CN111126131B - High-efficiency dark and weak space target identification method - Google Patents
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Abstract
A high-efficiency dark and weak space target identification method comprises the following steps: 1) imaging a star map to obtain a series of star maps containing dark and weak targets; 2) and (5) processing a star map and extracting massive bright spots. 3) Removing stars, namely removing most of star elements from all bright spots; 4) obtaining the motion characteristics of each block-shaped bright spot in the suspected target queue; 5) determining an ordered feature set; 6) and tracking the target, namely tracking the established confirmation target queue, predicting the target position of the next frame, calculating target parameters if the target position is successfully predicted, extracting accurate azimuth information, and adding the accurate azimuth information into the confirmation target queue. The method can remove interference elements such as stars, noise points and the like from the extracted bright points to the maximum extent, finally realize the tasks of detecting, extracting, identifying, tracking and the like of the space dim and weak target, and is particularly suitable for occasions with low signal-to-noise ratio, complex star map backgrounds and unknown information of various targets.
Description
Technical Field
The invention relates to a high-efficiency dark and weak space target identification method, and belongs to the technical fields of situation perception, space monitoring and the like.
Background
The rapid growth of non-catalogued micro targets in space orbits, such as millimeter-scale or centimeter-scale micro fragments and space objects in reverse orbits of spacecrafts, poses direct threats to the tasks of space station construction and maintenance, satellite protection and spacecraft launching, and the high-sensitivity detection technology faces practical requirements. More than 30000 fragments and satellites with a diameter of more than 10cm, about 50 thousands of 1 cm-10 cm, and more than 100 hundred million of less than 1cm, however, 23000 space targets which have been cataloged and targeted by ground-based radar and optoelectronic systems are difficult to monitor for targets with a low orbit less than 10cm and a high orbit less than 0.5 m.
Aiming at the extraction of point-like moving targets, Marchant and the like invented an interframe search method in the 90 s, the target can be effectively extracted by setting and updating an interframe search area and a threshold value for many times, and when the target interframe search area or the threshold value is unreasonable, the target can be lost or elements such as stars and the like can be mistaken for the target; yanagisawa and the like need to superpose a plurality of dark and weak star maps in order to solve the GEO fragment search, a plurality of fixed stars need to be overlapped, other targets which cannot be overlapped are regarded as suspected targets and then are distinguished, the realization idea is consistent with the inter-frame matching, and the map matching process is complex. Schildknecht et al propose a mask technique mainly used for processing original image information, requiring a mask region to achieve a certain accuracy, Bertin et al propose a cross-correlation algorithm requiring the provision of prior information such as the original star or target position. Other research schemes, such as Dawson proposed a maximum likelihood tracking algorithm, Vananti proposed an identification method that convolves the band image with different filters, and Kouprianov proposed a PSF fitting technique, are mainly directed at band objects, not the main study objects. The spatial non-cooperative multi-target capturing and tracking algorithm provided by the Zhanming et al has high efficiency, but needs manual experience to set a matching threshold, and when the threshold is set too large or too small, the same problem of interframe search adopted by the Marchant et al can occur. The track cataloging method provided by the trembler et al searches the target by adopting a threshold radius searching method, and when the target running speed is higher, the possibility of losing the target is higher. The space-based space target identification method proposed by threefruit wind and the like adopts a reference star alignment method, and has similar problems with the stacking method adopted by Yanagisawa and the like. The method can be seen that the existing method has a high degree of dependence on prior information, especially needs to be determined empirically for setting a threshold, and cannot adapt to the problem that the spatial target has fast motion change and cannot complete classification identification.
In order to solve the problems, a high-efficiency dark and weak space target identification method is provided, prior information is not needed, online classification, association and extraction can be carried out on non-cooperative targets, and the identification accuracy and speed are greatly improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method overcomes the defects of the prior art, provides a high-efficiency dark and weak space target identification method, and solves the problems that the prior method has large dependence on prior information, is easy to generate false identification under the multi-target condition, has low extraction speed and can not be classified on line.
The technical scheme of the invention is as follows:
a high-efficiency dark and weak space target identification method comprises the following steps:
1) imaging a starry sky by using a high-resolution camera to obtain a K frame dark field image, wherein the dark field image comprises a plurality of space moving objects; k is a positive integer;
2) sampling the K frames of dark field images obtained in the step 1), extracting one dark field image as a sampling image every t frames to obtain M frames of sampling images, and performing image processing on the sampling images to obtain block bright spots in each frame of sampling images; wherein t is a positive integer greater than or equal to 1, and t is less than K;
3) screening all block bright spots in each frame of sampling image in the step 2) to obtain a suspected target queue;
4) obtaining the motion characteristics of each block-shaped bright spot in the suspected target queue in the step 3);
5) determining N ordered feature sets according to the motion features of all the block bright spots obtained in the step 4);
6) and (5) determining N ordered feature sets according to the motion features of each block bright point in the suspected target queue in the step 4) and the motion features of each block bright point in the step 5), identifying a space motion target, and finishing the identification work of the dark and weak space target.
The method for obtaining the suspected target queue in the step 3) specifically comprises the following steps:
31) respectively determining the angular distance between two block bright spots in each frame of sampled image
The number of the blocky bright spots in the jth frame sampling image is b, if b (b-1)/2 angular distances exist, i and j are positive integers,
represents the angular distance between the ith block-shaped bright spot and the jth block-shaped bright spot in the k frame of sampling image, and k belongs to [1, M ∈]；
32) According to the angular distance threshold value kappa1And step 31) extracting the angular distance in each frame of sampling image to be greater than or equal to the angular distance threshold value kappa from each frame of sampling image respectively1The angular distance of each frame is used as a primary screening result of each frame of sampling image; wherein, κ1Angular distance error greater than the fixed star;
33) and sequentially and respectively comparing each block bright spot in the primary screening result of each frame of sampling image with each block bright spot in the primary screening result of the previous frame of sampling image, and extracting the block bright spots which do not meet the rejection conditions from the primary screening result of each frame of sampling image according to the rejection conditions to serve as a suspected target queue.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention provides a space target authentication method, which is different from the current domestic open target tracking method, foreign open mask method and relevant matching method, and has less dependence on prior information and hardware;
2) the invention can automatically classify and verify the targets on line, and is suitable for various small space targets;
3) the invention realizes the real-time authentication processing of a plurality of targets, can extract a plurality of targets with various types, unlimited quantity, few redundant steps and high efficiency.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating the effect of star point extraction according to the present invention;
FIG. 3 is a diagram of a suspected target distribution according to the present invention;
FIG. 4 is a graph of the distribution of a moving object according to the present invention;
FIG. 5 is a diagram of the trajectories of 3 moving objects of the present invention;
FIG. 6 is a flow chart of multi-frame satellite picking according to the present invention;
FIG. 7 is a diagram illustrating the effect of the motion classification according to the present invention;
FIG. 8 is a diagram illustrating an effect of the embodiment of the present invention.
Detailed Description
The invention discloses a high-efficiency dark and weak space target identification method, which comprises the following steps as shown in figure 1:
1) imaging a starry sky by using a high-resolution camera to obtain a K frame dark field image, wherein the dark field image comprises a plurality of space moving objects; k is a positive integer;
2) sampling the K frames of dark field images obtained in the step 1), extracting one dark field image as a sampling image every t frames to obtain M frames of sampling images, and performing image processing on the sampling images to obtain block bright spots in each frame of sampling images; t is a positive integer greater than or equal to 1, and t is less than K;
step 2) the total frame number M of the sampling image is specifically as follows:
when the number of K is an odd number,
when K is an even number, the number of bits is,
3) screening all the block bright spots in each frame of the sampling image in the step 2), and removing fixed stars and noise spots in the block bright spots to obtain a suspected target queue, wherein the method specifically comprises the following steps:
31) respectively determining the angular distance between two block bright spots in each frame of sampled image
The number of the blocky bright spots in the jth frame sampling image is b, if b (b-1)/2 angular distances exist, i and j are positive integers,
represents the angular distance between the ith block-shaped bright spot and the jth block-shaped bright spot in the k frame of sampling image, and k belongs to [1, M ∈]；
32) According to the angular distance threshold value kappa1And step 31) extracting the angular distance in each frame of sampling image to be greater than or equal to the angular distance threshold value kappa from each frame of sampling image respectively1The angular distance of each frame is used as a primary screening result of each frame of sampling image; wherein, κ1Angular distance error greater than the fixed star;
33) sequentially and respectively comparing each block bright spot in the primary screening result of each frame of sampling image with each block bright spot in the primary screening result of the previous frame of sampling image, and extracting the block bright spots which do not meet the rejection conditions from the primary screening result of each frame of sampling image according to the rejection conditions to serve as a suspected target queue;
step 33) the removing conditions are as follows: if it is
Or
When it is, then
The ith block-shaped bright spot and the jth block-shaped bright spot in the corresponding k frame of sampling image meet the removing condition, otherwise, the removing condition is not met; wherein the content of the first and second substances,
representing the p block-shaped bright spot and the q block-shaped bright spot in the dark field image of the (k-1) th frame; kappa1Is the angular distance threshold, κ1Angular distance error, κ, greater than star2Is a position threshold,. kappa.2Greater than 0.3 pixels of the detector,
is the euler distance of any two blocky bright spots in the kth frame.
4) Obtaining the motion characteristics of each block-shaped bright spot in the suspected target queue in the step 3) by adopting an authentication method; the motion characteristics of the block bright spots are (u, v) the positions L of the block bright spots, (ra, dec) the declination of the block bright spots or the vector information of the block bright spots
Any one of them.
5) Determining N ordered feature sets according to the motion features of all the block bright spots in the step 4), specifically:
51) selecting the vector information of the block bright spots for representing the motion characteristics of the block bright spots, and obtaining M-1 groups of differential data according to the motion characteristics of the block bright spots belonging to the suspected target queue in the M frames of sampled images;
the step 51) is a method for obtaining the kth group of differential data corresponding to the kth frame of sample image, specifically:
511) randomly selecting one block bright spot belonging to a suspected target queue from the k frame of sampling image, and obtaining the difference result of the motion characteristics of the block bright spot and the motion characteristics of each block bright spot belonging to the suspected target queue in the k-1 frame of sampling image
Wherein the content of the first and second substances,
vector information representing the s-th block-shaped bright spot belonging to the suspected target queue in the k-th frame of sampled image,
vector information of a t-th block-shaped bright spot belonging to a suspected target queue in a k-1-th frame of sampling image is represented;
512) and repeating the step 511) until obtaining the difference results corresponding to all the block-shaped bright spots belonging to the suspected target queue in the k frame of the sampling image, and taking all the obtained difference results as the k group of difference data.
52) Screening out differential results meeting a threshold condition from the M-1 groups of differential data in the step 51), and equally dividing all screened out differential results into N parts according to the size to obtain N ordered feature sets; wherein, the number of the difference results in each ordered feature set is F, and F/M is more than 0.5;
step 52) the threshold condition is specifically:
6) And (5) determining N ordered feature sets according to the motion features of each block bright point in the suspected target queue in the step 4) and the motion features of each block bright point in the step 5), identifying a space motion target, and finishing the identification work of the dark and weak space target.
The method for identifying the space moving target in the step 6) specifically comprises the following steps:
61) randomly selecting one block bright spot belonging to a suspected target queue from two sampling images with adjacent frame numbers to obtain two block bright spots;
62) obtaining a difference result of the two block-shaped bright spots according to the motion characteristics of the two block-shaped bright spots in the step 61);
63) repeating the steps 61) -62) to traverse all the block-shaped bright spots belonging to the suspected target queue in the M frames of sampling images to obtain a plurality of difference results;
64) classifying the plurality of differential results obtained in the step 62), and classifying the differential results belonging to the same ordered feature set into the same group of classified differential groups;
65) extracting block bright spots corresponding to each difference result from the difference results in the same group of classified difference groups to serve as track points of the space moving target; if a plurality of difference results in the same group of classified difference groups belong to the same two frames of sampling images, selecting a block bright spot corresponding to the difference result with the minimum difference result in the two frames of sampling images as a track point of a space moving target; different classification difference groups respectively correspond to different space moving objects.
Example 1:
(1) and shooting a star map to finish star point extraction. Firstly, estimating background gray, then, judging cluster points meeting the condition that the number of image points is more than 4 as bright points through an eight-connected domain algorithm, and finishing star point extraction, wherein the obtained result is shown in figure 2. The background gray level adopts an area background prediction method, and the threshold value calculation formula is as follows:
(2) and (5) removing star points. Removing fixed stars through comparison with a star table, removing influences of elements such as fixed bad pixels, thermal noise points and the like, establishing a suspicious space target queue, and obtaining a suspected target as shown in figure 3;
(3) for a known moving target, a target tracking method is used to judge whether a bright spot in a target queue in a suspicious space is a target, and the obtained moving target is shown in fig. 4. In this example, a right ascension and declination velocity tracking method is adopted, and if the right ascension and declination of the target is X ═ α, δ, the prediction method is:
δ＝μδ(t-t0)+δ0，α＝μα(t-t0)+α0，
(4) and processing a plurality of frames of images continuously, wherein the obtained motion trail is shown in figure 5.
(5) And (5) calculating parameters. Calculating right ascension and declination of right ascension by least square methodSpeed of change:
solving to obtain:
respectively changing x into alpha and delta to obtain delta0,μδ,α0,μαThe value is obtained.
Example 2:
the embodiment 2 is the same as the embodiment 1, the different step (2) is the multi-frame removing method of the invention, and the step (3) is that the tracking method adopts an image plane coordinate prediction method. According to the stable characteristic of stars in the inertial system, all useless background stars are removed through interframe comparison, and the implementation method is shown in fig. 6. More background stars can be removed than with the previous method. The tracking method adopts an image plane coordinate prediction method, and if the position coordinate of the target is X ═ u, v, the prediction method is as follows:
u＝μu(t-t0)+u0，v＝μv(t-t0)+v0，
therefore, the position of the target in the image frame is obtained, and the target is extracted, so that the position of the target is extracted more directly and accurately compared with the method.
Example 3:
1) calculating the movement rate of the blocky bright spots in the target suspected queue of each frame in the inertial space, eliminating all matched star pairs with the rate larger than the upper threshold limit (0.2 degrees), and obtaining all movement rate information after carrying out iterative processing on multi-frame information;
2) all the motion characteristics are counted, the motion ordered characteristic set is calculated according to the frequency, and the motion characteristic set with the minimum comprehensive speed is selected as a final class A ordered characteristic set, and the graph of the motion characteristic set is shown in FIG. 7.
3) And re-counting the suspected targets located in the A-class ordered feature set, performing interframe one-to-one association on target serial numbers falling into the feature set, fitting motion parameters after the association is finished, and when the fitting error is smaller than a certain value, confirming that the serial numbers belong to the same target and giving new numbers.
4) And continuing to perform interframe association on other targets falling into the feature set according to the step 3), calculating parameters, confirming as new targets if the parameters are different from those in the confirmed target queue, and giving new numbers to the targets to indicate that the targets are new space moving targets.
Compared with the tracking method of embodiment 2, 1 more space moving object is determined, as shown in fig. 8, where there are 4 space moving objects.
Those skilled in the art will appreciate that the details of the invention not described in detail in the specification are within the skill of those skilled in the art.
Claims (8)
1. A high-efficiency dark and weak space target identification method is characterized by comprising the following steps:
1) imaging a starry sky by using a high-resolution camera to obtain a K frame dark field image, wherein the dark field image comprises a plurality of space moving objects; k is a positive integer;
2) sampling the K frames of dark field images obtained in the step 1), extracting one dark field image as a sampling image every t frames to obtain M frames of sampling images, and performing image processing on the sampling images to obtain block bright spots in each frame of sampling images; wherein t is a positive integer greater than or equal to 1, and t is less than K;
3) screening all block bright spots in each frame of sampling image in the step 2) to obtain a suspected target queue;
4) obtaining the motion characteristics of each block-shaped bright spot in the suspected target queue in the step 3);
5) determining N ordered feature sets according to the motion features of all the block bright spots obtained in the step 4);
6) determining N ordered feature sets according to the motion features of each block-shaped bright point in the suspected target queue in the step 4) and the motion features of each block-shaped bright point in the step 5), identifying a space motion target, and completing the identification work of the dark and weak space target;
the method for obtaining the suspected target queue in the step 3) specifically comprises the following steps:
31) respectively determining the angular distance between two block bright spots in each frame of sampled image
The number of the blocky bright spots in the jth frame sampling image is b, then b (b-1)/2 angular distances exist, i and j are positive integers,
represents the angular distance between the ith block-shaped bright spot and the jth block-shaped bright spot in the k frame of sampling image, and k belongs to [1, M ∈]；
32) According to the angular distance threshold value kappa1And step 31) extracting the angular distance in each frame of sampling image to be greater than or equal to the angular distance threshold value kappa from each frame of sampling image respectively1The angular distance of each frame is used as a primary screening result of each frame of sampling image; wherein, κ1Angular distance error greater than the fixed star;
33) and sequentially and respectively comparing each block bright spot in the primary screening result of each frame of sampling image with each block bright spot in the primary screening result of the previous frame of sampling image, and extracting the block bright spots which do not meet the rejection conditions from the primary screening result of each frame of sampling image according to the rejection conditions to serve as a suspected target queue.
2. The method for identifying the target in the dark and weak space with high efficiency according to claim 1, wherein the removing conditions in step 33) are as follows: if it is
When it is, then
The ith block-shaped bright spot in the corresponding k frame sampling imageThe jth block-shaped bright spot meets the rejection condition, otherwise, the rejection condition is not met; wherein the content of the first and second substances,
showing the p block-shaped bright spot and the q block-shaped bright spot in the dark field image of the (k-1) th frame.
3. The method as claimed in claim 2, wherein the moving characteristic of the block bright point in step 4) is any one of the position of the block bright point, the declination of the block bright point in the right ascension or the vector information of the block bright point.
4. The method for identifying the target in the dark and weak space with high efficiency according to claim 2, wherein the step 5) is a method for determining N ordered feature sets, and specifically comprises the following steps:
51) selecting the vector information of the block bright spots for representing the motion characteristics of the block bright spots, and obtaining M-1 groups of differential data according to the motion characteristics of the block bright spots belonging to the suspected target queue in the M frames of sampled images;
52) screening out differential results meeting a threshold condition from the M-1 groups of differential data in the step 51), and equally dividing all screened out differential results into N parts according to the size to obtain N ordered feature sets; wherein, the number of the difference results in each ordered feature set is F, and F/M is more than 0.5.
5. The method for identifying the target in the dark and weak space with high efficiency according to claim 4, wherein the step 51) is a method for obtaining the kth group of differential data corresponding to the kth frame of the sampled image, and specifically comprises:
511) randomly selecting one block bright spot belonging to a suspected target queue from the k frame of sampling image, and obtaining the difference result of the motion characteristics of the block bright spot and the motion characteristics of each block bright spot belonging to the suspected target queue in the k-1 frame of sampling image
Wherein the content of the first and second substances,
vector information representing the s-th block-shaped bright spot belonging to the suspected target queue in the k-th frame of sampled image,
vector information of a t-th block-shaped bright spot belonging to a suspected target queue in a k-1-th frame of sampling image is represented;
512) and repeating the step 511) until obtaining the difference results corresponding to all the block-shaped bright spots belonging to the suspected target queue in the k frame of the sampling image, and taking all the obtained difference results as the k group of difference data.
6. The method for identifying the target in the dark and weak space with high efficiency according to claim 5, wherein the threshold condition in step 52) is specifically: difference result
7. The method for identifying the dark and weak space target with high efficiency according to any one of claims 3 or 6, wherein the step 6) is a method for identifying a space moving target, and specifically comprises the following steps:
61) randomly selecting one block bright spot belonging to a suspected target queue from two sampling images with adjacent frame numbers to obtain two block bright spots;
62) obtaining a difference result of the two block-shaped bright spots according to the motion characteristics of the two block-shaped bright spots in the step 61);
63) repeating the steps 61) -62) to traverse all the block-shaped bright spots belonging to the suspected target queue in the M frames of sampling images to obtain a plurality of difference results;
64) classifying the plurality of differential results obtained in the step 62), and classifying the differential results belonging to the same ordered feature set into the same group of classified differential groups;
65) extracting block bright spots corresponding to each difference result from the difference results in the same group of classified difference groups to serve as track points of the space moving target; if a plurality of difference results in the same group of classified difference groups belong to the same two frames of sampling images, selecting a block bright spot corresponding to the difference result with the minimum difference result in the two frames of sampling images as a track point of a space moving target; different classification difference groups respectively correspond to different space moving objects.
8. The method for identifying the target in the dark and weak space with high efficiency according to claim 7, wherein the total frame number M of the sampling image in the step 2) is specifically:
when the number of K is an odd number,
when K is an even number, the number of bits is,
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