CN108919268B - Track initiation algorithm based on unmanned aerial vehicle monitoring radar - Google Patents
Track initiation algorithm based on unmanned aerial vehicle monitoring radar Download PDFInfo
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Abstract
The invention discloses a track initiation algorithm based on an unmanned aerial vehicle monitoring radar, which comprises the following steps: the system carries out unilateral multi-hypothesis correlation judgment on the point tracks scanned by the radar to form unilateral multi-hypothesis temporary tracks; performing multi-model temporary track starting judgment on the single-side multi-hypothesis temporary track to form a temporary track meeting track starting conditions; and performing track filtering judgment on the temporary track meeting the track starting condition to form a stable track. The invention solves the problems of difficult track initiation and serious false alarm in clutter areas of the unmanned aerial vehicle and improves the monitoring efficiency of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the field of low-altitude monitoring of three-coordinate active phased array radars, in particular to a track initiation algorithm based on an unmanned aerial vehicle monitoring radar.
Background
In recent years, with the rapid development of the unmanned aerial vehicle technology, the unmanned aerial vehicle has been expanded from the original military market to the civil market in a large scale, and safety accidents caused by black flight are rare, so that the control in the low-altitude field of China bears great threat. Under the urgent needs of national security defense departments such as frontier defense, civil defense, public security and the like, the unmanned aerial vehicle monitoring radar gradually rises.
Because civil unmanned aerial vehicle adopts non-metallic fuselage and fuselage is small usually, lead to RCS reflection sectional area little, radar detection echo weak, especially flight speed is slow, flight height is low, the nimble characteristics of flight, let unmanned aerial vehicle's detection and tracking become the difficult problem of industry recognition, wherein, because priori information is not enough, the target mobility is big, the target detection discontinuous characteristics, the initial difficulty of unmanned aerial vehicle flight path that leads to, clutter district false alarm seriously has become the difficult problem that unmanned aerial vehicle monitored.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a track initiation algorithm based on an unmanned aerial vehicle monitoring radar, which solves the problems of difficult track initiation and serious clutter area false alarm of the unmanned aerial vehicle and improves the monitoring efficiency of the unmanned aerial vehicle.
In order to achieve the purpose, the invention adopts the following technical scheme that:
a track starting algorithm based on an unmanned aerial vehicle monitoring radar is characterized by comprising the following steps:
s1, the system receives first frame trace point data scanned by the radar, and temporary tracks are established for all trace points in the first frame trace point data; the trace point data includes: three-dimensional coordinate information, time information, attribute values and amplitude values of the trace points;
s2, the system receives the next frame of point trace data scanned by the radar, and makes unilateral multi-hypothesis correlation judgment on each point trace in the next frame of point trace data and each established temporary track, judges whether the next frame of point trace is related to the established temporary track, if a certain point trace in the next frame of point trace is related to the established temporary track, the related point trace is related to the established temporary track to form a unilateral multi-hypothesis temporary track; if a certain point track in the next frame point track is not related to each established temporary track, establishing the temporary track according to the point track;
s3, performing multi-model temporary track starting judgment on the single-side multi-hypothesis temporary track, judging whether the single-side multi-hypothesis temporary track meets track starting conditions, cascading the single-side multi-hypothesis temporary track with a plurality of temporary track starting models, judging whether the single-side multi-hypothesis temporary track meets the starting conditions of one of the temporary track starting models, and if so, judging that the single-side multi-hypothesis temporary track is the temporary track meeting the track starting conditions; if the one-side multi-hypothesis temporary track does not meet the track starting condition, the one-side multi-hypothesis temporary track waits for carrying out one-side multi-hypothesis correlation judgment on the next frame of point track data, and if the one-side multi-hypothesis temporary track does not have correlation point tracks within a certain continuous frame number, the one-side multi-hypothesis temporary track is deleted;
s4, performing track filtering judgment on the temporary track meeting the track starting condition, judging whether the temporary track meeting the track starting condition passes the track filtering processing or not, and if the temporary track meeting the track starting condition passes the track filtering processing, forming a stable track by the temporary track meeting the track starting condition; if the temporary flight path meeting the flight path starting condition is not subjected to the flight path filtering processing, deleting the temporary flight path meeting the flight path starting condition;
in steps S1 and S2, the track point for establishing the temporary track is the track head.
In step S2, the single-side multi-hypothesis correlation determination employs a comprehensive membership degree correlation algorithm, which specifically includes: and calculating the comprehensive membership degree of the trace point and each established temporary track according to the membership degree of each established temporary track and the feature factor of the trace point and the membership degree of each established temporary track and the motion state factor of the trace point, wherein the comprehensive membership degree meets a threshold set by a user and is related to the threshold.
If the established temporary track is relevant to a plurality of tracks in the next frame of track points, performing optimal relevant track point selection on the temporary track, and only reserving one optimal relevant track point in the established temporary track to form a single-side multi-hypothesis temporary track, wherein the optimal relevant track point selection comprises the following steps: and selecting the point track with the highest comprehensive membership degree as the optimal related point track of the temporary track according to the established comprehensive membership degree of the temporary track and each point track, and reserving the optimal related point track.
In step S3, the multi-model temporary track start determination includes the following specific steps:
s31, calculating the comprehensive membership degree of each frame point track forming the one-sided multi-hypothesis temporary track and the one-sided multi-hypothesis temporary track, and counting the frame number (HighSubCnt) of which the comprehensive membership degree belongs to high membership degree and the frame number (VeryHighhSubCnt) of which the comprehensive membership degree belongs to very high membership degree;
s32, calculating the course consistency of each frame point track forming the single-side multi-hypothesis temporary track and the single-side multi-hypothesis temporary track, and counting the frame numbers HighCourseUniformMcnt with consistent course and VeryHighCourseUniformMcnt with consistent course height;
s33, counting the clutter region frame number CluterCnt of each frame point track forming the unilateral multi-hypothesis temporary track;
s34, judging whether the temporary track of the single-side multi-hypothesis belongs to the clutter region or not according to the clutter region frame number CluterCnt, if not, carrying out the initial judgment of the temporary track initial model 1 on the temporary track of the single-side multi-hypothesis, and executing the step S35; if yes, performing initial judgment of the temporary track initial model 2 on the temporary track of the single-side multi-hypothesis, and executing step S36;
s35, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 1, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, performing initial judgment of the temporary track initial model 3 on the temporary track with the single-side multiple hypotheses, and executing step S38;
s36, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 2, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, go to step S37;
s37, judging whether the temporary track of the single-side multi-hypothesis belongs to a similar cleaning area, if so, performing initial judgment of a temporary track initial model 3 on the temporary track of the single-side multi-hypothesis, and executing the step S38; if not, the single-side multi-hypothesis temporary track does not meet the starting condition of the temporary track; the similar cleaning zone: the ratio of the frame number CluterCnt of the temporary track belonging to the clutter zone to the frame number FrameCnt of the temporary track is smaller than the threshold value CleanFrameThresold of the frame number of the clutter zone;
s38, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 3, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, the single-side multi-hypothesis temporary track does not meet the starting condition of the temporary track;
wherein, the track starting condition of the temporary track starting model 1 is as follows:
the HighSubThresold is the threshold of the high membership frame number;
HighCourseUniformmThreshold is a heading consistent frame number threshold;
track start conditions of the temporary track start model 2:
VeryHighSubThresold is the threshold of the frame number with very high membership;
VeryHighCourseUniformmThreshold is a heading height consistency frame number threshold;
track start conditions of the temporary track start model 3:
framecnt is the number of frames forming the temporary track of the single-side multi-hypothesis;
the CleanFrameThresold is the clutter zone frame number threshold;
InitFrameCnt is the initial frame number of the temporary track of the single-side multi-hypothesis;
InitFrameThresold is the starting threshold;
LostCnt is the number of lost point frames;
framecnt is the temporary track forming frame number of the single-side multi-hypothesis;
sub is the comprehensive membership;
SubThresold is the correlation threshold.
In step S31, if the comprehensive membership degree is greater than or equal to 0.8 and less than 0.85, it is a high membership degree; if the comprehensive membership degree is more than or equal to 0.85 and less than 1, the comprehensive membership degree belongs to a very high membership degree.
In step S32, if the heading consistency is greater than 0.5 degrees and less than or equal to 1.5 degrees, it is heading consistency; and if the course consistency is less than or equal to 0.5 degree and greater than 0 degree, the course is consistent in height.
In step S4, the track filtering decision performs a first filtering, where the first filtering: calculating the ratio of the distance between the head and tail points of the temporary track meeting the track starting condition and the range of the temporary track meeting the track starting condition according to the trace point data of each frame scanned by the radar, judging whether the ratio is greater than a clutter shaking threshold, and if so, filtering by a first stage; if not, deleting the temporary track meeting the track starting condition.
And performing second-stage filtering on the temporary track meeting the track starting condition through the first-stage filtering, wherein the second-stage filtering comprises the following steps: judging whether continuous point tracks in a forbidden initiation area in the temporary tracks meeting the track initiation conditions reach a set threshold value by adopting a sliding window method, and if so, deleting the temporary tracks meeting the track initiation conditions; if not, forming a stable track through second-stage filtering, namely through track filtering judgment; the starting forbidden area is an area which is set by a user and is not allowed to establish a temporary track.
The invention has the advantages that:
(1) the method adopts unilateral multi-hypothesis correlation judgment, solves the problem that the point track cannot be correlated with the established temporary track due to high target mobility and clutter interference, avoids explosive increase of the number of the established temporary tracks, and saves computer resources.
(2) The invention adopts multi-model flight path initial judgment, and the temporary flight path initial model is expanded according to the flight state and the clutter environment of the target, thereby ensuring that the unmanned aerial vehicle can be quickly started in different environments and different flight states.
(3) The 3 temporary track starting models are respectively suitable for the temporary tracks belonging to clutter areas, non-clutter areas and similar clean areas in the clutter areas, so that the target starting efficiency is ensured, and meanwhile, the false alarm can be effectively controlled.
(4) The invention adopts the track filtering judgment, eliminates the interference of clutter on the temporary track and reduces the false alarm rate.
(5) According to the two-stage track filtering processing, the first stage of filtering is used for suppressing clutter interference, the second stage of filtering is used for providing site optimization for the user, and the method is suitable for different user groups and use scenes, and improves user experience.
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FIG. 1 is an algorithm flow diagram of the track initiation algorithm of the present invention.
FIG. 2 is a flowchart of a single-sided multi-hypothesis correlation determination method of the present invention.
FIG. 3 is a flowchart of a method for multi-model temporary track initiation determination according to the present invention.
FIG. 4 is a flow chart of a method for temporal track filtering determination according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a track initiation algorithm based on an unmanned aerial vehicle surveillance radar includes the following steps:
s1, the radar scans the first frame, the system receives the first frame trace point data, the trace point data includes: and establishing temporary tracks for all the trace points in the first frame of trace point data.
S2, scanning a second frame by the radar, receiving the frame of point track data by the system, carrying out unilateral multi-hypothesis correlation judgment on each point track in the frame of point track data and each established temporary track, judging whether the point track in the frame of point track is related to the established temporary track, and if a certain point track is related to the established temporary track, associating the related point track to the established temporary track to form a unilateral multi-hypothesis temporary track; if a certain point track is not related to each established temporary track, the point track is used for establishing the temporary track.
S3, performing multi-model temporary track starting judgment on the single-side multi-hypothesis temporary track, judging whether the single-side multi-hypothesis temporary track meets track starting conditions, cascading the single-side multi-hypothesis temporary track with 3 temporary track starting models, judging whether the single-side multi-hypothesis temporary track meets the starting conditions of one of the temporary track starting models, and if so, judging that the single-side multi-hypothesis temporary track is the temporary track meeting the track starting conditions; if the one-side multi-hypothesis temporary track does not meet the track starting condition, the one-side multi-hypothesis temporary track is used as an established temporary track to wait to perform one-side multi-hypothesis correlation judgment with the third frame of point track data, the analogy is repeated, the one-side multi-hypothesis temporary track not meeting the track starting condition waits to perform one-side multi-hypothesis correlation judgment with the next frame of point track data, and if the one-side multi-hypothesis temporary track does not have related point tracks in three continuous frames, the one-side multi-hypothesis temporary track is deleted.
S4, performing track filtering judgment on the temporary track meeting the track starting condition, eliminating the interference of clutter on the temporary track starting, wherein the track filtering is divided into two-stage filtering, the first stage filtering is clutter shaking points filtering, the second stage filtering is temporary track filtering in a forbidden starting area, judging whether the temporary track meeting the track starting condition passes through the two-stage filtering of the track filtering, and if the temporary track meeting the track starting condition passes through the two-stage filtering, forming a stable track; and if the temporary track meeting the track starting condition is not filtered by two stages, deleting the temporary track meeting the track starting condition.
Wherein, the track point of the temporary track established in steps S1 and S2 is the track head.
In step S2, as shown in fig. 2, the method for determining the one-sided multi-hypothesis temporary track correlation includes the following steps:
s21, calculating the membership SubAttribute of the established temporary track and trace characteristic factors, as shown in formula 1, wherein plotAttributeiA trace point attribute value, TTAttribute, which is a trace point characteristic factor iiFor temporary track attribute values, normalizedAttributeiFor normalization, the maximum fluctuation range of the characteristic Factor, is usually takeniThe maximum value is 1 for the contribution of the trace feature factor i to the trace feature membership degree, and the membership degree of the trace feature factor is the comprehensive membership degree of the computed n trace feature factors.
S22, calculating the membership degree SubDynamic of the established temporary track and point track motion state factors, as shown in formula 2, wherein plotDynamiciIs the motion state value of the trace point motion state factor i, TTDynamiciFor temporary track motion state values, normalized dynamiciFor normalization, the maximum fluctuation range, Factor, of the motion state Factor is usually takeniThe maximum value of the contribution of the point trace motion state factor i to the point trace motion state membership degree is 1, and the membership degree of the point trace motion state is the comprehensive membership degree of the calculated m point trace motion states.
And S23, calculating a comprehensive membership degree Sub according to the established membership degree SubAttribute of the temporary track and the trace characteristic factor and the established membership degree SubDynamic of the temporary track and the trace motion state factor, wherein k is a membership degree ratio of the trace characteristic factor, the maximum value is 1, and the contribution ratio of the trace characteristic factor and the trace motion state factor to the trace membership degree is measured.
Sub ═ SubAttribute × k + SubDynamic x (1-k); equation 3
S24, judging whether the comprehensive membership degree Sub is larger than a threshold set by a user, in the embodiment, setting the threshold as 0.6, if so, the temporary flight path is related to the point path, and executing a step S25; if not, step S26 is executed.
S25, performing optimal relevant point track selection on all relevant point tracks of the established temporary track, and finally only reserving one optimal relevant point track to form a single-side multi-hypothesis temporary track; selecting all optimal related points: and comparing the established temporary track with the comprehensive membership degree of each track, wherein the track with the highest comprehensive membership degree is the optimal related track of the temporary track, and reserving the optimal related track.
And S26, associating only one finally reserved relevant point track to the established temporary track to form a unilateral multi-hypothesis temporary track.
The established temporary track is only related to one point track at most in each frame of point track data, and a single-side multi-hypothesis temporary track is formed.
In step S3, as shown in fig. 3, the method for determining the starting position of the multi-model temporary flight path includes the following steps:
s31, calculating the comprehensive membership degree of the point track forming the temporary track of the single-side multi-hypothesis and the temporary track of the single-side multi-hypothesis, and counting the number of frames (HighSubCnt) with high membership degree and the number of frames (VeryHighSubCnt) with very high membership degree;
the comprehensive membership degree is the comprehensive membership degree Sub calculated in step S23, and in this embodiment, the comprehensive membership degree is greater than or equal to 0.8 and less than 0.85, which is a high membership degree; the comprehensive degree of membership is greater than or equal to 0.85 and less than 1, which means a very high degree of membership.
S32, calculating the course consistency of the point track forming the single-side multi-hypothesis temporary track and the single-side multi-hypothesis temporary track, and counting the frame numbers HighCourseUniformMcnt with consistent course and VeryHighCourseUniformMcnt with consistent course height;
the course consistency is the difference between the point track of the temporary track forming the single-side multi-hypothesis and the course of the temporary track forming the single-side multi-hypothesis, and in the embodiment, the course consistency is less than or equal to 1.5 degrees and more than 0.5 degrees, and the courses are consistent; and if the course consistency is less than or equal to 0.5 degrees and greater than 0 degree, the course height is consistent.
S33, counting the clutter region frame number CluterCnt of the point track forming the unilateral multi-hypothesis temporary track, judging the clutter region of the temporary track, and executing the step S34 if the temporary track belongs to the non-clutter region, namely the clutter region frame number CluterCnt is 0; if the frame belongs to the clutter zone, i.e. the clutter zone frame number CluterCnt is greater than 0, step S35 is executed.
S34, judging whether the initial condition of the temporary track initial model 1 is satisfied, if so, executing the step S38; if not, go to step S37; the starting conditions are as follows: the frame number of high membership HighSubCNT, the frame number of consistent course HighCourseUniformmCNT meet the high membership frame number threshold HighSubThresold, the frame number of consistent course HighCourseUniformmThreshold, as shown in the relational expression 1.
S35, judging whether the initial condition of the temporary track initial model 2 is satisfied, if so, executing the step S38; if not, go to step S36; the starting conditions are as follows: the frame number of very high membership VeryHighhSubCNT, the frame number of course highly consistent VeryHighCourseUniformmCNT satisfy a very high membership frame number threshold VeryHighhSubThresold, a course highly consistent frame number threshold VeryHighCourseUniformThresold, as shown in relational expression 2.
S36, judging whether the image belongs to the similar cleaning area, if yes, executing the step S37; if not, go to step S39; the similar cleaning zone: the ratio of the frame number CluterCnt of the temporary track belonging to the clutter zone of the single-side multi-hypothesis to the temporary track forming frame number FrameCnt of the single-side multi-hypothesis is smaller than the clutter zone frame number threshold value CleanFrameThresold.
S37, judging whether the initial condition of the temporary track initial model 3 is satisfied, if so, executing the step S38; if not, go to step S39; the starting conditions are as follows: the target flight trajectory is discontinuous, that is, the number of lost frames is LostCnt, the initial frame number InitFrameCnt dynamically increases the number of lost frames on the basis of the initial threshold initframethreshold, and it is determined whether the temporary trajectory forming frame number FrameCnt of the single-side multi-hypothesis is greater than the initial frame number, and whether the comprehensive membership degree Sub is greater than the membership degree threshold value subthreshold, as shown in relational expression 3.
And S38, the temporary track of the single-side multi-hypothesis is the temporary track meeting the track starting condition.
S39, using the temporary track of single-side multiple hypothesis as the established temporary track to wait for the next frame of trace point data to make single-side multiple hypothesis correlation judgment, and if the temporary track of single-side multiple hypothesis has no correlation trace point in three continuous frames, deleting the temporary track of single-side multiple hypothesis.
In this embodiment, the multi-model temporary track start determination is performed on the single-side multi-hypothesis temporary track formed in step S2.
The temporary track starting model for multi-model temporary track starting judgment is extended according to the flight state and clutter environment of the target, in the embodiment, the multi-model temporary track starting judgment comprises 3 temporary track starting models, wherein the temporary track starting model 1: the method is suitable for temporary flight tracks belonging to non-clutter areas; temporary track initiation model 2: the method is suitable for temporary flight tracks belonging to clutter areas; temporary track initiation model 3: the method is suitable for temporary tracks belonging to non-clutter areas which do not meet the track starting model 1, and is also suitable for temporary tracks belonging to similar clean areas in clutter areas.
In this embodiment, the high membership frame number threshold highSubThresold is 3 frames;
the very high membership frame number threshold VeryHighSubThresold is 4 frames;
the heading uniform frame number threshold HighCourseUniformmThreshold is 3 frames;
the heading height consistency frame number threshold VeryHighCourseUniformmThreshold is 4 frames;
the clutter zone frame number threshold clearmembrane threshold is 3/8;
the membership threshold SubThresold is 0.8.
In step S4, as shown in fig. 3, the method for temporary track filtering determination includes the following steps:
s41, recording initial point position information FirstX and FirstY when the temporary track meeting the track starting condition is created; recording the journey information TotalRoute of each frame of the temporary track meeting the track starting condition; and recording tail point position information EndX and EndY of the temporary track meeting the track starting condition.
And S42, calculating the ratio of the distance between the head and tail points of the temporary track meeting the track starting condition to the range.
S43, judging whether the temporary track meeting the track starting condition passes through first-level filtering or not, and judging whether the ratio of the distance between the head and the tail of the temporary track meeting the track starting condition and the range is smaller than a clutter shaking threshold ShakeThresold or not, wherein in the embodiment, the clutter shaking threshold ShakeThresold is 0.7, as shown in formula 4, if yes, the temporary track does not pass through, and executing the step S47; otherwise, through the first stage filtering, step S44 is performed.
S44, the user sets a sector area as a prohibited start area, and the prohibited start area is an area where the temporary track is not allowed to be established.
S45, judging whether the temporary flight path passes through a second-stage filtering, wherein the second-stage filtering adopts a sliding window filtering method, sliding a fan-shaped initial area, traversing all the point tracks forming the temporary flight path, and judging whether the continuous point tracks in the forbidden initial area in the temporary flight path meeting the flight path initial conditions reach a set number, in the embodiment, the set number is 3, if so, the continuous point tracks do not pass through, and executing the step S47; otherwise, through the second stage filtering, step S46 is executed;
and S46, forming a stable track.
S47, the temporary track satisfying the track start condition is deleted.
The first-stage filtering is used for filtering clutter shaking points, and when the forming point track of the temporary track is the clutter shaking points, the distance between the head point and the tail point of the temporary track is smaller than the route traveled by the temporary track; the second stage of filtering is to filter out temporary tracks formed in the forbidden starting area.
The invention is not to be considered as limited to the specific embodiments shown and described, but is to be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A track starting algorithm based on an unmanned aerial vehicle monitoring radar is characterized by comprising the following steps:
s1, the system receives first frame trace point data scanned by the radar, and temporary tracks are established for all trace points in the first frame trace point data; the trace point data includes: three-dimensional coordinate information, time information, attribute values and amplitude values of the trace points;
s2, the system receives the next frame of point trace data scanned by the radar, and makes unilateral multi-hypothesis correlation judgment on each point trace in the next frame of point trace data and each established temporary track, judges whether the next frame of point trace is related to the established temporary track, if a certain point trace in the next frame of point trace is related to the established temporary track, the related point trace is related to the established temporary track to form a unilateral multi-hypothesis temporary track; if a certain point track in the next frame point track is not related to each established temporary track, establishing the temporary track according to the point track;
s3, performing multi-model temporary track starting judgment on the single-side multi-hypothesis temporary track, judging whether the single-side multi-hypothesis temporary track meets track starting conditions, cascading the single-side multi-hypothesis temporary track with a plurality of temporary track starting models, judging whether the single-side multi-hypothesis temporary track meets the starting conditions of one of the temporary track starting models, and if so, judging that the single-side multi-hypothesis temporary track is the temporary track meeting the track starting conditions; if the one-side multi-hypothesis temporary track does not meet the track starting condition, the one-side multi-hypothesis temporary track waits for carrying out one-side multi-hypothesis correlation judgment on the next frame of point track data, and if the one-side multi-hypothesis temporary track does not have correlation point tracks within a certain continuous frame number, the one-side multi-hypothesis temporary track is deleted;
s4, performing track filtering judgment on the temporary track meeting the track starting condition, judging whether the temporary track meeting the track starting condition passes the track filtering processing or not, and if the temporary track meeting the track starting condition passes the track filtering processing, forming a stable track by the temporary track meeting the track starting condition; if the temporary flight path meeting the flight path starting condition is not subjected to the flight path filtering processing, deleting the temporary flight path meeting the flight path starting condition;
in steps S1 and S2, the track point for establishing the temporary track is the track head.
2. The track initiation algorithm based on UAV surveillance radar as claimed in claim 1, wherein in step S2, the single-side multi-hypothesis correlation decision employs a comprehensive membership correlation algorithm, which specifically comprises: and calculating the comprehensive membership degree of the trace point and each established temporary track according to the membership degree of each established temporary track and the feature factor of the trace point and the membership degree of each established temporary track and the motion state factor of the trace point, wherein the comprehensive membership degree meets a threshold set by a user and is related to the threshold.
3. The track initiation algorithm based on UAV surveillance radar as claimed in claim 2, wherein if the established temporary track is related to a plurality of tracks in the next frame of tracks, the optimal related track is selected for the temporary track, and the established temporary track is selected by only retaining one optimal related track to form a single-side multi-hypothesis temporary track, and the optimal related track is selected by: and selecting the point track with the highest comprehensive membership degree as the optimal related point track of the temporary track according to the established comprehensive membership degree of the temporary track and each point track, and reserving the optimal related point track.
4. The unmanned surveillance radar-based track initiation algorithm of claim 3, wherein in step S3, the multi-model temporary track initiation determination comprises the following specific steps:
s31, calculating the comprehensive membership degree of each frame point track forming the one-sided multi-hypothesis temporary track and the one-sided multi-hypothesis temporary track, and counting the frame number (HighSubCnt) of which the comprehensive membership degree belongs to high membership degree and the frame number (VeryHighhSubCnt) of which the comprehensive membership degree belongs to very high membership degree;
s32, calculating the course consistency of each frame point track forming the single-side multi-hypothesis temporary track and the single-side multi-hypothesis temporary track, and counting the frame numbers HighCourseUniformMcnt with consistent course and VeryHighCourseUniformMcnt with consistent course height;
s33, counting the clutter region frame number CluterCnt of each frame point track forming the unilateral multi-hypothesis temporary track;
s34, judging whether the temporary track of the single-side multi-hypothesis belongs to the clutter region or not according to the clutter region frame number CluterCnt, if not, carrying out the initial judgment of the temporary track initial model 1 on the temporary track of the single-side multi-hypothesis, and executing the step S35; if yes, performing initial judgment of the temporary track initial model 2 on the temporary track of the single-side multi-hypothesis, and executing step S36;
s35, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 1, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, performing initial judgment of the temporary track initial model 3 on the temporary track with the single-side multiple hypotheses, and executing step S38;
s36, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 2, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, go to step S37;
s37, judging whether the temporary track of the single-side multi-hypothesis belongs to a similar cleaning area, if so, performing initial judgment of a temporary track initial model 3 on the temporary track of the single-side multi-hypothesis, and executing the step S38; if not, the single-side multi-hypothesis temporary track does not meet the starting condition of the temporary track; the similar cleaning zone: the ratio of the frame number CluterCnt of the temporary track belonging to the clutter zone to the frame number FrameCnt of the temporary track is smaller than the threshold value CleanFrameThresold of the frame number of the clutter zone;
s38, judging whether the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track starting model 3, if so, judging that the temporary track of the single-side multi-hypothesis meets the starting condition of the temporary track; if not, the single-side multi-hypothesis temporary track does not meet the starting condition of the temporary track;
wherein, the track starting condition of the temporary track starting model 1 is as follows:
the HighSubThresold is the threshold of the high membership frame number;
HighCourseUniformmThreshold is a heading consistent frame number threshold;
track start conditions of the temporary track start model 2:
VeryHighSubThresold is the threshold of the frame number with very high membership;
VeryHighCourseUniformmThreshold is a heading height consistency frame number threshold;
track start conditions of the temporary track start model 3:
framecnt is the number of frames forming the temporary track of the single-side multi-hypothesis;
the CleanFrameThresold is the clutter zone frame number threshold;
InitFrameCnt is the initial frame number of the temporary track of the single-side multi-hypothesis;
InitFrameThresold is the starting threshold;
LostCnt is the number of lost point frames;
framecnt is the temporary track forming frame number of the single-side multi-hypothesis;
sub is the comprehensive membership;
SubThresold is the correlation threshold.
5. The trajectory initiation algorithm according to claim 4, wherein in step S31, if the comprehensive membership degree is greater than or equal to 0.8 and less than 0.85, the comprehensive membership degree is high; if the comprehensive membership degree is more than or equal to 0.85 and less than 1, the comprehensive membership degree belongs to a very high membership degree.
6. The track start algorithm based on UAV surveillance radar as claimed in claim 4, wherein in step S32, if the course consistency is greater than 0.5 degrees and less than or equal to 1.5 degrees, it is determined that the courses are consistent; and if the course consistency is less than or equal to 0.5 degree and greater than 0 degree, the course is consistent in height.
7. The unmanned surveillance radar-based track initiation algorithm of claim 1, wherein in step S4, the track filtering decision is performed with a first filtering stage, and the first filtering stage: calculating the ratio of the distance between the head and tail points of the temporary track meeting the track starting condition and the range of the temporary track meeting the track starting condition according to the trace point data of each frame scanned by the radar, judging whether the ratio is greater than a clutter shaking threshold, and if so, filtering by a first stage; if not, deleting the temporary track meeting the track starting condition.
8. The unmanned surveillance radar-based track initiation algorithm of claim 7, wherein the second filtering is performed on the temporary track meeting the track initiation condition by the first filtering, and the second filtering is performed on the temporary track meeting the track initiation condition: judging whether continuous point tracks in a forbidden initiation area in the temporary tracks meeting the track initiation conditions reach a set threshold value by adopting a sliding window method, and if so, deleting the temporary tracks meeting the track initiation conditions; if not, forming a stable track through second-stage filtering, namely through track filtering judgment; the starting forbidden area is an area which is set by a user and is not allowed to establish a temporary track.
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