CN113504527A - Radar target prediction processing method and system - Google Patents
Radar target prediction processing method and system Download PDFInfo
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- CN113504527A CN113504527A CN202111065968.9A CN202111065968A CN113504527A CN 113504527 A CN113504527 A CN 113504527A CN 202111065968 A CN202111065968 A CN 202111065968A CN 113504527 A CN113504527 A CN 113504527A
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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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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Abstract
The embodiment of the invention discloses a radar target prediction processing method, which comprises the following steps: determining time ranges of historical long track points, historical conventional track points and historical short track points as prediction parameters; judging whether the target is a static target, if so, determining the time range of the historical ultralong track points, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters; on the basis of a uniform linear model, respectively predicting target positions in three track point modes by adopting prediction parameters or new prediction parameters; and respectively predicting the maximum motion range of the target in the three track point modes by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model. The embodiment of the invention also discloses a radar target prediction processing system. The method can solve the problem of unstable tracking of the static target and the maneuvering target, and improve the accuracy of target prediction and the stability of target tracking.
Description
Technical Field
The invention relates to the technical field of radars, in particular to a radar target prediction processing method and a radar target prediction processing system.
Background
The core of the traditional target prediction processing method is to calculate the duration of historical track points participating in prediction, predict the state information of the constant-speed moving target at the next moment based on the least square method, however, for the static target and the maneuvering target, the duration of the historical track points participating in prediction is not effectively calculated and updated, so that the static target and the maneuvering target cannot be distinguished to adopt more appropriate prediction parameters to predict the target state, the prediction of the state information of the static target and the maneuvering target is inaccurate, and the problems of unstable target tracking and target loss occur.
Disclosure of Invention
The invention aims to provide a radar target prediction processing method and a radar target prediction processing system, which can solve the problem that the tracking of a static target and a maneuvering target is unstable, and improve the accuracy of target prediction and the stability of target tracking.
The embodiment of the invention provides a radar target prediction processing method, which comprises the following steps:
determining time ranges of historical long track points, historical conventional track points and historical short track points according to the information of the historical track points of the target, and taking the time ranges as prediction parameters;
judging whether the target is a static target or not according to the information of the historical track points of the target, if so, determining the time range of the historical ultralong track points, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are track points obtained by prolonging the number of the historical ultralong track points according to the preset value;
on the basis of a uniform linear model, respectively predicting target positions in three track point modes by adopting the prediction parameters or the new prediction parameters, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and respectively predicting the maximum motion range of the target in the three track point modes by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
As a further improvement of the invention, the information of the target historical track points comprises information of n historical track points of the target,
the step of judging whether the target is a static target or not according to the information of the historical track points of the target comprises the following steps:
respectively determining the displacement offset X between two adjacent track points between the current moment track point and the nth historical track point of the target1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
As a further improvement of the invention, for any one track point mode of the three track point modes, the method comprises m historical track points,
the predicting method for predicting the target positions in the three track point modes by adopting the prediction parameters or the new prediction parameters based on the uniform linear model comprises the following steps:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively showing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the x direction, and y1、y2、…、ymRespectively representing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current moment.
As a further improvement of the present invention, for any one of the three trace point modes,
the method includes the following steps that target positions in three track point modes obtained by prediction are adopted on the basis of an acceleration model and a steering model, and the maximum motion ranges of targets in the three track point modes are respectively predicted, and the method includes the following steps:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point.
The embodiment of the invention also provides a radar target prediction processing system, which comprises:
the prediction parameter determining module is used for determining time ranges of the historical long track points, the historical conventional track points and the historical short track points as prediction parameters according to the information of the historical track points of the target;
the static target judgment module is used for judging whether a target is a static target or not according to the information of historical track points of the target, determining the time range of the historical ultralong track points if the target is the static target, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are track points obtained by prolonging the number of the historical ultralong track points according to preset values;
the target position prediction module is used for predicting target positions in three track point modes respectively by adopting the prediction parameters or the new prediction parameters based on a uniform linear model, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and the target motion range prediction module is used for predicting the maximum motion range of the target in the three track point modes respectively by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
As a further improvement of the present invention, the information of the target historical track points includes information of n historical track points of the target, and the static target determination module includes:
at the current time track of the targetRespectively determining the displacement offset X between two adjacent track points between the track point and the nth historical track point1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
As a further improvement of the present invention, for any one of the three track point modes, the target position prediction module includes m historical track points, and includes:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively showing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the x direction, and y1、y2、…、ymRespectively representing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current moment.
As a further improvement of the present invention, for any one of the three track point modes, the target motion range prediction module includes:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point.
Embodiments of the present invention also provide an electronic device, which includes a memory and a processor, where the memory is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the method.
Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method.
The invention has the beneficial effects that:
the method adopts the information of various historical track points as prediction parameters, and carries out target state parameter estimation on various track point modes respectively based on a least square algorithm. The method of the invention adds the judgment of the static target before predicting the target state, updates the prediction parameter when the target is the static target, and adopts the new prediction parameter to predict the target state. The prediction parameters of the static target and the maneuvering target are distinguished, and the target state is predicted by respectively adopting more appropriate prediction parameters, so that the problem of unstable tracking of the static target and the maneuvering target can be solved, and the accuracy of target prediction and the stability of target tracking are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a flowchart illustrating a radar target prediction processing method according to an exemplary embodiment of 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.
It should be noted that, if directional indications (such as up, down, left, right, front, and back … …) are involved in the embodiment of the present invention, the directional indications are only used to explain the relative positional relationship between the components, the movement situation, and the like in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indications are changed accordingly.
In addition, in the description of the present invention, the terms used are for illustrative purposes only and are not intended to limit the scope of the present invention. The terms "comprises" and/or "comprising" are used to specify the presence of stated elements, steps, operations, and/or components, but do not preclude the presence or addition of one or more other elements, steps, operations, and/or components. The terms "first," "second," and the like may be used to describe various elements, not necessarily order, and not necessarily limit the elements. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. These terms are only used to distinguish one element from another. These and/or other aspects will become apparent to those of ordinary skill in the art in view of the following drawings, and the description of the embodiments of the present invention will be more readily understood by those of ordinary skill in the art. The drawings are only for purposes of illustrating the described embodiments of the invention. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated in the present application may be employed without departing from the principles described in the present application.
As shown in fig. 1, a method for radar target prediction processing according to an embodiment of the present invention includes:
determining time ranges of historical long track points, historical conventional track points and historical short track points according to the information of the historical track points of the target, and taking the time ranges as prediction parameters;
judging whether the target is a static target or not according to the information of the historical track points of the target, if so, determining the time range of the historical ultralong track points, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are obtained by increasing the number of the historical ultralong track points according to preset;
on the basis of a uniform linear model, respectively predicting target positions in three track point modes by adopting the prediction parameters or the new prediction parameters, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and respectively predicting the maximum motion range of the target in the three track point modes by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
The radar target prediction mainly comprises filtering detection, track point processing and track processing. The traditional filtering detection adopts a transverse false alarm detection method, a self-adaptive filtering curve is provided for the radar, the radar system can filter most of clutter and noise according to the filtering curve, and a target echo at a detection position is effective. And (4) the trace point processing is used for condensing, creating and matching the echo after filtering detection according to the correlation between the scanning lines. And the track processing is used for carrying out track creation, matching and association according to the correlation of track points.
The radar target prediction mainly uses the successfully matched target and the historical track information of the target to predict the state of the target at the next moment, and mainly uses the target parameters (speed, course and position) at the current moment and the historical moment of the target to predict and update the parameters (speed, course and position) of the target at the next frame. The core of the radar target prediction method is a target prediction algorithm, and the state of a target is optimally estimated according to current target parameters and historical target parameters. The core of the traditional target prediction processing based on the least square method is to calculate the duration of the historical track points participating in prediction, and can predict the state information of the target moving at a constant speed at the next moment.
The method adopts information of various historical track points as prediction parameters and carries out target state parameter estimation based on a least square algorithm. Before predicting the target state, whether the target is a static target needs to be judged, when the target is judged to be the static target, the prediction parameters are updated (the prediction parameters are updated by using the time range of the historical ultralong track points), the target state is predicted by using the new prediction parameters (the information of the historical ultralong track points, the information of the historical conventional track points and the information of the historical short track points of the target), and when the target is judged not to be the static target, the target state is predicted by still using the prediction parameters (the information of the historical ultralong track points, the information of the historical conventional track points and the information of the historical short track points). According to the method, the judgment of the static target is added before the target state is predicted, the static target and the maneuvering target are distinguished, the prediction parameters of the static target are updated, different track point information is used as the prediction parameters of different targets to predict the target state, the problem that the static target and the maneuvering target are unstable in tracking can be solved, the different targets can be well distinguished to realize effective state prediction of the different targets, and the accuracy of target prediction and the stability of target tracking are improved. In addition, the method can also carry out target single-frame motion range estimation on multiple track point modes according to the acceleration and the steering rate of the target so as to obtain the motion range of a static target or a maneuvering target, and can further improve the accuracy of target prediction and the stability of target tracking. And after the position of the target is predicted based on the uniform linear model, the predicted position is used for predicting the maximum motion range of the target, and the predicted maximum motion range is used for subsequent target matching.
The method determines the number of three track points (long track point, conventional track point and short track point) according to preset conditions, and respectively calculates the time ranges of the three track points according to the time of one circle of rotation of the radar, so that the time ranges of the historical long track point, the historical conventional track point and the historical short track point can be obtained and used as prediction parameters in target prediction. The time range of the historical long track points can be understood as the time of each historical long track point, the time range of the historical conventional track points can be understood as the time of each historical conventional track point, and the time range of the historical short track points can be understood as the time of each historical short track point. The ultra-long track point is an estimation point obtained by extending the number of the long track point, and the historical ultra-long track point is a track point obtained by extending the number of the historical long track point.
In an alternative embodiment, the information of the target historical track points includes information of n historical track points of the target,
the step of judging whether the target is a static target or not according to the information of the historical track points of the target comprises the following steps:
respectively determining the displacement offset X between two adjacent track points between the current moment track point and the nth historical track point of the target1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
It can be understood that n is an integer greater than 3, n +1 track points are arranged between the track point at the current moment and the nth historical track point, and when judging whether the target is a static target, the displacement offset between two adjacent track points between the n +1 track points needs to be calculated respectively to obtain n displacement offsets which are X respectively1、X2、…、XnAnd then the displacement offset sum S can be obtained. And judging that the preset threshold T of the static target is a preset coefficient, wherein the value of the coefficient is not specifically limited by the invention. Because environmental factors such as wind, wave and the like and the later data processing process cause speed deviation of a static target detected by a radar, the method provided by the invention utilizes the displacement offset as a judgment parameter of the static target, can eliminate the speed deviation in the process of detecting the static target, and improves the tracking stability of the static target.
In an alternative embodiment, for any one of the three track point modes, including m historical track points,
the predicting method for predicting the target positions in the three track point modes by adopting the prediction parameters or the new prediction parameters based on the uniform linear model comprises the following steps:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively showing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the x direction, and y1、y2、…、ymRespectively representing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current moment.
A rectangular coordinate system is established by taking the radar position as an origin, the positive north direction as the positive y-axis direction and the positive east direction as the positive x-axis direction. In the present application, the x-axis direction and the y-axis direction are simply referred to as the x-direction and the y-direction. x is the number of1、x2、…、xmAnd y1、y2、…、ymThe target position is predicted by using the measured values of the historical track points.
And determining the slope bx and the intercept cx of the constant-speed straight line model in the x direction by adopting a least square method according to the position of each historical track point in the x direction and the time of each historical track point. Correspondingly, the slope by and the intercept cy of the uniform linear model in the y direction can be determined by adopting a least square method according to the position of each historical track point in the y direction and the time of each historical track point. The number m of each historical track point is not specifically limited in the present invention. It can also be understood that m in the number m of the historical long trace points, the number m of the historical conventional trace points, and the number m of the historical short trace points may respectively represent different numbers, and the present invention is not particularly limited.
It should be noted that the method of the present invention is directed to three trace point modes, including a long trace point mode, a conventional trace point mode, and a short trace point mode. And respectively determining the target position by adopting the method for any one of the three track point modes.
(1) For the long track point mode, the slope bx and the intercept cx of the constant-speed straight line model in the x direction, and the slope by and the intercept cy in the y direction are obtained by adopting the method in the long track point mode, so that the position Sx of the target in the x direction and the position Sy in the y direction are obtained.
The first historical track point, the second historical track point, … and the mth historical track point can be understood as the first historical long track point, the second historical long track point, … and the mth historical long track point in the historical long track points.
x1、x2、…、xmThe positions of the first historical long track point, the second historical long track point, … and the mth historical long track point in the x direction, y1、y2、…、ymThe positions of the first historical long track point, the second historical long track point, … and the mth historical long track point in the y direction are respectively.
If it is judged not to be the static target, t1、t2、…、tmThe time of the first historical long track point, the second historical long track point, … and the mth historical long track point. If it is determined as a static target, t1、t2、…、tmThe time of the first historical ultralong track point, the second historical ultralong track point, … and the mth historical ultralong track point.
(2) For the conventional track point mode, the slope bx and the intercept cx of the constant-speed straight line model in the x direction and the slope by and the intercept cy in the y direction are obtained by the method in the conventional track point mode, and then the position Sx of the target in the x direction and the position Sy in the y direction are obtained.
The first historical track point, the second historical track point, … and the mth historical track point can be understood as the first historical conventional track point, the second historical conventional track point, … and the mth historical conventional track point in the historical conventional track points.
x1、x2、…、xmRespectively being the positions of a first historical conventional track point, a second historical conventional track point, … and an mth historical conventional track point in the x direction, y1、y2、…、ymRespectively being the positions t of the first historical conventional track point, the second historical conventional track point, … and the mth historical conventional track point in the y direction1、t2、…、tmThe first historical conventional track point, the second historical conventional track point, …And the mth time of the conventional track point.
(3) For the short track point mode, the slope bx and the intercept cx of the constant-speed straight line model in the x direction, and the slope by and the intercept cy in the y direction are obtained by adopting the method in the short track point mode, and then the position Sx of the target in the x direction and the position Sy in the y direction are obtained.
The first historical track point, the second historical track point, … and the mth historical track point can be understood as the first historical short track point, the second historical short track point, … and the mth historical short track point in the historical short track points.
x1、x2、…、xmRespectively the positions of the first historical short track point, the second historical short track point, … and the mth historical short track point in the x direction, y1、y2、…、ymRespectively being the positions t of the first historical short track point, the second historical short track point, … and the mth historical short track point in the y direction1、t2、…、tmThe time of the first historical short track point, the second historical short track point, … and the mth historical short track point.
In an alternative embodiment, for any one of the three trace point modes,
the method includes the following steps that target positions in three track point modes obtained through prediction are adopted on the basis of an acceleration steering model, and the maximum motion ranges of targets in the three track point modes are respectively predicted, and the method includes the following steps:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point. The acceleration a of the target and the turning rate of the target are preset values, and preset adjustment can be performed according to a test result of data.
As mentioned above, the method of the present invention is directed to three trace point modes, including a long trace point mode, a normal trace point mode, and a short trace point mode. And for any one of the three track point modes, determining the maximum motion range of the target by respectively adopting the method. Wherein, the acceleration steering model is:
(1) for the long trace point mode, when determining dPosX and dPosY, bx and by therein are respectively the slope bx of the constant-speed straight line model calculated by the foregoing method in the long trace point mode in the x direction and the slope by in the y direction.
(2) For the conventional track point mode, when determining dPosX and dPosY, bx and by therein are respectively the slope bx of the constant-speed straight-line model calculated by the foregoing method in the conventional track point mode in the x direction and the slope by in the y direction.
(3) For the short track point mode, when determining dPosX and dPosY, bx and by therein are respectively the slope bx of the constant-speed straight line model calculated by the foregoing method in the short track point mode in the x direction and the slope by in the y direction.
The objective prediction process of the method of the present invention will be described in detail below with reference to fig. 1.
S1, calculating time ranges (namely time ranges participating in target prediction) tl, tp and ts of the historical long track points, the historical conventional track points and the historical short track points according to the information of the historical track points of the target;
s2, judging whether the target is a static target, if so, calculating the time range (namely the time range participating in target prediction) tl' of the historical ultralong track point to update tl, and entering S3, otherwise entering S3;
s3, based on the uniform linear model, adopting tl or tl' to predict the target position in the long track point mode, adopting tp to predict the target position in the conventional track point mode, and adopting ts to predict the target position in the short track point mode;
and S4, based on the acceleration steering model, predicting the maximum motion range of the target in the long track point mode by adopting the slope in the constant-speed linear model in the long track point mode obtained by prediction in S3, predicting the maximum motion range of the target in the conventional track point mode by adopting the slope in the constant-speed linear model in the conventional track point mode obtained by prediction in S3, and predicting the maximum motion range of the target in the short track point mode by adopting the slope in the constant-speed linear model in the short track point mode obtained by prediction in S3.
The embodiment of the invention provides a radar target prediction processing system, which comprises:
the prediction parameter determining module is used for determining time ranges of the historical long track points, the historical conventional track points and the historical short track points as prediction parameters according to the information of the historical track points of the target;
the static target judgment module is used for judging whether a target is a static target or not according to the information of historical track points of the target, determining the time range of the historical ultralong track points if the target is the static target, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are track points obtained by prolonging the number of the historical ultralong track points according to preset values;
the target position prediction module is used for predicting target positions in three track point modes respectively by adopting the prediction parameters or the new prediction parameters based on a uniform linear model, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and the target motion range prediction module is used for predicting the maximum motion range of the target in the three track point modes respectively by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
In an optional implementation manner, the information of the target historical track points includes information of n historical track points of the target, and the static target determination module includes:
respectively determining the displacement offset X between two adjacent track points between the current moment track point and the nth historical track point of the target1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
In an optional embodiment, for any one of the three track point modes, the target position prediction module includes m historical track points:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively showing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the x direction, and y1、y2、…、ymRespectively representing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current moment.
In an optional embodiment, for any one of the three track point modes, the target motion range prediction module includes:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point.
The invention also relates to an electronic device comprising the server, the terminal and the like. The electronic device includes: at least one processor; a memory communicatively coupled to the at least one processor; and a communication component communicatively coupled to the storage medium, the communication component receiving and transmitting data under control of the processor; wherein the memory stores instructions executable by the at least one processor to implement the method of the above embodiments.
In an alternative embodiment, the memory is used as a non-volatile computer-readable storage medium for storing non-volatile software programs, non-volatile computer-executable programs, and modules. The processor executes various functional applications of the device and data processing, i.e., implements the method, by executing nonvolatile software programs, instructions, and modules stored in the memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be connected to the external device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory and, when executed by the one or more processors, perform the methods of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
The present invention also relates to a computer-readable storage medium for storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Furthermore, those of ordinary skill in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
It will be understood by those skilled in the art that while the present invention has been described with reference to exemplary embodiments, various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (10)
1. A radar target prediction processing method, the method comprising:
determining time ranges of historical long track points, historical conventional track points and historical short track points according to the information of the historical track points of the target, and taking the time ranges as prediction parameters;
judging whether the target is a static target or not according to the information of the historical track points of the target, if so, determining the time range of the historical ultralong track points, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are track points obtained by prolonging the number of the historical ultralong track points according to the preset value;
on the basis of a uniform linear model, respectively predicting target positions in three track point modes by adopting the prediction parameters or the new prediction parameters, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and respectively predicting the maximum motion range of the target in the three track point modes by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
2. The method of claim 1, wherein the information of the target historical track points includes information of n historical track points of the target,
the step of judging whether the target is a static target or not according to the information of the historical track points of the target comprises the following steps:
respectively determining two adjacent track points between the current moment track point and the nth historical track point of the targetDisplacement offset X between trace points1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
3. The method of claim 1, wherein for any one of the three trace point patterns, including m historical trace points,
the predicting method for predicting the target positions in the three track point modes by adopting the prediction parameters or the new prediction parameters based on the uniform linear model comprises the following steps:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively representing a first historical track point, a second historical track point, …,Position of mth history track point in x direction, y1、y2、…、ymRespectively representing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current time.
4. A method according to claim 3, wherein, for any one of the three patterns of trace points,
the method includes the following steps that target positions in three track point modes obtained through prediction are adopted on the basis of an acceleration steering model, and the maximum motion ranges of targets in the three track point modes are respectively predicted, and the method includes the following steps:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point.
5. A radar target prediction processing system, the system comprising:
the prediction parameter determining module is used for determining time ranges of the historical long track points, the historical conventional track points and the historical short track points as prediction parameters according to the information of the historical track points of the target;
the static target judgment module is used for judging whether a target is a static target or not according to the information of historical track points of the target, determining the time range of the historical ultralong track points if the target is the static target, and updating the time range of the historical ultralong track points by using the time range of the historical ultralong track points to obtain new prediction parameters, wherein the historical ultralong track points are track points obtained by prolonging the number of the historical ultralong track points according to preset values;
the target position prediction module is used for predicting target positions in three track point modes respectively by adopting the prediction parameters or the new prediction parameters based on a uniform linear model, wherein the three track point modes comprise a long track point mode, a conventional track point mode and a short track point mode;
and the target motion range prediction module is used for predicting the maximum motion range of the target in the three track point modes respectively by adopting the target positions in the three track point modes obtained by prediction based on the accelerated steering model.
6. The system of claim 5, wherein the information of the target historical track points includes information of n historical track points of the target, and the static target determination module includes:
respectively determining two adjacent track points between the current moment track point and the nth historical track point of the targetOffset X of displacement between individual track points1、X2、…、XnObtaining the displacement offset sum S = (X)1+ X2+…+ Xn）；
Determining a displacement offset X between the current moment track point and the nth historical track point;
and if S > T X, determining that the target is a static target, wherein T is a preset threshold value.
7. The system of claim 5, wherein for any of the three track point modes, including m historical track points, the target location prediction module comprises:
based on the corresponding prediction parameters of the current track point mode, determining the slope bx and the intercept cx of the constant-speed linear model in the x direction and the slope by and the intercept cy of the constant-speed linear model in the y direction by adopting a least square method,
in the formula, x1、x2、…、xmRespectively showing the positions of the first historical track point, the second historical track point, … and the mth historical track point in the x direction, and y1、y2、…、ymRespectively representing the first historical track pointsThe second historical track point, …, the position of the mth historical track point in the y direction, t1、t2、…、tmRespectively representing the time of the first historical track point, the time of the second historical track point, the time of the … and the time of the mth historical track point;
determining a target position Sx in the x-direction based on the slope bx and the intercept cx, and determining a target position Sy in the y-direction based on the slope by and the intercept cy,
in the formula, t represents the time of the track point at the current time.
8. The system of claim 7, wherein the target range of motion prediction module comprises, for any one of the three track point modes:
determining a maximum movement range dPosX of the target in the x-direction and a maximum movement range dPosY of the target in the y-direction based on the acceleration steering model,
in the formula, a represents the acceleration of the target, turn represents the steering rate of the target, bx and by respectively represent the slope of the constant-speed straight line model in the x direction and the slope of the constant-speed straight line model in the y direction in the current track point mode, and t represents the time of the current track point.
9. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored, the computer program being executable by a processor for implementing the method according to any one of claims 1-4.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115184896A (en) * | 2022-09-09 | 2022-10-14 | 北京海兰信数据科技股份有限公司 | Radar-based target amplitude weighted mean filtering tracking processing method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070164897A1 (en) * | 2006-01-17 | 2007-07-19 | Leskiw Donald M | Single scan track initiation for radars having rotating, electronically scanned antennas |
| CN109523574A (en) * | 2018-12-27 | 2019-03-26 | 联想(北京)有限公司 | A kind of run trace prediction technique and electronic equipment |
| CN111488984A (en) * | 2020-04-03 | 2020-08-04 | 中国科学院计算技术研究所 | Method for training trajectory prediction model and trajectory prediction method |
| CN112285696A (en) * | 2020-12-29 | 2021-01-29 | 北京海兰信数据科技股份有限公司 | Radar target tracking method and system |
| CN113221677A (en) * | 2021-04-26 | 2021-08-06 | 阿波罗智联(北京)科技有限公司 | Track abnormity detection method and device, road side equipment and cloud control platform |
-
2021
- 2021-09-13 CN CN202111065968.9A patent/CN113504527B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070164897A1 (en) * | 2006-01-17 | 2007-07-19 | Leskiw Donald M | Single scan track initiation for radars having rotating, electronically scanned antennas |
| CN109523574A (en) * | 2018-12-27 | 2019-03-26 | 联想(北京)有限公司 | A kind of run trace prediction technique and electronic equipment |
| CN111488984A (en) * | 2020-04-03 | 2020-08-04 | 中国科学院计算技术研究所 | Method for training trajectory prediction model and trajectory prediction method |
| CN112285696A (en) * | 2020-12-29 | 2021-01-29 | 北京海兰信数据科技股份有限公司 | Radar target tracking method and system |
| CN113221677A (en) * | 2021-04-26 | 2021-08-06 | 阿波罗智联(北京)科技有限公司 | Track abnormity detection method and device, road side equipment and cloud control platform |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115184896A (en) * | 2022-09-09 | 2022-10-14 | 北京海兰信数据科技股份有限公司 | Radar-based target amplitude weighted mean filtering tracking processing method and system |
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