CN113281736B - Radar maneuvering intersection target tracking method based on multi-hypothesis singer model - Google Patents

Radar maneuvering intersection target tracking method based on multi-hypothesis singer model Download PDF

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CN113281736B
CN113281736B CN202110374602.3A CN202110374602A CN113281736B CN 113281736 B CN113281736 B CN 113281736B CN 202110374602 A CN202110374602 A CN 202110374602A CN 113281736 B CN113281736 B CN 113281736B
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CN113281736A (en
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雷亚龙
郭艳艳
朱嘉启
李昆仑
吴宝杰
尹照亮
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Qingdao Rpm Electronics Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
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Abstract

The invention relates to a radar maneuvering intersection target tracking method based on a multi-hypothesis singer model, which comprises the following steps of: s1, establishing a target track; s2, tracking the target which is not in the track crossing area by using a singer model; tracking the intersected target in a track crossing area by adopting a multi-hypothesis singer model; and S3, realizing accurate and stable tracking of the target through the tracking of the multi-hypothesis singer model in the step S2. The invention has the advantages that: 1. tracking errors in the radar maneuvering intersection target tracking can be reduced, and more accurate target tracking is realized; 2. the probability of error tracking in the tracking of the radar maneuvering intersection target can be reduced, and the intersection target can be correctly tracked; 3. the invention has wide application range, has no special requirements on radar system parameters, and has popularization feasibility.

Description

Radar maneuvering intersection target tracking method based on multi-hypothesis singer model
Technical Field
The invention relates to a radar maneuvering intersection target tracking method based on a multi-hypothesis singer model, and relates to the field of radars.
Background
The method is characterized in that the radar maneuvering target tracking is a difficult problem in the field of radar data processing, and particularly when 2 maneuvering targets are intersected, because the maneuvering characteristics of the 2 targets are random and unpredictable, the radar track is correlated incorrectly, so that the tracking error of the 2 maneuvering targets is large, and even the tracking target is lost.
The Singer model has a good effect when being applied to maneuvering target tracking, and the actual motion trail of the target is described more accurately due to the fact that the Singer model fully considers the acceleration and deceleration characteristics of the target in radar target tracking, but most of the current Singer model tracks a single target, and when 2 maneuvering targets are intersected, the existing Singer model has a poor tracking effect.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a radar intersection target tracking method based on a multi-hypothesis singer model, two intersection targets are tracked by a method of combining multiple model hypotheses, the correlation between a tracking result and the previous states of the two targets is calculated, and finally a group of model tracking results with the maximum correlation value are taken as the real tracks of the intersection targets, so that the tracking error of the maneuvering targets during intersection is reduced, and the correct and stable tracking is realized.
The invention provides a radar maneuvering intersection target tracking method based on a multi-hypothesis singer model, which adopts the technical scheme that:
a radar maneuvering intersection target tracking method based on a multi-hypothesis singer model comprises the following steps:
s1, establishing a target track;
s2, tracking the target which is not in the track crossing area by using a singer model; and tracking the intersected target by adopting a multi-hypothesis singer model for the target in the track crossing area.
And S3, realizing accurate and stable tracking of the target through the tracking of the multi-hypothesis singer model in the step S2.
The step S1 includes the following steps:
(1) radar detection is carried out, and ith frame of measurement data of a target, including data target information of a target position coordinate X, a position coordinate Y, a target speed v and an acceleration a, is obtained; i is the number of data frames acquired during radar measurement;
(2) judging whether the frame number of the measured data is more than or equal to 3, if the frame number of the measured data does not meet the condition that the frame number of the measured data is more than or equal to 3, storing the measured data into a buffer area;
(3) if the condition that the number of frames is more than or equal to 3 is met, judging whether a target track is established or not, searching whether a historical track point of the target exists or not in a track library, if so, considering that the target has established the track, and if not, considering that the target does not establish the track and is a new target; if no track is established and the 2/3 navigation establishing condition is met, namely in the radar detection data of continuous 3 frames, if any frame is not less than 2 frames, the target is detected, namely the detection probability is more than or equal to 2/3, the track is established for the target; otherwise, the target measurement data buffer area is emptied if the navigation condition is not met;
(4) and if the target track is established, judging whether the target is in a track crossing area.
In step S2, the target tracking method using the singer model is as follows:
the Singer model is a tracking model considering the acceleration characteristic of the target, and the tracking model has the following expression form:
Figure BDA0003010694730000031
wherein:
x (t) is the position of the target at time t;
Figure BDA0003010694730000032
is the first differential of X (t), which is the speed of the target at time t;
Figure BDA0003010694730000033
is the second order differential of X (t), which is the acceleration of the target at time t;
Figure BDA0003010694730000034
is the third order differential to X (t); a is the reciprocal of maneuver time, called maneuver frequency, w (t) is the process noise at time t;
if the sampling interval during radar target tracking is T, the target tracking discrete dynamic equation of the singer model is as follows:
X(k+1)＝F(k)X(k)+w(k)
wherein, X (k + 1): a target position at time k + 1;
x (k): a target position at time k;
f (k): a state transition matrix at time k;
w (k): process noise at time k;
the state transition matrix F is:
Figure BDA0003010694730000041
the covariance matrix of the process noise w (k) is:
Figure BDA0003010694730000042
q (k) is the covariance matrix of the process noise V at time k;
q11、q12、q13、q21、q22、q23、q31、q32、q33: the autocorrelation covariance value of the process noise w (k);
wherein:
Figure BDA0003010694730000043
Figure BDA0003010694730000044
Figure BDA0003010694730000045
Figure BDA0003010694730000046
Figure BDA0003010694730000047
Figure BDA0003010694730000048
where a is the inverse of the maneuver time, called the maneuver frequency
T: sampling intervals when the radar tracks the target;
e: a mathematical constant with a value of 2.71828;
the method for tracking the target by adopting the multi-hypothesis singer model comprises the following steps:
(1) assuming that 2 targets are X1 and X2, respectively, in the process of converging two targets, each target will generate three motion states of acceleration, deceleration and uniform speed, respectively, and then the 2 converged targets will generate the following 9 mechanical combinations: the first state is X1 acceleration, X2 acceleration; the second state is that X1 accelerates, and X2 is at a constant speed; the third state is that X1 accelerates and X2 decelerates; the fourth state is that X1 is at a constant speed and X2 is accelerated; the fifth state is that X1 is at a constant speed, and X2 is at a constant speed; the sixth state is that X1 is at a constant speed, and X2 decelerates; the seventh state is X1 deceleration, X2 acceleration; the eighth state is that X1 decelerates, and X2 keeps constant speed; the ninth state is X1 deceleration, X2 deceleration;
(2) at time K, assuming the states of target X1 and target X2 are the first state, the singer tracking state vectors for both targets X1 and X2 are:
Figure BDA0003010694730000051
Figure BDA0003010694730000052
wherein
Figure BDA0003010694730000053
And the measured values of the two targets are obtained
Figure BDA0003010694730000054
The measurement errors of the two targets in the first state are respectively: ε 11(k)＝X1(k)-Y1(k)、ε12(k)＝X2(k)-Y2(k) And respectively calculating the normalized error variance of the quantity targets as follows:
Figure BDA0003010694730000055
where C is a normalization coefficient, C ═ 0.50.30.2]'；
(3) The mean normalized error variance of target X1 and target X2 at the first state is calculated as:
Figure BDA0003010694730000056
(4) respectively calculating according to the mode of the step (2), and obtaining the average normalized error variance from the second state to the ninth state as follows: e2, e3... e 9; comparing the sizes of e1, e2, and e e3., e9, the minimum error variance is the state with the strongest correlation, the state with the strongest correlation is assumed to be the nth state, and is marked as en, when the target tracking state is updated, the target is tracked by using the nth group of state parameters.
The maneuvering frequency has a value of 1/10.
The step (4) is specifically as follows: and traversing all the tracks in the track library, and solving the two-dimensional distance of the plane space for any two tracks in the track library, wherein the distance D is (X1-X2) ^2+ (Y1-Y2) ^2, if D is less than or equal to 20m, the track is considered as a track crossing area, and otherwise, the track is considered as a track non-crossing area.
The invention has the advantages that:
1. the tracking error in the radar maneuvering intersection target tracking can be reduced, and more accurate target tracking is realized;
2. the probability of error tracking in the tracking of the radar maneuvering intersection target can be reduced, and the intersection target can be correctly tracked;
3. the invention has wide application range, has no special requirements on radar system parameters, and has popularization feasibility.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. These examples are illustrative only and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.
Referring to fig. 1, the invention relates to a radar maneuvering intersection target tracking method based on a multi-hypothesis singer model, comprising the following steps:
s1, establishing a target track;
s2, tracking the target which is not in the track crossing area by using a singer model; and tracking the intersected target by adopting a multi-hypothesis singer model for the target in the track crossing area.
And S3, realizing accurate and stable tracking of the target through the tracking of the multi-hypothesis singer model in the step S2.
The step S1 includes the following steps:
(1) radar detection is carried out, and ith frame of measurement data of a target, including data target information of a target position coordinate X, a position coordinate Y, a target speed v and an acceleration a, is obtained; i is the number of data frames acquired during radar measurement;
(2) judging whether the frame number of the measured data is more than or equal to 3, if the frame number of the measured data does not meet the condition that the frame number of the measured data is more than or equal to 3, storing the measured data into a buffer area;
(3) if the condition that the number of frames is more than or equal to 3 is met, judging whether a target track is established or not, searching whether a historical track point of the target exists or not in a track library, if so, considering that the target has established the track, and if not, considering that the target does not establish the track and is a new target; if no track is established and the 2/3 navigation establishing condition is met, namely in the radar detection data of continuous 3 frames, if any frame is not less than 2 frames, the target is detected, namely the detection probability is more than or equal to 2/3, the track is established for the target; otherwise, the navigation condition is not satisfied, and the target measurement data buffer area is emptied;
(4) if the target track is established, judging whether the target is in a track crossing area, wherein the step (4) specifically comprises the following steps: and traversing all the tracks in the track library, and solving the two-dimensional distance of the plane space for any two tracks in the track library, wherein the distance D is (X1-X2) ^2+ (Y1-Y2) ^2, if D is less than or equal to 20m, the track is considered as a track crossing area, and otherwise, the track is considered as a track non-crossing area. For example, the plane distances of track 1 and track 2 are: d12 ═ X1-X2 ^2+ (Y1-Y2) ^2, if D12 is less than or equal to 20m, the area is considered to be the track crossing area, otherwise, the area is considered to be the track non-crossing area.
In step S2, the target tracking method using the singer model is as follows:
the Singer model is a tracking model considering the acceleration characteristic of the target, and the tracking model has the following expression form:
Figure BDA0003010694730000081
wherein:
x (t) is the position of the target at time t;
Figure BDA0003010694730000082
is the first differential of X (t), which is the speed of the target at time t;
Figure BDA0003010694730000083
is the second order differential of X (t), which is the acceleration of the target at time t;
Figure BDA0003010694730000084
is the third order differential to X (t); a is the reciprocal of maneuver time, called maneuver frequency, w (t) is the process noise at time t;
if the sampling interval during radar target tracking is T, the target tracking discrete dynamic equation of the singer model is as follows:
X(k+1)＝F(k)X(k)+w(k)
wherein, X (k + 1): a target position at time k + 1;
x (k): a target position at time k;
f (k): a state transition matrix at time k;
w (k): process noise at time k;
the state transition matrix F is:
Figure BDA0003010694730000085
the covariance matrix of the process noise w (k) is:
Figure BDA0003010694730000091
q (k) is the covariance matrix of the process noise V at time k;
q11、q12、q13、q21、q22、q23、q31、q32、q33: the autocorrelation covariance value of the process noise w (k);
wherein:
Figure BDA0003010694730000092
Figure BDA0003010694730000093
Figure BDA0003010694730000094
Figure BDA0003010694730000095
Figure BDA0003010694730000096
Figure BDA0003010694730000097
where a is the inverse of the maneuver time, called the maneuver frequency
T: sampling intervals when the radar tracks the target;
e: a mathematical constant with a value of 2.71828;
the method for tracking the target by adopting the multi-hypothesis singer model comprises the following steps:
(1) assuming that 2 targets are X1 and X2, respectively, in the process of converging two targets, each target will generate three motion states of acceleration, deceleration and uniform speed, respectively, and then the 2 converged targets will generate the following 9 mechanical combinations: the first state is X1 acceleration, X2 acceleration; the second state is that X1 accelerates, and X2 is at a constant speed; the third state is that X1 accelerates and X2 decelerates; the fourth state is that X1 is at a constant speed and X2 is accelerated; the fifth state is that X1 is at a constant speed, and X2 is at a constant speed; the sixth state is that X1 is at a constant speed, and X2 decelerates; the seventh state is X1 deceleration, X2 acceleration; the eighth state is that X1 decelerates, and X2 keeps constant speed; the ninth state is X1 deceleration, X2 deceleration;
(2) at time K, assuming the states of target X1 and target X2 are the first state, the singer tracking state vectors for both targets X1 and X2 are:
Figure BDA0003010694730000101
Figure BDA0003010694730000102
wherein
Figure BDA0003010694730000103
And the measured values of the two targets are obtained
Figure BDA0003010694730000104
The measurement errors of the two targets in the first state are respectively: ε 11(k)＝X1(k)-Y1(k)、ε12(k)＝X2(k)-Y2(k) And respectively calculating the normalized error variance of the quantity targets as follows:
Figure BDA0003010694730000105
where C is a normalization coefficient, C ═ 0.50.30.2]'；
(3) The mean normalized error variance of target X1 and target X2 at the first state is calculated as:
Figure BDA0003010694730000106
(4) respectively calculating according to the mode of the step (2), and obtaining the average normalized error variance from the second state to the ninth state as follows: e2, e3... e 9; comparing the magnitudes of e1, e2, and e e3... e9, the smallest error variance is the state with the strongest correlation, and the state with the strongest correlation is assumed to be the nth state and is marked as en (which may be one of e1, e2, and e e3... e 9), and when the target tracking state is updated, the target is tracked by using the nth group of state parameters.
The maneuvering frequency has a value of 1/10.
The principle of the invention is as follows: firstly, assuming that two intersected targets respectively perform three different maneuvers (acceleration, deceleration and uniform speed) in a tracking interval time, then respectively assuming that the two targets are in one of three maneuvering states, establishing nine different maneuvering combination models, then tracking the two intersected targets by adopting the nine different combination models, then respectively calculating the correlation between the nine tracking results and the latest state of the targets, and finally taking a group of model tracking results with the maximum correlation value to be regarded as a real track of the intersected target, thereby realizing the stable and correct tracking of the maneuvering intersected target.

Claims (3)

1. A radar maneuvering intersection target tracking method based on a multi-hypothesis singer model is characterized by comprising the following steps of:
s1, establishing a target track;
s2, tracking the target which is not in the track crossing area by using a singer model; tracking the intersected target in a track crossing area by adopting a multi-hypothesis singer model;
s3, realizing accurate and stable tracking of the target through tracking of the multi-hypothesis singer model in the step S2;
the step S1 includes the following steps:
(1) radar detection is carried out, and ith frame of measurement data of a target, including data target information of a target position coordinate X, a position coordinate Y, a target speed v and an acceleration a, is obtained; i is the number of data frames acquired during radar measurement;
(2) judging whether the frame number of the measured data is more than or equal to 3, if the frame number of the measured data does not meet the condition that the frame number of the measured data is more than or equal to 3, storing the measured data into a buffer area;
(3) if the condition that the number of frames is more than or equal to 3 is met, judging whether a target track is established or not, searching whether a historical track point of the target exists or not in a track library, if so, considering that the target has established the track, and if not, considering that the target does not establish the track and is a new target; if no track is established and the 2/3 navigation establishing condition is met, namely in the radar detection data of continuous 3 frames, if any not less than 2 frames detect the target, namely the detection probability is not less than 2/3, establishing the track for the target; otherwise, the target measurement data buffer area is emptied if the navigation condition is not met;
(4) if the target track is established, judging whether the target is in a track crossing area;
in step S2, the target tracking method using the singer model is as follows:
the Singer model is a tracking model considering the acceleration characteristic of the target, and the tracking model has the following expression form:
Figure FDA0003600262020000021
wherein:
x (t) is the position of the target at time t;
Figure FDA0003600262020000022
is the first differential of X (t), which is the speed of the target at time t;
Figure FDA0003600262020000023
is the second order differential of X (t), which is the acceleration of the target at time t;
Figure FDA0003600262020000024
is the third order differential to X (t); a is the reciprocal of maneuver time, called maneuver frequency, w (t) is the process noise at time t;
if the sampling interval during radar target tracking is T, the target tracking discrete dynamic equation of the singer model is as follows:
X(k+1)＝F(k)X(k)+w(k)
wherein, X (k + 1): a target position at time k + 1;
x (k): a target position at time k;
f (k): a state transition matrix at time k;
w (k): process noise at time k;
the state transition matrix F is:
Figure FDA0003600262020000025
the covariance matrix of the process noise w (k) is:
Figure FDA0003600262020000031
q (k) is the covariance matrix of the process noise V at time k;
q11、q12、q13、q21、q22、q23、q31、q32、q33: the autocorrelation covariance value of the process noise w (k);
wherein:
Figure FDA0003600262020000032
Figure FDA0003600262020000033
Figure FDA0003600262020000034
Figure FDA0003600262020000035
Figure FDA0003600262020000036
Figure FDA0003600262020000037
wherein a is the inverse of the maneuver time, called the maneuver frequency
T: sampling intervals when the radar tracks the target;
e: a mathematical constant with a value of 2.71828;
the method for tracking the target by adopting the multi-hypothesis singer model comprises the following steps:
(1) assuming that 2 targets are X1 and X2, respectively, in the process of converging two targets, each target will generate three motion states of acceleration, deceleration and uniform speed, respectively, and then the 2 converged targets will generate the following 9 mechanical combinations: the first state is X1 acceleration, X2 acceleration; the second state is that X1 accelerates, and X2 is at a constant speed; the third state is that X1 accelerates and X2 decelerates; the fourth state is that X1 is at a constant speed and X2 is accelerated; the fifth state is that X1 is at a constant speed, and X2 is at a constant speed; the sixth state is that X1 is at a constant speed, and X2 decelerates; the seventh state is X1 deceleration, X2 acceleration; the eighth state is that X1 decelerates, and X2 keeps constant speed; the ninth state is X1 deceleration, X2 deceleration;
(2) at time K, assuming the states of target X1 and target X2 are the first state, the singer tracking state vectors for both targets X1 and X2 are:
Figure FDA0003600262020000041
Figure FDA0003600262020000042
wherein
Figure FDA0003600262020000043
And the measured values of the two targets are obtained
Figure FDA0003600262020000044
The measurement errors of the two targets in the first state are respectively: ε 11(k)＝X1(k)-Y1(k)、ε12(k)＝X2(k)-Y2(k) And respectively calculating the normalized error variance of the quantity targets as follows:
Figure FDA0003600262020000045
wherein C is a normalization coefficient, C ═ 0.50.30.2 ]';
(3) the mean normalized error variance of target X1 and target X2 at the first state is calculated as:
Figure FDA0003600262020000046
(4) respectively calculating according to the mode of the step (2), and obtaining the average normalized error variance from the second state to the ninth state as follows: e2, e3... e 9; comparing the sizes of e1, e2, and e e3., e9, the minimum error variance is the state with the strongest correlation, the state with the strongest correlation is assumed to be the nth state, and is marked as en, when the target tracking state is updated, the target is tracked by using the nth group of state parameters.
2. The method for tracking the radar maneuvering intersection target based on the multi-hypothesis singer model as recited in claim 1, wherein the maneuvering frequency is 1/10.
3. The method for tracking the radar maneuvering intersection target based on the multi-hypothesis singer model as recited in claim 1, wherein the step (4) in the step S1 is specifically: and traversing all the tracks in the track library, and solving the two-dimensional distance of the plane space for any two tracks in the track library, wherein the distance D is (X1-X2) ^2+ (Y1-Y2) ^2, if D is less than or equal to 20m, the track is considered as a track crossing area, and otherwise, the track is considered as a track non-crossing area.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109633589A (en) * 2019-01-08 2019-04-16 沈阳理工大学 The Multi-target Data Associations assumed are optimized based on multi-model more in target following
CN111289965A (en) * 2019-12-04 2020-06-16 南京长峰航天电子科技有限公司 Multi-target radar rapid tracking method and system

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US6724916B1 (en) * 2000-01-05 2004-04-20 The United States Of America As Represented By The Secretary Of The Navy Composite hough transform for multitarget multisensor tracking

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Publication number Priority date Publication date Assignee Title
CN109633589A (en) * 2019-01-08 2019-04-16 沈阳理工大学 The Multi-target Data Associations assumed are optimized based on multi-model more in target following
CN111289965A (en) * 2019-12-04 2020-06-16 南京长峰航天电子科技有限公司 Multi-target radar rapid tracking method and system

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