A method for optical measurement and correlation of space target motion trajectory

Abstract

The present application relates to a kind of space target motion trajectory optical measurement and associated method, comprising the following steps: S1, the camera image plane set to be identified point is obtained;S2, initialization is constructed target trajectory;S3, the position of next frame on image plane under target trajectory is predicted;S4, predicted point and measured point matching;S5, trajectory update;S6, confidence test;S7, target tracking.The method proposed in the present application compared with existing target trajectory associated method, the advantage is that: it can greatly reduce the false alarm rate of noise point target and improve the extraction rate of dim weak target, and the calculation complexity is low, can satisfy the real-time requirement of space vehicle on celestial body.

Description

Technical Field

[0001] This invention relates to the field of space target detection, and in particular to a method for optical measurement and correlation of the motion trajectory of a space target. Background Technology

[0002] During missions, satellites may be threatened by other space targets. This is mainly manifested in the potential collision risk that may arise when a satellite is drifting, descenting, hovering, approaching, or accompanying another satellite, due to excessively close proximity to unknown satellites. Therefore, the detection of space targets has become a hot topic.

[0003] Satellites are typically equipped with target detection sensors that image the sky at a specified location, obtaining the image points of the corresponding celestial targets on the image plane. Since the time, location, size, and speed of the space target's appearance are unknown, and no texture features are available, it is impossible to distinguish the target point from background noise points based on a single frame of detection information. However, because the exposure time of photoelectric detectors is often very short (less than 200ms), it can be assumed that the target's motion on the detector's image plane during the exposure time is uniform linear motion. Therefore, the trajectory information of the space target can be correlated based on the motion patterns of the image points across multiple frames, and the relative positional relationship between the space target and the satellite can be calculated.

[0004] Currently, methods for associating spatial target trajectories mainly fall into two categories. One is machine learning based on feature extraction, which builds a machine learning model to learn from multiple frames of star charts and extract the motion features of target points to associate and predict target trajectories. However, this method is slow and requires a large amount of computing resources; space-based processors cannot handle such complex calculations, and real-time performance cannot be guaranteed. The other category is 3D matched filtering, which achieves good association results for targets with stable brightness and velocity. However, when the target velocity is mismatched or noise and clutter interference do not conform to a Gaussian process, the matching performance of this method drops significantly.

[0005] How to efficiently and reliably correlate and correct the trajectory information of multi-frame target imaging has become a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an optical measurement and association method for the motion trajectory of a space target, which, while associating the trajectory of target imaging points in multiple frames of images, predicts and corrects the future trajectory information of the target in real time.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] A method for optical measurement and correlation of the motion trajectory of a space target, characterized by the following steps:

[0009] S1, Obtain the set of points to be identified on the camera image plane. Where N represents the number of imaging points on the camera image plane, (x,y) represents the centroid coordinates of the imaging points, and r represents the blur radius of the imaging points;

[0010] S2, if three points in three consecutive frames satisfy the condition of formula (1), then initialize and construct the target trajectory.

[0011] And add it to the trajectory library;

[0012]

[0013] in, These represent the x-coordinate, y-coordinate, and blur radius of the point to be identified on the image plane of the (k-2), (k-1), and (k) frames, respectively.

[0014] S3, predict the position of the target trajectory on the image plane in the next frame;

[0015] S4, in the set of points to be identified in the image plane of the (k+1)th frame, find the match with (Px) k ,Py k The closest measured point that is less than L pixels away. The matching of predicted points and measured points is completed, and the value of L can be determined by engineering experience;

[0016] S5, Determine the measured point Does it exist, and update the trajectory accordingly;

[0017] S6, Calculate the trajectory confidence α i And the trajectory confidence α i Perform verification;

[0018] S7. Step S7 applies Kalman filtering to the trajectory that passed the confidence test in step S6 starting from the next frame to track the target. By fusing the measured values ​​with the predicted values, the predicted trajectory information is corrected, and the association of the target trajectory is completed.

[0019] Furthermore, the set of points to be identified on the camera image plane obtained in step S1 includes the set of points to be identified on the image plane of the (k-2)th frame, the (k-1)th frame, the kth frame, and the (k+1)th frame.

[0020] Furthermore, step S2 also includes:

[0021] Traverse the set of points to be identified on the image plane of frame (k-2), frame (k-1), and frame (k) until all trajectories that meet the requirements are constructed. At the same time, delete the points that have been constructed into trajectories from the corresponding point set.

[0022] Furthermore, the target trajectory constructed in step S2 satisfies the data structure of formula (2):

[0023]

[0024] Among them, (x k ,y k ),(Lx k ,Ly k ) represent the phase positions of the target in frame k and frame (k-1), respectively. k ,Py k MatchNum represents the predicted position of the target trajectory on the image plane in the next frame. k TotalNum represents the total number of matching points in the trajectory. k This represents the total number of points in the trajectory.

[0025] Furthermore, in step S3, the position of the target trajectory on the image plane in the next frame is predicted according to formula (3):

[0026] Px k =2x k -Lx k

[0027] Py k =2y k -Ly k (3).

[0028] Further, step S5 includes:

[0029] If the measured points in step 4 If it exists, update the trajectory according to formula (4);

[0030]

[0031] like If it does not exist, then update the trajectory according to formula (5):

[0032]

[0033] Further, step S6 includes:

[0034] When the total number of trajectory points in the trajectory library is greater than the trajectory length threshold, the trajectory confidence α is calculated for the target trajectory according to formula (6). i If α i Greater than the high confidence threshold α H If α is true, then the trajectory is the trajectory of the spatial target, and subsequent trajectory association or target tracking calculations can be performed; if α is true... i Less than the low confidence threshold α LIf α i If the confidence level is between the low and high thresholds, then repeat steps S1 to S6 starting from the next frame.

[0035]

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] (1) This invention divides the target trajectory association into several modules, including target initialization construction, target point prediction, target point and measured point matching, confidence test, and target tracking association. All calculations are based on the detector image coordinate system as the reference system, without complex numerical calculations, which meets the real-time requirements of spacecraft components.

[0038] (2) Due to the limitations of the detector's detection capability, some faint targets may fail to be detected in certain frames during association. If the detection rate of consecutive frames is strictly limited, the trajectories of these faint targets will not be associated. This invention introduces a confidence test module, which does not limit the target decision condition to being detected in 3 consecutive frames, but uses the ratio of the number of valid points in the trajectory to the total number of points as the decision condition. Combined with the upper and lower limit threshold parameters given by the stargazing experiment, this allows the trajectories of faint targets to be associated and extracted as much as possible while satisfying the target's motion law.

[0039] (3) At the same time, spatial noise and stray light have a certain probability of appearing in three consecutive frames that meet the target motion law and are thus constructed as the target trajectory, causing false alarms. However, noise still conforms to the principle of randomness over a long period of time, so when the trajectory length accumulates to a certain threshold, the confidence test module can reduce the false alarm rate to a certain extent.

[0040] (4) This invention uses Kalman filtering to predict, fuse, and associate target trajectories based on the confidence level test results. If only the trajectory information of two consecutive frames is used to predict the trajectory position of the next frame, the predicted position will deviate significantly from the actual trajectory when the satellite or target suddenly maneuvers. Kalman filtering not only predicts the future trajectory based on prior position information, but also fuses the measured values ​​with the predicted values ​​to correct the trajectory information in real time, thus achieving efficient and rapid association of target trajectories.

[0041] (5) The calculation of the present invention does not involve complex convolution, integration, etc., and fully meets the computational complexity of the spacecraft's onboard processor. Its real-time performance is superior to other methods in the field of the present invention. Attached Figure Description

[0042] Figure 1 This is a flowchart of an optical measurement and correlation method for the motion trajectory of a space target according to the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and by providing a detailed description of a preferred embodiment.

[0044] like Figure 1 As shown, the optical measurement and correlation method for the motion trajectory of a space target includes the following steps:

[0045] S1. Obtain the set of points to be identified on the camera image plane. Where N represents the number of imaging points on the camera image plane, (x,y) represents the centroid coordinates of the imaging points, and r represents the blur radius of the imaging points.

[0046] In an embodiment of the present invention, the set of points to be identified on the camera image plane includes the set of points to be identified on the image plane of the (k+1)th frame, the kth frame, the (k-1)th frame, and the (k-2)th frame.

[0047] S2. If three points in three consecutive frames satisfy the condition of formula (1), then initialize and construct the target trajectory and add it to the trajectory library. Traverse the image plane point set to be identified in the (k-2)th frame, the (k-1)th frame, and the kth frame until all trajectories that meet the requirements are constructed. At the same time, delete the points that have been constructed as trajectories from the corresponding point sets.

[0048]

[0049] in, These represent the x-coordinate, y-coordinate, and blur radius of the point to be identified on the image plane of the (k-2), (k-1), and (k) frames, respectively.

[0050] In an embodiment of the present invention, the trajectory satisfies the data structure of formula (2):

[0051]

[0052] Among them, (x k ,y k ),(Lx k ,Ly k ) represent the image plane positions of the target in the k-th frame and the (k-1)-th frame, respectively. k ,Py k The ) represents the predicted position of the target trajectory on the image plane in the next frame, MatchNumk represents the total number of matching points in the trajectory, and TotalNum k This represents the total number of points in the trajectory.

[0053] S3. Predict the position of the target trajectory on the image plane in the next frame according to formula (3):

[0054]

[0055] S4. Find the match between (Px) and the set of points to be identified in the (k+1)th frame image plane. k ,Py k The closest measured point that is less than L pixels away. Complete the matching of predicted points with measured points.

[0056] In an embodiment of the present invention, L is 3 pixels.

[0057] S5. If the measured points in step S4 If it exists, update the trajectory according to formula (4); if If it does not exist, the trajectory is updated according to formula (5).

[0058]

[0059]

[0060] S6. When the total number of points on some trajectories in the trajectory library exceeds the trajectory length threshold, the trajectory confidence α is calculated for these target trajectories according to formula (6). i If α i Greater than the high confidence threshold α H If α is true, then the trajectory is a spatial target trajectory, and subsequent trajectory association or target tracking calculations can be performed. i Less than the low confidence threshold α L If α i If the confidence level is between the low and high thresholds, then repeat steps 1 to 6 starting from the next frame.

[0061]

[0062] In an embodiment of the present invention, the trajectory length threshold is set to 10, the high confidence threshold is set to 0.85, and the low confidence threshold is set to 0.75.

[0063] S7. Starting from the next frame, use Kalman filtering to track the target trajectory that passed the confidence test in step S6. By fusing the measured values ​​with the predicted values, the predicted trajectory information is corrected, and the association of the target trajectory is completed.

[0064] This invention enables the association and trajectory prediction of imaging trajectories of space targets on the detector's image plane. The confidence verification module significantly reduces the false alarm rate for noisy and stray light targets, while greatly improving the extraction rate of weak targets. The Kalman filter target tracking method not only predicts the future trajectory based on prior position information but also fuses measured and predicted values ​​to correct trajectory information in real time, achieving efficient and rapid association of target trajectories. The calculations in this invention do not involve complex convolutions or integrals, fully meeting the computational complexity requirements of spacecraft processors, and its real-time performance surpasses other methods in this field.

[0065] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for optical measurement and correlation of the motion trajectory of a space target, characterized in that, Includes the following steps: S1, Obtain the set of points to be identified on the camera image plane. Where N represents the number of imaging points on the camera's image plane. Indicates the centroid coordinates of the imaging point. Indicates the radius of the blur spot at the imaging point; S2, if three points in three consecutive frames satisfy the condition of formula (1), then initialize and construct the target trajectory and add it to the trajectory library; (1) in, These represent the x-coordinate, y-coordinate, and blur radius of the point to be identified on the image plane of the (k-2), (k-1), and (k) frames, respectively. S3, predict the position of the target trajectory on the image plane in the next frame, and record the position as the prediction point. ; S4, search for points in the image plane to be identified in the (k+1)th frame that match... The closest measured point that is less than L pixels away The matching of predicted points and measured points is completed, and the value of L is determined by engineering experience; S5, Determine the measured point Does it exist, and update the trajectory accordingly; S6, Calculate trajectory confidence. And the trajectory confidence Perform verification; S7. Step S7 uses Kalman filtering to track the target starting from the next frame for the trajectory that passed the confidence test in step S6. By fusing the measured value and the predicted value, the predicted trajectory information is corrected, and the association of the target trajectory is completed. The data structure for constructing the target trajectory in step S2 that satisfies formula (2) is as follows: (2) in, These represent the phase positions of the target in frame k and frame (k-1), respectively. This indicates the predicted position of the target trajectory on the image plane in the next frame. This indicates the total number of matching points in the trajectory. This represents the total number of points in the trajectory.

2. The method for optical measurement and correlation of the motion trajectory of a space target as described in claim 1, characterized in that, The set of points to be identified on the camera image plane obtained in step S1 includes the set of points to be identified on the image plane of the (k-2)th frame, the (k-1)th frame, the kth frame, and the (k+1)th frame.

3. The method for optical measurement and correlation of the motion trajectory of a space target as described in claim 1, characterized in that, Step S2 further includes: Traverse the set of points to be identified on the image plane of frame (k-2), frame (k-1), and frame (k) until all trajectories that meet the requirements are constructed. At the same time, delete the points that have been constructed into trajectories from the corresponding point set.

4. The method for optical measurement and correlation of the motion trajectory of a space target as described in claim 1, characterized in that, In step S3, the position of the target trajectory on the image plane in the next frame is predicted according to formula (3): (3)。 5. The method for optical measurement and correlation of the motion trajectory of a space target as described in claim 1, characterized in that, Step S5 includes: If the measured points in step 4 If it exists, update the trajectory according to formula (4); (4) like If it does not exist, then update the trajectory according to formula (5): (5)。 6. The method for optical measurement and correlation of the motion trajectory of a space target as described in claim 1, characterized in that, Step S6 includes: When the total number of trajectory points in the trajectory library is greater than the trajectory length threshold, the trajectory confidence level of the target trajectory is calculated according to formula (6). ,like Greater than the high confidence threshold If so, then the trajectory is the spatial target trajectory, and subsequent trajectory association or target tracking calculations will continue; if Less than the low confidence threshold If the trajectory is false alarm, it is a false alarm trajectory and is deleted from the trajectory database; if If the confidence level is between the low and high thresholds, then repeat steps S1 to S6 starting from the next frame. (6)。

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