CN-121482510-B - Inhibition method, equipment and medium for observing fixed star false alarm aiming at foundation telescope

CN121482510BCN 121482510 BCN121482510 BCN 121482510BCN-121482510-B

Abstract

The embodiment of the application provides a suppression method, equipment and medium for observing star false alarms aiming at a foundation telescope, analyzes a constructed false alarm data set, determines characteristic coefficient data such as a star speed approaching coefficient, a star speed direction approaching coefficient, a star eccentricity approaching coefficient, a star phase angle approaching coefficient, a frame breaking punishment coefficient and the like corresponding to a false alarm result, and combines a PSO-SVM recognition algorithm to recognize the star false alarms in a detection result, thereby solving the problem that the star and the space targets cannot be distinguished by adopting the same threshold under different scenes in star dense scenes, further causing the false alarms to be generated, improving the detection accuracy of the star false alarms, and realizing effective suppression of the star false alarms in the false alarm data.

Inventors

WANG XIA
HUANG CHEN
ZHU ZHAOKUN
WANG FANG
HAN XIAOLEI

Assignees

中国人民解放军63921部队

Dates

Publication Date: 20260508
Application Date: 20260108

Claims (10)

1. A method for suppressing star false alarms observed by a ground telescope, the method comprising: Constructing a false alarm data set; Detecting shape parameter data, track data and broken frame data corresponding to the track data in the false alarm data set; determining a star speed magnitude approach coefficient and a star speed direction approach coefficient of the false alarm data set according to the track data; determining a star eccentricity approaching coefficient and a star phase angle approaching coefficient of the false alarm data set according to the shape parameter data; determining a broken frame penalty coefficient of the false alarm data set according to the broken frame data; Identifying the false alarm data set by adopting a support vector machine algorithm based on particle swarm optimization according to the star speed approaching coefficient, the star speed direction approaching coefficient, the star eccentricity approaching coefficient, the star phase angle approaching coefficient and the broken frame punishment coefficient to obtain a star false alarm; and suppressing the star false alarm.
2. The method of claim 1, wherein the trajectory data comprises a set of trajectory points including a start point and an end point, wherein determining a star speed magnitude approach coefficient and a star speed direction approach coefficient of the false alarm data set from the trajectory data comprises: acquiring a fixed star movement speed and a frame number difference between the initial end point and the final end point; determining a straight-line track corresponding to the track point set; calculating a first foot drop of the initial point on the linear track and calculating a second foot drop of the terminal point on the linear track; calculating the average speed and the track speed of the track according to the initial point, the final point, the linear track, the frame number difference, the first foot drop and the second foot drop; and determining a star speed magnitude approach coefficient and a star speed direction approach coefficient of the false alarm data set according to the star movement speed, the track average speed and the track speed magnitude.
3. The method of claim 2, wherein said determining the star speed magnitude and star speed direction approach coefficients of the false alarm data set from the star movement speed, the track average speed, and the track speed magnitude comprises: Calculating the star speed approaching coefficient according to the star movement speed and the track speed; calculating the star speed direction approaching coefficient according to the star movement speed and the track average speed; The star speed magnitude approach coefficient is calculated by the following formula: Wherein, the For the star speed magnitude approach factor, For the magnitude of the track velocity in question, A speed of movement for the star; the star speed direction approach coefficient is calculated by the following formula: Wherein, the For the star velocity direction approach factor, For the average speed of the track, For the star movement speed, the Representation of And The angle between the two vectors.
4. The method of claim 1, wherein said determining the star eccentricity approach factor of the false alarm data set from the shape parameter data comprises: determining the star eccentricity according to the shape parameter; determining a track point eccentricity set corresponding to the track point set; calculating the star eccentricity approaching coefficient according to the star eccentricity and the track point eccentricity set; the star eccentricity approach coefficient is calculated by the following formula: Wherein, the For the star eccentricity approach factor, K is the track point currently traversed for the track point set, For the eccentricity of the star of said sun, For the eccentricity of the kth track point in the set of track point eccentricities, () And the number of the track points in the track point set is the number of the track points.
5. The method of claim 1, wherein said determining the star phase angle approach coefficients of the false alarm data set from the shape parameter data comprises: determining a star phase angle according to the shape parameter; Determining a track point phase angle set corresponding to the track point set; Calculating the star phase angle approach coefficient according to the star phase angle and the track point phase angle set; the star phase angle approach coefficient is calculated by the following formula: Wherein, the For the star phase angle approach factor, K is the track point currently traversed for the track point set, For the phase angle of the star, For the phase angle of the kth track point in the track point phase angle set, () And the number of the track points in the track point set is the number of the track points.
6. The method of claim 1, wherein said determining the break frame penalty factor for the false alarm data set from the break frame data comprises: Acquiring the total frame number in a detection queue of the track data; calculating the broken frame penalty coefficient according to the total frame number in the detection queue of the track data and the track point number in the track point set; the broken frame penalty coefficient is calculated by the following formula: Wherein, the For the broken frame penalty factor, For the total number of frames in the detection queue of the trajectory data, And the number of the track points in the track point set is the number of the track points.
7. The method of claim 1, wherein prior to said identifying the false alarm data set using a support vector machine algorithm based on particle swarm optimization based on the star speed magnitude approach factor, the star speed direction approach factor, the star eccentricity approach factor, the star phase angle approach factor, and the frame break penalty factor, the method comprises: Acquiring a support vector machine algorithm to be trained, particle swarm optimization parameters and an fitness function, wherein a kernel function of the support vector machine algorithm to be trained is a Gaussian kernel function, the support vector machine algorithm to be trained has a corresponding penalty factor, and the Gaussian kernel function has a corresponding kernel scale; according to the particle swarm optimization parameters and the fitness function, optimizing the penalty factors and the kernel scale of the support vector machine algorithm to be trained by adopting a particle swarm optimization method to obtain target penalty factors and target kernel scale; And using the support vector machine algorithm corresponding to the target penalty factor and the target kernel scale as the support vector machine algorithm based on particle swarm optimization.
8. The method of claim 7, wherein the identifying the false alarm data set using a preset identification algorithm based on the star speed magnitude approach factor, the star speed direction approach factor, the star eccentricity approach factor, the star phase angle approach factor, and the frame outage penalty factor to obtain a star false alarm comprises: The star speed magnitude approach coefficient, the star speed direction approach coefficient, the star eccentricity approach coefficient, the star phase angle approach coefficient and the frame breaking penalty coefficient corresponding to the false alarm data set are used as characteristic coefficient data of the false alarm data set; Inputting the characteristic coefficient data into the support vector machine algorithm based on particle swarm optimization to obtain a classification result of the support vector machine algorithm based on particle swarm optimization for the false alarm data set, wherein the classification result comprises the star false alarm.
9. An electronic device is characterized by comprising a processor; and A memory having executable code stored thereon that, when executed, causes the processor to perform the suppression method of observing star false alarms for a ground-based telescope as recited in any one of claims 1-8.
10. A machine readable medium having stored thereon executable code which when executed causes a processor to perform the suppression method of observing star false alarms for a ground based telescope as recited in any one of claims 1-8.

Description

Inhibition method, equipment and medium for observing fixed star false alarm aiming at foundation telescope Technical Field The application relates to the technical field of aerospace, in particular to a suppression method and device for observing star false alarms aiming at a foundation telescope, electronic equipment and a storage medium. Background False alarms are a problem that cannot be ignored in space target detection based on ground telescope observation. The improper detection algorithm can lead to a large number of false alarms, so that unnecessary consumption is brought to an early warning system, a security system and related manpower and material resources, and the real-time performance of subsequent processing is greatly influenced. In practical application, the conventional research thinking is to eliminate images with poor imaging quality (excessive noise, heavy stray light, cloud cover and the like) to avoid false alarms to a certain extent. However, in order to discover more dark and weak targets, methods of increasing the exposure time of a camera and reducing the threshold value of the detection signal to noise ratio are generally adopted in preprocessing an image, so that the constant star images are densely distributed, a plurality of false time sequence motion tracks are generated, and are easily confused with space targets, and the detection effect is affected. Disclosure of Invention The embodiment of the application provides a suppression method for observing fixed star false alarms aiming at a foundation telescope, which aims to solve the problem that fixed star false alarms are generated because fixed star and space targets cannot be distinguished by adopting the same threshold under different scenes in space target detection. Correspondingly, the embodiment of the application also provides a suppression device for observing the star false alarm aiming at the foundation telescope, an electronic device and a storage medium, which are used for ensuring the realization and the application of the method. In order to solve the problems, the embodiment of the application discloses a suppression method for observing star false alarms aiming at a foundation telescope, which comprises the following steps: Constructing a false alarm data set; Detecting shape parameter data, track data and broken frame data corresponding to the track data in the false alarm data set; determining a star speed magnitude approach coefficient and a star speed direction approach coefficient of the false alarm data set according to the track data; determining a star eccentricity approaching coefficient and a star phase angle approaching coefficient of the false alarm data set according to the shape parameter data; determining a broken frame penalty coefficient of the false alarm data set according to the broken frame data; Identifying the false alarm data set by adopting a support vector machine algorithm based on particle swarm optimization according to the star speed approaching coefficient, the star speed direction approaching coefficient, the star eccentricity approaching coefficient, the star phase angle approaching coefficient and the broken frame punishment coefficient to obtain a star false alarm; and suppressing the star false alarm. The embodiment of the application also discloses electronic equipment which comprises a processor and a memory, wherein executable codes are stored on the memory, and when the executable codes are executed, the processor is caused to execute the suppression method for observing the star false alarm aiming at the foundation telescope according to one or more of the embodiments of the application. The embodiment of the application also discloses a machine-readable medium, wherein executable codes are stored on the machine-readable medium, and when the executable codes are executed, a processor executes the suppression method for observing the star false alarm aiming at the foundation telescope. Compared with the prior art, the embodiment of the application has the following advantages: In the embodiment of the application, the star speed approaching coefficient, the star speed direction approaching coefficient, the star eccentricity approaching coefficient, the star phase angle approaching coefficient, the broken frame punishment coefficient and other characteristic coefficient data corresponding to the false alarm result are determined by analyzing the constructed false alarm data set, the star false alarm in the detection result is identified by combining a PSO-SVM identification algorithm, the problem that the star cannot be distinguished from the space target by adopting the same threshold value in different scenes in the star dense scene is solved, the false alarm is caused, the detection accuracy of the star false alarm is improved, and the star false alarm in the false alarm data is effectively restrained. Drawings FIG. 1 is a flow chart of steps of an embodiment of a