filtering model is iteratively updated; the detected target point coordinates are converted back to the image before rotation to obtain the target point coordinates in the image before rotation; and based on the target point coordinates, the line of sight angular velocity of the rotating platform is calculated and output as a tracking signal to the rotating platform to control the posture of the rotating platform.

[0011] Furthermore, the tracking unit includes:

[0012] An image receiving module is used to obtain continuous frame images from the target positioning unit;

[0013] An initial frame image determination module is used to capture a target from continuous frame images and use the image frame where the target is captured as the initial frame image for tracking;

[0014] An image rotation module is used to rotate subsequent frame images obtained after the initial frame image to eliminate the rotation angle with the initial frame image to obtain a rotated image;

[0015] An image feature extraction module is used to extract features from the initial frame image and the rotated image to obtain a feature matrix;

[0016] The correlation filtering module is used to perform correlation filtering on the rotated images frame by frame to detect the coordinates of the target points; and in the correlation filtering detection one by one, the correlation filtering model is updated through iteration;

[0017] A coordinate conversion module is used to convert the detected target point coordinates back to the image before rotation to obtain the target point coordinates in the image before rotation;

[0018] The tracking signal calculation module is used to calculate the line of sight angular velocity of the rotating platform according to the coordinates of the target points in the pre-rotation images one by one, and output it as a tracking signal to the rotating platform to control the posture of the rotating platform.

[0019] Furthermore, in the image rotation module, the calculation process of eliminating the rotation angle of the image by rotation is:

[0020] 1) According to the posture data of the rotating platform, establish the transformation matrix T from the point in the world coordinate system to the point in the imaging coordinate system:

[0021]

[0022] Where φ, θ, and γ are the imaging heading angle, pitch angle, and roll angle, respectively; T -1 is the transformation matrix from the imager coordinate system to the earth coordinate system:

[0023] 2) Find the inverse matrix T of the transformation matrix T -1 Obtain the transformation matrix from the imager coordinate system to the earth coordinate system;

[0024]

[0025] 3) Get the image rotation angle ξ:

[0026]

[0027] Furthermore, the image rotation module performs bilinear interpolation on the rotated image to obtain a grayscale image including the grayscale value of each pixel in the image.

[0028] Furthermore, in the micro-motion target positioning module, the phases of the two images are extracted, and the interference phase is detected by setting a certain threshold phase, the stationary target is canceled, and then the cancellation result is modulo-ed to obtain the micro-motion target energy.

[0029] Furthermore, in the sub-aperture carrier removal and fast time matched filtering processing module, the sub-aperture carrier removal and fast time matched filtering processing are performed, and the echo signal obtained is:

[0030]

[0031]

[0032] in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

[0033] Furthermore, in the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna is compensated, and the compensation function is:

[0034]

[0035] Where, j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

[0036] Furthermore, in the module for compensating the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the compensation function C1(t a ) directly multiplied by J p1 To compensate, J p2 The echo signal after compensation is unchanged:

[0037]

[0038]

[0039] in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

[0040] Furthermore, in the convolution processing module, the azimuth reference function is used Respectively with J p1 and J p2 Perform convolution processing and obtain the imaging result after convolution processing:

[0041]

[0042]

[0043] Among them, A3(t r ,t a ) is an additional modulation term; θ(t a) is the transformation angle; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; B d is the Doppler bandwidth of the echo signal, θ(t a ) is the transformation angle.

[0044] Furthermore, in the compensation module, the compensation function C is used 12 (t a ) interpolates and registers the two complex images to compensate for the phase error caused by the position deviation of the receiving aperture. The compensation function C 12 (t a )for:

[0045]

[0046] Where j is the imaginary unit, k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

[0047] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0048] (1) The present invention can use the change in Doppler frequency of echoes from different antennas to offset stationary targets and extract moving targets. It can also use the relationship between the phase difference between two SAR images and the interferometric SAR system parameters and target motion parameters to obtain target motion parameter information. The method of the present invention is simple and highly operational, combining SAR's high-resolution imaging of ground targets with micro-motion target detection. In subsequent engineering transformation and practical applications, it can detect, locate, and track high-value targets, providing useful information for battlefield situation assessment, command, and control, and is of great significance to reconnaissance and battlefield perception.

[0049] (2) The target tracking method provided by the present invention has a wide range of applications and can be generally applied to photoelectric tracking and guidance in rotating platforms.

[0050] (3) The algorithm has low computational complexity and good real-time performance, and can meet real-time computing requirements on commonly used embedded hardware platforms.

[0051] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages will become apparent from the description or be understood through practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the embodiments of the description and the contents particularly pointed out in the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0053] Figure 1 Schematic diagram of the composition of the target tracking system based on multi-channel detection of the present invention;

[0054] Figure 2 A schematic diagram of a dual-channel SAR system according to the present invention;

[0055] Figure 3 This is a schematic diagram of the imaging results of a ship and a micro-moving target according to the present invention;

[0056] Figure 4 Schematic diagram of the micro-motion target interference detection result of the present invention;

[0057] Figure 5 Schematic diagram of the repositioning result of the micro-movement target of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0059] The present invention provides an inversion positioning system for micro-movement targets based on multi-channel detection, such as Figure 1As shown, the inversion positioning system includes an electrically connected imaging unit, a target positioning unit and a target tracking unit; the imaging unit includes a sub-aperture carrier removal and fast time matched filtering processing module, a module for compensating for an additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, and a convolution processing module; the target positioning unit includes a compensation module and a micro-motion target positioning module; the data output end of the sub-aperture carrier removal and fast time matched filtering processing module is connected to the module for compensating for an additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the data output end of the module for compensating for an additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna is connected to the convolution processing module, the data output end of the convolution processing module is connected to the compensation module, and the data output end of the compensation module is connected to the micro-motion target positioning module.

[0060] The target tracking unit obtains continuous frame images from the target positioning unit, and enters a tracking control process after determining the initial frame image in which the target has been captured; during the tracking control process, subsequent frame images obtained after the initial frame image are rotated frame by frame to eliminate the rotation angle with the initial frame image; correlation filtering is performed on the rotated images frame by frame to detect the coordinates of the target point; and, in the one-by-one correlation filtering detection, the correlation filtering model is iteratively updated; the detected target point coordinates are converted back to the image before rotation to obtain the target point coordinates of the target point in the image before rotation; and based on the target point coordinates, the line of sight angular velocity of the rotating platform is calculated and output as a tracking signal to the rotating platform to control the posture of the rotating platform.

[0061] For the imaging unit and target positioning unit, when they are working, they first perform SAR imaging on the two echo signals respectively; then interpolate and align the two complex images; then conjugate multiply the two complex image data to extract the phase; by setting a certain threshold phase, the interference phase is detected. If the interference phase exceeds the threshold phase, it is determined that the target exists.

[0062] It should be noted that the radar coordinate system is defined as follows Figure 2 As shown: R0 is the distance from the radar to the target, the target rotation radius is r, r<<R o , the speed is ω, and the initial phase is t a is the flight time, D a is the phase center distance between the two antennas of the dual-channel SAR system. Antenna No. 2 transmits, and the echo signal is received by antenna No. 1 and antenna No. 2 at the same time. The radar coordinate system (X, Y, Z) is established with antenna No. 2 as the origin. The origin is always fixed on the phase center of antenna No. 2. The SAR carrier moves along the positive direction of the X axis at a speed of V a Flight; Assume that the position of the target P in the radar coordinate system is (x0, y0, -h), and the flight speed of the SAR platform is (V a,0,0), the phase center position of antenna No. 1 is (0,0,D a ).

[0063] It should be noted that Figure 2 In the figure, the dotted line represents a plane parallel to the XOY plane.

[0064] In the sub-aperture carrier removal and fast time matched filtering processing module, the two sub-apertures are orthogonally demodulated to remove the carrier frequency, and matched filtering is performed by a matched filter in the fast time domain. The obtained echo signal is:

[0065]

[0066]

[0067] in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

[0068] In the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation between the dual-channel antennas, additional Doppler centroid deviation is caused by the azimuth deviation between the dual-channel antennas, so compensation is required. The compensation function is as follows:

[0069]

[0070] Where, j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

[0071] Using the compensation function C1(t a ) directly multiply by J in the previous step p1 To compensate, J p2The specific compensation process is as follows:

[0072]

[0073] That is, the echo signal of antenna 1 after compensation is:

[0074]

[0075]

[0076] in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

[0077] In the convolution processing module, the azimuth reference function is:

[0078]

[0079] Using the above azimuth phase reference function and J p1 and J p2 Perform convolution processing to obtain imaging results;

[0080]

[0081]

[0082] Among them, A3(t r ,t a ) is an additional modulation term; θ(t a ) is the transformation angle; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; Va is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; B d is the Doppler bandwidth of the echo signal, θ(t a ) is the transformation angle.

[0083] In the compensation module, the two complex images are interpolated and registered using the following compensation function to compensate for the phase error caused by the azimuth position deviation of the receiving aperture.

[0084] Specifically, after imaging processing, since the receiving aperture has an azimuth position deviation, it is necessary to compensate for the phase error caused by this position deviation before performing ground clutter cancellation.

[0085] The compensation function is shown below:

[0086]

[0087] Using the compensation function C 12 (t a ) multiplied by I P1 , the result is approximately equal to I P2 The specific calculation process is as follows:

[0088]

[0089]

[0090] I p1 (t r ,t a )*C 12 (t a )≈I p2 (t r ,t a )

[0091] It should be noted that in the above calculation process, x0=V a *t a .

[0092] Where, The term actually corresponds to the compensation function Compensation function C1(t a) is because the time domain linear term corresponds to the frequency domain shift, so compensation is performed to align the center. However, the image obtained after imaging is a dual time domain image. Therefore, the phase before compensation corresponds to the phase error caused by the antenna aperture, which can be seen to be independent of the starting position x0 of the target point; where j is the imaginary unit, k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

[0093] In the micro-motion target positioning module, the phases of the two images are extracted. By setting a certain threshold phase, the interference phase is detected and the stationary target is canceled. That is, the two images are subtracted and canceled. Then the cancellation result is modulo (see the following formula) to obtain the micro-motion target energy.

[0094]

[0095] Take the modulus value of the cancellation result to obtain the micro-motion target energy:

[0096]

[0097] Among them, A3(t r ,t a ) is the additional modulation term; λ is the wavelength of the radar transmission signal; k is the signal modulation frequency; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the speed; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; B d is the Doppler bandwidth of the echo signal, θ(t a ) is the transformation angle.

[0098] After obtaining the energy of the micro-motion target, the maximum value of the micro-motion target energy is calculated, and the position of the maximum value is the position of the micro-motion target in the SAR image.

[0099] In one embodiment, through digital simulation, a ship is selected as an imaging target, wherein the ship warning radar has micro-motion target characteristics, and the simulation parameters used are shown in Table 1.

[0100] Table 1 Simulation parameters of micro-motion target

[0101]

[0102] According to the above simulation parameters, the ship target imaging results are as follows: Figure 3 As shown in Figure 2, we can see that the ship target can be well imaged and the outline of the ship is clear. At the same time, during the accumulation time, the radar rotation causes obvious Doppler stripes, such as Figure 3 As shown in the white circle.

[0103] The system provided by the present invention performs dual-channel image interference cancellation and performs micro-motion target detection based on the detection threshold, and extracts the micro-motion target results as follows: Figure 4 According to the relationship between the dual-channel interferometric phase and the rotation parameters, the relocation of the rotating radar target is realized, and the results are shown in Figure 5 As shown in the figure, when the SNR is better than 15dB, the corresponding target positioning accuracy can reach 1 / 15 of the beam width, and is better than 0.5° when the beam width is less than 7°.

[0104] It should be emphasized that after the target is located, the target tracking unit is used to track the target. It should be emphasized that the imaging unit and the positioning unit of the present invention can obtain a series of imaging results at consecutive moments; the previous image at adjacent moments is defined as the initial frame image, and the image at the next moment is defined as the initial frame image. Based on the obtained series of imaging results at consecutive moments, the present invention can also track the target. The specific structure of the target tracking unit is:

[0105] like Figure 1 As shown, the imaging unit and the target positioning unit are mounted on a rotating rotating platform, and the imaging unit, the target positioning unit and the rotating platform are electrically connected to a tracking unit for target tracking; the tracking unit for target tracking obtains continuous frame images from the target positioning unit, and enters a tracking control process after determining the initial frame image in which the target has been captured; in the tracking control process, the subsequent frame images obtained after the initial frame image are rotated frame by frame to eliminate the rotation angle with the initial frame image; correlation filtering is performed on the rotated images frame by frame to detect the coordinates of the target point; and in the one-by-one correlation filtering detection, the correlation filtering model is iteratively updated; the detected target point coordinates are converted back to the image before rotation to obtain the target point coordinates in the image before rotation; and based on the target point coordinates, the line of sight angular velocity of the rotating platform is calculated and output as a tracking signal to the rotating platform to control the posture of the rotating platform.

[0106] Specifically, such as Figure 2 As shown, the tracking unit includes: an image receiving module, an initial frame image determination module, an image rotation module, an image feature extraction module, a correlation filtering module, a coordinate conversion module and a tracking signal calculation module;

[0107] An image receiving module is used to obtain continuous frame images from the target positioning unit;

[0108] An initial frame image determination module is used to capture a target from continuous frame images and use the image frame where the target is captured as the initial frame image for tracking;

[0109] An image rotation module is used to rotate subsequent frame images obtained after the initial frame image to eliminate the rotation angle with the initial frame image to obtain a rotated image;

[0110] An image feature extraction module is used to extract features from the initial frame image and the rotated image to obtain a feature matrix;

[0111] The correlation filtering module is used to perform correlation filtering on each rotated image frame by frame to detect the target point coordinates (or directly use the target point coordinates obtained by the target positioning unit); and, in the correlation filtering detection one by one, iteratively update the correlation filtering model;

[0112] A coordinate conversion module is used to convert the detected target point coordinates back to the image before rotation to obtain the target point coordinates in the image before rotation;

[0113] The tracking signal calculation module is used to calculate the line of sight angular velocity of the rotating platform according to the coordinates of the target points in the pre-rotation images one by one, and output it as a tracking signal to the rotating platform to control the posture of the rotating platform.

[0114] The initial frame image determination module is connected to the image receiving module, obtains continuous frame images from the image receiving module to capture the target, and uses the frame image after completing the target capture as the initial frame image for tracking;

[0115] Specifically, target capture in the initial frame image determination module can be performed using existing target recognition technologies such as target edge extraction and target recognition, or learning and training a classifier, or training a neural network. Any of these existing target capture technologies does not affect the inventiveness of the target tracking system of the present invention.

[0116] Specifically, in the image rotation module, the calculation process of eliminating the rotation angle of the image by rotation is:

[0117] 1) According to the posture data of the rotating platform, establish the transformation matrix T from the point in the world coordinate system to the point in the imaging coordinate system:

[0118]

[0119] Where φ, θ, and γ are the imaging heading angle, pitch angle, and roll angle, respectively; T -1 is the transformation matrix from the imager coordinate system to the earth coordinate system:

[0120] 2) Find the inverse matrix T of the transformation matrix T -1 Obtain the transformation matrix from the imager coordinate system to the earth coordinate system;

[0121]

[0122] 3) Get the image rotation angle ξ:

[0123]

[0124] Specifically, after the image is rotated in the image rotation module, the corresponding relationship between the pixel coordinates of the rotated image and the pixel coordinates of the image before rotation is: Among them, (x, y) is the pixel coordinate of the image before rotation; (x r ,y r ) is the coordinate of any point on the image before rotation as the rotation axis; ξ is the image rotation angle.

[0125] Since the image coordinates obtained after rotation are generally floating point numbers, while the image pixel coordinates are generally integers, the image rotation module in this embodiment performs bilinear interpolation on the rotated image to obtain a grayscale image including the grayscale value of each pixel in the image.

[0126] Specifically, bilinear interpolation uses the grayscale values ​​of the four neighboring points to perform linear interpolation in two directions to obtain the grayscale value of the sampled point. That is, the grayscale value of the sampled point is calculated based on the corresponding weights determined according to the distance between the sampled point and its neighboring points. Where (x, y) coordinates represent the position of the pixel, and f(x, y) represents the grayscale value of the pixel. Its mathematical expression is:

[0127] f(i+u,j+v)=(1-u)(1-v)f(i,j)+(1-u)vf(i,j+1)+u(1-v)f(i+1,j)+uvf(i+1,j+1);

[0128] In the above formula, u and v range from (0, 1), representing the difference between the x-coordinate and y-coordinate of the sampling point and the upper left corner of the four adjacent points; i and j represent the x-coordinate and y-coordinate of the sampling point and the upper left corner of the four adjacent points.

[0129] Specifically, the image feature extraction module includes a gradient calculation module, a gradient direction histogram construction module, a normalization module and a feature matrix output module;

[0130] in,

[0131] Gradient calculation module, used to calculate the gradients in the X and Y directions, including gradient amplitude and angle;

[0132] Gradient values ​​in x and y directions:

[0133]

[0134] Gradient magnitude and gradient angle:

[0135]

[0136] In the above formula, f(x+1, y), f(x-1, y), f(x, y+1), and f(x, y-1) represent the grayscale values ​​of the pixels at the corresponding positions, respectively. x (x, y) represents the gradient value in the x direction at the coordinate (x, y), G y (x, y) represents the gradient value in the y direction at the coordinate (x, y), G(x, y) is the gradient amplitude, and θ(x, y) is the gradient angle.

[0137] The gradient direction histogram construction module is used to divide cells and construct the gradient direction histogram of each cell;

[0138] Specifically, when constructing the gradient direction histogram, a soft mapping strategy is adopted. When constructing the gradient direction histogram, for pixels at the intersection of multiple cells, the gradient of each pixel is distributed to the adjacent cells in a linear interpolation manner;

[0139] More specifically, P1 to P4 represent four pixel positions, corresponding to four cases of linear interpolation:

[0140] P1 is shared by 4 cells;

[0141] P2 is shared by the upper and lower cells;

[0142] P3 is shared by the left and right cells;

[0143] P4 is mapped to only one cell.

[0144] The mapping relationship of point P1:

[0145] Assuming that the coordinates of P1 are (x, y), the mapping formula of the gradient histogram vector of point P1 to the adjacent 4 cells is:

[0146]

[0147] In the above formula, cellsize represents the number of pixels contained in each cell. and Represents the rounding down of a and b, h p1 Represents the gradient histogram vector of point P1, h A 、h B 、h C 、h DRespectively represent the gradient histogram vectors of the four cells adjacent to point P1; preferably, the value of cellsize is 4.

[0148] The mapping formula of points P2 and P3 is:

[0149]

[0150] In the above formula, the calculation method of a and b is the same as the previous formula, h p2 Represents the gradient histogram vector of point P2, h p3 Represents the gradient histogram vector of point P3, h E 、h F Represents the gradient histogram vectors of the upper and lower cells adjacent to point P2, h G 、h H Represent the gradient histogram vectors of the left and right cells adjacent to point P3 respectively.

[0151] The mapping formula for point P4 is:

[0152] h I + = h P4 ;

[0153] In the above formula, h p4 Represents the gradient histogram vector of point P4, h I Represents the gradient histogram vector of the cell where the P4 point is located.

[0154] Normalization module, used to normalize the gradient histogram;

[0155] Specifically, when normalizing the gradient histogram, each cell is normalized, taking the cell as the basic unit, integrating 2*2 cells into 1 block, and using the L2 norm of the undirected gradient histogram vector to calculate the normalization factor of each block;

[0156] The cell unit E (coordinates (i, j)) belongs to the same four blocks B1, B2, B3, and B4 in space. The normalization factor of each block is:

[0157]

[0158] In the above formula, δ and γ are 1 or -1. Represents the gradient histogram vector of cell E, N δ，γ (i, j) is the block normalization factor of the cell unit E corresponding to the position (δ, γ).

[0159] Preferably, when both δ and γ are 1, N δ，γ(i, j) is the normalization factor of cell unit E in the lower right block, Represents the gradient histogram vector of the cell to the right of cell E, Represents the gradient histogram vector of the cell below cell E, Represents the gradient histogram vector of the cell to the lower right of cell E.

[0160] The normalization factor is used to normalize the elements of each gradient histogram vector, the set threshold is used to truncate the data, and the average of the four values ​​obtained after the normalization of each element is taken as the final result after the normalization of the current element.

[0161] Specifically, four normalization factors are used to normalize the elements of each gradient histogram vector, and a threshold of 0.2 is used for truncation. The four values ​​obtained after normalization of each element are averaged as the final result after normalization of the current element. The formula is as follows:

[0162]

[0163] In the above formula, is the gradient histogram vector of the cell unit E direction k in the kth quadrant with the coordinate (i, j) after taking the mean, Represents the gradient histogram vector of the cell unit E with coordinates (i, j) before taking the mean; N -1，-1 (i, j), N 1，-1 、N -1，1 、N 1，1 Respectively represent the normalization factors of cell unit E in the upper left, upper right, lower left, and lower right blocks; T 0.2 (x) represents the threshold cutoff of the variable x in the brackets, and average() means taking the average of multiple values ​​in the brackets;

[0164] T 0.2 In (x), if x is less than or equal to 0.2, the value is x; otherwise, the value is 0.2.

[0165] In addition, the undirected gradients normalized by different normalization factors are accumulated to obtain the sum of four gradient energies, which are used as the texture distribution features of a cell. The formula is as follows:

[0166]

[0167] In the above formula, nbins represents the number of quadrants divided by all gradient directions. Represents the cumulative sum of all directional gradient values; preferably, nbins is 6.

[0168] The feature matrix output module is used to connect the normalized gradient histogram vectors together to obtain the Hog features and form a feature matrix for output.

[0169] Specifically, in the correlation filter module, the correlation filter model is used for target detection, and the filter response matrix R is calculated; the peak value is searched in the filter response matrix R to obtain the target point coordinates (X T , Y T );

[0170] in, F -1 is the inverse Fourier transform; is the Fourier transform of the correlation filter model A; is the Fourier transform of the Gaussian kernel function; λ is the regularization parameter; It is the Fourier transform of the cross-correlation function of the correlation filter model template data X and the feature matrix Z of the image after rotation of the frame picture to be tracked; the correlation function σ is the width parameter of the Gaussian kernel function; X is the template data of the correlation filter model; is the conjugate of the Fourier transform of the template data; It is the Fourier transform of the feature matrix Z of the rotated image of the frame to be tracked.

[0171] In the process of performing correlation filtering on the rotated images frame by frame to detect the coordinates of the target points, the correlation filtering model is updated when performing correlation filtering detection on each new image frame to be detected.

[0172] The updating of the relevant filtering model includes updating the template data and updating the relevant model.

[0173] Template data updates include:

[0174] Before the correlation filter model is updated, the initial value of the template data X of the correlation filter model is: with the target point coordinates of the initial frame image as the center, an image of a set size is intercepted from the grayscale image of the initial frame image for normalization, and the Hog feature is extracted to obtain a feature matrix.

[0175] The update formula of the relevant filtering model template data X is:

[0176] X new =(1-α)*X old +α*X update ; where α is the update rate; X old is the template data of the last correlation filtering; X update The Hog feature matrix is ​​calculated by intercepting the search area with the target point coordinates of the rotated image of the latest tracked frame image as the center.

[0177] Among them, relevant model updates include:

[0178] Before the correlation filter model is updated, the correlation filter model is the Fourier transform of the Gaussian kernel function; λ is the regularization parameter; K XX is the autocorrelation function of the initial value of the template data X;

[0179] The update formula of the correlation filter model is A new =(1-α)*A ola +α*A update : Where α is the update rate; A old is the correlation filtering model of the last correlation filtering;

[0180] A update In order to obtain the relevant filtering model using the updated template data,

[0181]

[0182] α is the update rate, and its value is set to 0.012.

[0183] For example, when performing correlation filtering on an image after the initial frame image, the correlation filtering model is in the state before updating. At this time, the template data X of the correlation filtering model is the initial value; the correlation filtering model

[0184] When the initial frame image is tracked and the next frame image is tracked, the template data and the related model are updated. At this time, the template data of the related filtering model is updated so that the template data X new =(1-α)*X old +α*X update ; Among them, X old is the template data of the last correlation filtering, X update The Hog feature matrix is ​​calculated by intercepting the search area with the target point coordinates of the rotated image after the initial frame image as the center. new =(1-α)*A old +α*A update ; Where α is the update rate; A old is the correlation filtering model of the last correlation filtering; A update To use the updated template data X new Get the relevant filter model,

[0185] According to the above updating process, target detection is performed on all subsequent frame images through the updated correlation filter model.

[0186] Specifically, in the coordinate transformation module, the coordinates of the target point in the rotated image (X T , Y T ) is converted to the image before rotation, and the target point coordinates (X T , Y T ) is:

[0187] Where (x, y) is the coordinate of the point in the image before rotation; (x′, y′) is the pixel coordinate of the image after rotation; (x r ,y r ) is the coordinate of any point in the image before rotation; ζ is the image rotation angle.

[0188] Specifically, in the tracking signal calculation module, according to the formula Calculate the line-of-sight angular velocity of the rotating platform as the tracking output signal;

[0189] Among them, V x and V y is the angular velocity of the line of sight in the X and Y directions, X c and Y c is the coordinate of the center point in the X and Y directions of the image, R x and R y The angular velocity equivalents of the line of sight in the X and Y directions are set to 0.015 and 0.025 respectively.

[0190] In summary, the present invention achieves stable tracking during high-speed image rotation. The method of the present invention has a wide range of applications and can be generally applied to photoelectric tracking guidance in high-speed rotating platforms. The algorithm has low computational complexity and good real-time performance, and can meet real-time computing requirements on commonly used embedded hardware platforms. It has good adaptability.

[0191] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A target tracking system based on multi-channel detection, characterized in that: comprising an imaging unit, a target positioning unit and a target tracking unit electrically connected in sequence; The imaging unit includes a sub-aperture carrier removal and fast time matched filtering processing module, a module for compensating for additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, and a convolution processing module; The target positioning unit includes a compensation module and a micro-motion target positioning module; The data output end of the sub-aperture carrier removal and fast time matched filtering processing module is connected to the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the data output end of the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna is connected to the convolution processing module, the data output end of the convolution processing module is connected to the compensation module, and the data output end of the compensation module is connected to the micro-motion target positioning module; The target tracking unit obtains continuous frame images from the target positioning unit and enters the tracking control process after determining the initial frame image that the target has captured; During the tracking control process, subsequent frame images acquired after the initial frame image are rotated frame by frame to eliminate the rotation angle with the initial frame image; correlation filtering is performed on each rotated image frame by frame to detect the coordinates of the target point; and, in the one-by-one correlation filtering detection, the correlation filtering model is iteratively updated; the detected target point coordinates are converted back to the image before rotation to obtain the target point coordinates in the image before rotation; and based on the target point coordinates, the line of sight angular velocity of the rotating platform is calculated and output as a tracking signal to the rotating platform to control the posture of the rotating platform.

2. The target tracking system based on multi-channel detection according to claim 1, characterized in that: The tracking unit includes: An image receiving module is used to obtain continuous frame images from the target positioning unit; An initial frame image determination module is used to capture a target from continuous frame images and use the image frame where the target is captured as the initial frame image for tracking; An image rotation module is used to rotate subsequent frame images obtained after the initial frame image to eliminate the rotation angle with the initial frame image to obtain a rotated image; An image feature extraction module is used to extract features from the initial frame image and the rotated image to obtain a feature matrix; The correlation filtering module is used to perform correlation filtering on the rotated images frame by frame to detect the coordinates of the target points; and in the correlation filtering detection one by one, the correlation filtering model is updated through iteration; A coordinate conversion module is used to convert the detected target point coordinates back to the image before rotation to obtain the target point coordinates in the image before rotation; The tracking signal calculation module is used to calculate the line of sight angular velocity of the rotating platform according to the coordinates of the target points in the pre-rotation images one by one, and output it as a tracking signal to the rotating platform to control the posture of the rotating platform.

3. The target tracking system based on multi-channel detection according to claim 2, characterized in that: In the image rotation module, the calculation process of eliminating the rotation angle of the image by rotation is: 1) According to the posture data of the rotating platform, establish the transformation matrix T from the point in the world coordinate system to the point in the imaging coordinate system: Where φ, θ, and γ are the imaging heading angle, pitch angle, and roll angle, respectively; T -1 is the transformation matrix from the imager coordinate system to the earth coordinate system: 2) Find the inverse matrix T of the transformation matrix T -1 Obtain the transformation matrix from the imager coordinate system to the earth coordinate system; 3) Get the image rotation angle ξ:

4. The target tracking system based on multi-channel detection according to claim 3, characterized in that: In the image rotation module, bilinear interpolation is performed on the rotated image to obtain a grayscale image including the grayscale value of each pixel in the image.

5. The target tracking system based on multi-channel detection according to any one of claims 1 to 4, characterized in that: In the micro-motion target positioning module, the phases of the two images are extracted, and the interference phase is detected by setting a certain threshold phase, the stationary target is canceled, and then the cancellation result is modulo-ed to obtain the micro-motion target energy.

6. The target tracking system based on multi-channel detection according to claim 5, characterized in that: In the sub-aperture carrier removal and fast time matched filtering processing module, the sub-aperture carrier removal and fast time matched filtering processing are performed, and the echo signal obtained is: in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; T p is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

7. The target tracking system based on multi-channel detection according to claim 6, characterized in that: In the module for compensating for the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna is compensated, and the compensation function is: Where, j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

8. The target tracking system based on multi-channel detection according to claim 7, characterized in that: In the module for compensating the additional Doppler centroid deviation caused by the azimuth deviation of the dual-channel antenna, the compensation function C1(t a ) is directly multiplied by Jp1 for compensation, Jp2 remains unchanged, and the echo signal after compensation is: in, They represent the Doppler center frequency and modulation rate of the uniform rotation passive micro-interference echo received by the middle aperture respectively; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation rate; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; T p is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; θ(t a ) is the transformation angle.

9. The target tracking system based on multi-channel detection according to claim 8, characterized in that: In the convolution processing module, the azimuth reference function is used Respectively with J p1 and J p2 Perform convolution processing and obtain the imaging result after convolution processing: Among them, A3(t r ,t a ) is an additional modulation term; θ(t a ) is the transformation angle; λ is the wavelength of the radar transmission signal; j is an imaginary number; k is the signal modulation frequency; R0 is the distance from the radar to the target; r is the target rotation radius; r<<R o ;ω is the rotation speed; is the initial phase; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X axis; B is the signal bandwidth; Tp is the signal pulse width; c is the speed of light; t r Fast time; t a For slow time, J m (2kr) is the first kind of m-order Bessel function, where m is the order; B d is the Doppler bandwidth of the echo signal, θ(t a ) is the transformation angle.

10. The target tracking system based on multi-channel detection according to claim 9, characterized in that: In the compensation module, the compensation function C is used 12 (t a ) interpolates and registers the two complex images to compensate for the phase error caused by the position deviation of the receiving aperture. The compensation function C 12 (t a )for: Where j is the imaginary unit, k is the signal modulation frequency; R0 is the distance from the radar to the target; t a is the flight time; D a is the phase center distance between the two antennas of the dual-channel SAR system; V a is the flight speed of the SAR carrier along the positive direction of the X-axis.

Images