CN113761662B - Generation method of trajectory prediction pipeline of gliding target - Google Patents

Generation method of trajectory prediction pipeline of gliding target Download PDF

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CN113761662B
CN113761662B CN202111063725.1A CN202111063725A CN113761662B CN 113761662 B CN113761662 B CN 113761662B CN 202111063725 A CN202111063725 A CN 202111063725A CN 113761662 B CN113761662 B CN 113761662B
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邵雷
赵锦
雷虎民
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Abstract

A generation method of a trajectory prediction pipeline of a gliding target comprises the following steps: acquiring historical tracking data of a target; establishing a target semi-reentry dynamic model; calculating an estimated value of target characteristic parameters, wherein the target characteristic parameters comprise a target maneuvering control quantity and a quality normalization pneumatic parameter; fitting the target characteristic parameters, and calculating an estimated value of the target characteristic parameters and a standard deviation of the estimated value; and predicting the target motion track by using the fitting value and the standard deviation of the target characteristic parameter, and generating a track prediction pipeline based on the predicted value of the target characteristic parameter. According to the method, a target maneuvering parameter model and parameter estimation statistical characteristics are introduced into a track prediction pipeline for generation, and the generated track prediction pipeline can reflect target track motion characteristics more accurately; and the trajectory prediction pipelines of different probability areas can be obtained by controlling the error distribution standard deviation in the sampling process, so that a new method with probability characteristic prediction is provided for target motion area prediction.

Description

Generation method of trajectory prediction pipeline of gliding targets
Technical Field
The invention belongs to the technical field of guidance and control, and particularly relates to a generation method of a trajectory prediction pipeline of a gliding target.
Background
The gliding target has the characteristics of high altitude, high speed and random uncertain maneuvering, so that the track prediction precision of the non-cooperative gliding target is difficult to guarantee. The current method for predicting the track of the non-cooperative gliding target mainly focuses on two aspects: firstly, describing the change rule of a specific flight parameter, and researching the influence of a specific control parameter on a target track; and secondly, researching the regularity of the target historical state information, mining the characteristics of the target motion track, and predicting the target track by adopting a reasonable prediction algorithm. For the former method, the change rule of the pneumatic parameters in the target flight process is described by modeling the pneumatic parameters on the assumption that the flight parameters of the target do not change or change slightly in the prediction process, so as to realize the track prediction; however, when the flight parameters of the target change greatly or the flight mode changes, the actual motion state of the target cannot be reflected by the parameter change rule, and the prediction error is increased rapidly. For the latter method, the change characteristics of the target track are generally extracted by carrying out statistical analysis on the target tracking track, and fitting prediction or statistical prediction is carried out according to the change characteristics, so that track prediction errors caused by mismatching of target motion modes and inaccurate parameter estimation are avoided, and the robustness of the track prediction process can be improved to a certain extent; however, the establishment of statistical characteristics usually takes a large amount of prior sample information as input, and when the prediction is performed on a non-cooperative target with less available information, the characteristic statistics is often inaccurate, which brings a large prediction error.
Based on the above analysis, it is known that when a target trajectory is predicted, a prediction error inevitably exists, and how to obtain an uncertain range of target motion with a certain prediction error becomes important. However, current research on target trajectory prediction mainly focuses on research on prediction methods, and lacks research on the possible emergence range of targets.
Disclosure of Invention
The invention aims to provide a method for generating a trajectory prediction pipeline of a gliding target, which can generate a trajectory distribution range of the gliding target at a certain probability, wherein the trajectory prediction distribution range is the trajectory prediction pipeline.
In order to achieve the purpose, the invention adopts the following technical solutions:
a generation method of a trajectory prediction pipeline of a gliding target comprises the following steps:
s1, obtaining historical tracking data of a target, wherein the historical tracking data of the target comprises a geocentric distance, longitude, latitude, local speed, a local track inclination angle and a local track deflection angle of the target;
s2, establishing a target semi-reentry dynamic model;
the target semi-reentry kinetic model is:
Figure BDA0003257557940000021
in the formula r i 、
Figure BDA0003257557940000022
θ i 、v i 、γ i 、χ i Respectively representing the geocentric distance, longitude, latitude, local speed, local track inclination angle and local track deflection angle of the target tracked at the ith tracking moment, i =1,2, \8230;, N, N are the number of the target tracking moments, p is the air density of the atmospheric environment where the target is positioned, and beta i For the target maneuvering control quantity, K Di Normalization of the pneumatic parameters for the resistance mass, K Li The lift mass normalized aerodynamic parameters, g the local gravitational acceleration,
Figure BDA0003257557940000023
respectively representing the variation of the target geocentric distance, the variation of the longitude, the variation of the latitude, the variation of the local speed, the variation of the local track inclination angle and the variation of the local track deflection angle;
s3, calculating an estimated value of the target characteristic parameter;
the target characteristic parameter includes a target maneuvering control amount beta i Resistance mass normalization pneumatic parameter K Di Normalized aerodynamic parameter K with lift mass Li Estimation of target maneuver control quantity
Figure BDA0003257557940000024
Estimation value of resistance mass normalization pneumatic parameter
Figure BDA0003257557940000025
Normalization of estimated values of aerodynamic parameters with lift quality
Figure BDA0003257557940000026
Calculated by the following formulas, respectively:
Figure BDA0003257557940000031
Figure BDA0003257557940000032
Figure BDA0003257557940000033
in the formula
Figure BDA0003257557940000034
Is an estimate of the local track yaw variation,
Figure BDA0003257557940000035
is an estimate of the amount of change in local speed,
Figure BDA0003257557940000036
is an estimated value of the variation of the inclination angle of the ground track;
s4, fitting the target characteristic parameters, and calculating an estimated value of the target characteristic parameters and a standard deviation of the estimated value;
for each parameter in the target characteristic parameters, forming a data pair by the estimation time and an estimation value corresponding to the estimation time, fitting all data pairs in the prediction duration to obtain a fitting formula, respectively obtaining 3 fitting formulas by 3 target characteristic parameters, and respectively calculating a fitting value of each target characteristic parameter and a standard deviation of the fitting value according to each fitting formula;
s5, predicting the target motion track by using the fitting value and the standard deviation of the target characteristic parameter to generate a track prediction pipeline, wherein the steps are as follows:
s5-1, calculating a predicted value of each target characteristic parameter:
predicted value beta of target maneuvering control quantity j ＝f β (t j )+k*σ β *rand，
Predicted value K of resistance mass normalization pneumatic parameter KDj ＝f KD (t j )+k*σ KD *rand，
Predicted value K of lift quality normalization aerodynamic parameter KLj ＝f KL (t j )+k*σ KL *rand，
The upper typeF in (1) β (t i ) Fitting function representing target maneuvering control quantity, f KD (t j ) Normalization of the aerodynamic parameter K by means of a representation of the resistance mass Di Fitting function of f KL (t j ) Normalized aerodynamic parameter K representing lift mass Li K is the error pipeline confidence coefficient control coefficient, σ β 、σ KD 、σ KL Are each beta i 、K Di 、K Li The standard deviation of the fitting values, rand is a normally distributed random number with a mean value of 0 and a variance of 1, j =1,2, \ 8230, M, M is the given number of target tracks;
and S5-2, carrying out numerical solution on the predicted value based on the target characteristic parameter to generate a predicted track, and forming a predicted pipeline.
Further, in step S3, the estimated value of the local speed variation
Figure BDA0003257557940000041
Estimation of local track inclination variation
Figure BDA0003257557940000042
Estimation value of local track deviation angle variation
Figure BDA0003257557940000043
And calculating the historical tracking data based on the target by adopting a differential tracking method.
Further, in step S4, a least square method is used to fit the target characteristic parameters.
Further, in the step S5-2, a Runge-Kutta method is adopted to carry out numerical solution on the target track based on the predicted value of the target characteristic parameter, and a track prediction pipeline is formed.
According to the technical scheme, the target pneumatic parameter modeling and the modeling error probability statistics are combined, the target semi-reentry dynamic model is introduced into the track prediction process according to the reentry flight characteristic of the non-cooperative gliding target, the pneumatic parameter estimation error is counted, the pneumatic parameter estimation error distribution model is established, the standard deviation and the distribution characteristic of the error distribution are utilized, the predicted track boundary is generated, and the predicted track pipeline is formed. According to the method, the target maneuvering parameter model and the parameter estimation statistical characteristics are introduced into the predicted track pipeline for generation, and the generated track predicted pipeline can reflect the target track motion characteristics more accurately; meanwhile, by controlling the error distribution standard deviation in the sampling process, the trajectory prediction pipelines of different probability regions can be obtained, and a gliding target trajectory prediction pipeline generation method with probability characteristics is provided for target motion region prediction.
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FIG. 1 is a flow chart of the method of the present invention;
FIGS. 2a and 2b are graphs of results of trajectory-predicted pipeline simulations when the target is not undergoing lateral maneuvers;
FIGS. 3a and 3b are graphs of the results of a trajectory-predicted pipeline simulation when a target is laterally maneuvered;
fig. 4a and 4b are graphs of the simulation results of the trajectory prediction pipeline when the standard deviation is controlled by different multiples.
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Detailed Description
The technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying 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.
The method of the present invention will be described with reference to fig. 1, and as shown in fig. 1, the method of generating a trajectory prediction pipeline of a gliding object of the present invention includes the steps of:
s1, obtaining historical tracking data of a target, wherein the historical tracking data of the target comprises a geocentric distance, longitude, latitude, local speed, a local track inclination angle and a local track deflection angle of the target; the historical tracking data of the target can be tracked and measured by a radar, and after the target position and speed information of the target in a standard reference coordinate system are obtained by the radar, the target position and speed information can be obtainedHistorical tracking data of the target can be obtained through conversion
Figure BDA0003257557940000051
Wherein r is i 、
Figure BDA0003257557940000052
θ i 、v i 、γ i 、χ i Respectively representing the geocentric distance, longitude, latitude, local speed, local track inclination and local track declination of the target tracked at the ith tracking moment, wherein i =1,2, \ 8230, and N are the number of target tracking moments;
s2, establishing a target semi-reentry dynamic model;
the target semi-reentry kinetic model is:
Figure BDA0003257557940000053
where ρ is the air density in the atmosphere of the target, β i To a target maneuver controlled quantity, K Di Normalizing the pneumatic parameter, K, for the resistance mass Li The lift mass normalizes the aerodynamic parameters, subscript i indicates the value at the ith tracking moment, g is the local gravitational acceleration,
Figure BDA0003257557940000054
respectively representing the variation of the target geocentric distance, the variation of the longitude, the variation of the latitude, the variation of the local speed, the variation of the local track inclination angle and the variation of the local track deflection angle, wherein beta i 、K Di And K Li Forming target characteristic parameters;
s3, calculating an estimated value of the target characteristic parameter;
estimation value of target maneuvering control quantity
Figure BDA0003257557940000055
Resistance mass normalization pneumatic parameter K Di Is estimated value of
Figure BDA0003257557940000056
Estimation of lift mass normalized aerodynamic parametersEvaluation of values
Figure BDA0003257557940000057
Respectively calculated by the following formulas:
Figure BDA0003257557940000058
Figure BDA0003257557940000059
Figure BDA0003257557940000061
in the formula
Figure BDA0003257557940000062
Is an estimate of the local track yaw variation,
Figure BDA0003257557940000063
is an estimate of the amount of change in local speed,
Figure BDA0003257557940000064
is an estimate of the change in the inclination of the ground track. Here, the estimated value of the amount of change in local speed
Figure BDA0003257557940000065
Estimation of local track inclination variation
Figure BDA0003257557940000066
Estimation value of local track deviation angle variation
Figure BDA0003257557940000067
The estimation value can be calculated by adopting a differential method based on the historical tracking data of the target, for example, after Kalman filtering processing is carried out on the historical tracking data of the target, the estimation value can be calculated by adopting the differential tracking method, and the estimation value is calculated to be the existing estimation value by adopting the differential methodThe method is not the innovation of the invention, and is not described herein again;
s4, fitting the target characteristic parameters, and calculating an estimated value of the target characteristic parameters and a standard deviation of the estimated value;
for each parameter in the target characteristic parameters, forming a data pair by the estimation time and the estimation value corresponding to the time, fitting the data pair to obtain a fitting formula, wherein the number of the target characteristic parameters is 3, and 3 fitting formulas can be obtained;
with a target maneuvering control quantity beta in a target characteristic parameter i For example, time t will be estimated i And an estimated value of the target maneuvering control amount corresponding to the estimated time
Figure BDA0003257557940000068
Form data pairs
Figure BDA0003257557940000069
Fitting by using a least square method to obtain a least square fitting formula:
Figure BDA00032575579400000610
t in the formula i Representing the estimated time, f β (t i ) Indicating the target maneuver control quantity β i According to the fitting formula, calculating the fitting value of the target maneuvering control quantity, and further calculating the standard deviation of the fitting value
Figure BDA00032575579400000611
For the remaining target characteristic parameters K Di And K Li Processing by the same method to obtain a fitting formula and a standard deviation of a fitting value; except for fitting by adopting a least square method, fitting by adopting other fitting methods can also be carried out, and the fitting method is a conventional technical means and is not an innovation point of the invention, and is not described in detail herein;
s5, predicting the target motion track by using the fitted target characteristic parameters and the standard deviation to generate a track prediction pipeline, wherein the steps are as follows:
s5-1, calculating a predicted value of each target characteristic parameter:
predicted value beta of target maneuvering control quantity j ＝f β (t j )+k*σ β *rand，
Predicted value K of resistance mass normalization pneumatic parameter KDj ＝f KD (t j )+k*σ KD *rand，
Predicted value K of lift mass normalization aerodynamic parameter KLj ＝f KL (t j )+k*σ KL *rand，
In the above formula f β (t i ) Fitting function representing target maneuvering control quantity, f KD (t j ) Normalization of the aerodynamic parameter K by means of a representation of the resistance mass Di Fitting function of (f) KL (t j ) Normalized aerodynamic parameter K representing lift mass Li K is the error pipeline confidence coefficient control coefficient, sigma β 、σ KD 、σ KL Are each beta i 、K Di 、K Li The standard deviation of the fitting values, rand is a normally distributed random number with a mean value of 0 and a variance of 1, j =1,2, \ 8230, M, M is the given number of target tracks; the probability of parameter estimation is controlled to be multiple times of the standard deviation through k, and then the probability of the track prediction pipeline is controlled;
and S5-2, carrying out numerical solution on the predicted value based on the target characteristic parameter to generate a predicted track, and forming a predicted pipeline. For the numerical integration in the numerical solution, a numerical calculation method such as a classical euler method, a Runge-Kutta method and the like can be adopted for the solution. In this embodiment, a 4-order standard longge-kutta method is adopted to numerically solve the target trajectory based on the predicted value of the target characteristic parameter, so as to obtain M predicted trajectories, and form a trajectory prediction pipeline.
In order to verify the prediction performance of the method, MATLAB software is used for carrying out simulation on the conditions of lateral maneuver and non-lateral maneuver aiming at a certain gliding target. In the simulation process, the target enters from the height of 70km again, and the target motion trail is generated by flying in two modes of having a certain lateral maneuver and not having the lateral maneuver.
Meanwhile, during simulation, fitting functions selected by the 3 target characteristic parameters are respectively as follows:
f β (t i )＝β 2 τ 2 +β 1 τ 1 +β 0 ，
f KD (t j )＝K D2 τ 2 +K D1 τ 1 +K D0 ，
f KL (t j )＝K L2 τ 2 +K L1 τ 1 +K L0 ，
in the simulation process, firstly, a UKF filtering algorithm is adopted to track a target to obtain historical tracking data of the target; and then, performing track prediction, selecting an error pipeline confidence coefficient control coefficient k =1 in the prediction process, performing prediction of 1 time of standard deviation, wherein the prediction time is 200s, and generating 50 predicted tracks by utilizing 50 Monte Carlo simulations to form a track pipeline.
Fig. 2a and 2b are simulation results of a trajectory prediction pipeline when a target does not perform lateral maneuver, fig. 2a is a longitudinal trajectory prediction pipeline, and fig. 2b is a transverse trajectory prediction pipeline. Fig. 3a and 3b are simulation results of a trajectory prediction pipeline when a target performs lateral maneuver, fig. 3a is a longitudinal trajectory prediction pipeline, and fig. 3b is a transverse trajectory prediction pipeline. In fig. 2a, 2b, 3a, and 3b, point a is a starting point for target tracking, point b is a target track prediction starting point, a solid line curve is a target track tracking curve, a dotted line curve is a track prediction pipeline boundary curve, a curve s is a target actual track reference curve, and a black dot is a track prediction pipeline coverage area. As can be seen from fig. 2a to fig. 3b, the trajectory prediction pipeline generated by the method of the present invention can better cover the target motion area, and has a relatively small coverage area, and the predicted trajectory pipeline has a stable boundary and can reflect the target motion characteristics.
Fig. 4a and 4b are simulation results of the trajectory prediction pipeline when the standard deviation is controlled by different multiples. As can be seen from fig. 4a and 4b, when the standard deviation of different multiples is selected to control the prediction, the prediction regions are different, and the higher the multiple is, the larger the prediction region is, the higher the probability that the future motion region of the target is included is, but the larger the prediction region is, the larger the uncertainty of the target becomes.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A generation method of a trajectory prediction pipeline of a gliding target is characterized by comprising the following steps:
s1, obtaining historical tracking data of a target, wherein the historical tracking data of the target comprises a geocentric distance, longitude, latitude, local speed, a local track inclination angle and a local track deflection angle of the target;
s2, establishing a target semi-reentry dynamic model;
the target semi-reentry kinetic model is:
Figure FDA0003257557930000011
in the formula r i 、
Figure FDA0003257557930000012
θ i 、v i 、γ i 、χ i Respectively representing the geocentric distance, longitude, latitude, local speed, local track inclination angle and local track deflection angle of the target tracked at the ith tracking moment, i =1,2, \ 8230;, wherein N, N are the number of the target tracking moments, rho is the air density of the atmospheric environment where the target is located, and beta is the air density of the atmospheric environment where the target is located i For the target maneuvering control quantity, K Di The pneumatic parameters are normalized for the resistance mass,K Li normalization of aerodynamic parameters for lift mass, g local gravitational acceleration,
Figure FDA0003257557930000013
respectively representing the variation of the target geocentric distance, the variation of the longitude, the variation of the latitude, the variation of the local speed, the variation of the local track inclination angle and the variation of the local track deflection angle;
s3, calculating an estimated value of the target characteristic parameter;
the target characteristic parameter includes a target maneuvering control amount beta i Resistance mass normalization pneumatic parameter K Di Normalization of aerodynamic parameter K with lift mass Li Estimated value of target maneuvering control quantity
Figure FDA0003257557930000014
Estimation value of resistance quality normalization pneumatic parameter
Figure FDA0003257557930000015
Normalization of estimated values of aerodynamic parameters with lift quality
Figure FDA0003257557930000016
Calculated by the following formulas, respectively:
Figure FDA0003257557930000017
Figure FDA0003257557930000021
Figure FDA0003257557930000022
in the formula
Figure FDA0003257557930000023
Is an estimate of the local track yaw variation,
Figure FDA0003257557930000024
is an estimate of the amount of change in local speed,
Figure FDA0003257557930000025
an estimated value of the inclination angle variation of the ground track;
s4, fitting the target characteristic parameters, and calculating an estimated value of the target characteristic parameters and a standard deviation of the estimated value;
for each parameter in the target characteristic parameters, forming a data pair by the estimation time and an estimation value corresponding to the estimation time, fitting all data pairs in the prediction duration to obtain a fitting formula, respectively obtaining 3 fitting formulas by 3 target characteristic parameters, and respectively calculating a fitting value of each target characteristic parameter and a standard deviation of the fitting value according to each fitting formula;
s5, predicting the target motion track by using the fitting value and the standard deviation of the target characteristic parameter to generate a track prediction pipeline, wherein the steps are as follows:
s5-1, calculating the predicted value of each target characteristic parameter:
predicted value beta of target maneuvering control quantity j ＝f β (t j )+k*σ β *rand，
Predicted value K of resistance mass normalization pneumatic parameter KDj ＝f KD (t j )+k*σ KD *rand，
Predicted value K of lift quality normalization aerodynamic parameter KLj ＝f KL (t j )+k*σ KL *rand，
F in the above formula β (t i ) Fitting function representing target maneuvering control quantity, f KD (t j ) Normalization of the aerodynamic parameter K by means of a representation of the resistance mass Di Fitting function of f KL (t j ) Normalized aerodynamic parameter K representing lift mass Li K is the error pipeline confidence coefficient control coefficient, sigma β 、σ KD 、σ KL Are each beta i 、K Di 、K Li The standard deviation of the fitting values, rand is a normally distributed random number with a mean value of 0 and a variance of 1, j =1,2, \ 8230, M, M is the given number of target tracks;
and S5-2, carrying out numerical solution on the predicted value based on the target characteristic parameter to generate a predicted track, and forming a predicted pipeline.
2. The method for generating a trajectory prediction pipeline for a gliding object according to claim 1, wherein: in step S3, the estimated value of the local speed variation
Figure FDA0003257557930000026
Estimation of local track inclination variation
Figure FDA0003257557930000027
Estimation value of local track deviation angle variation
Figure FDA0003257557930000028
And calculating the historical tracking data based on the target by adopting a differential tracking method.
3. The method for generating a trajectory prediction pipeline for a gliding object according to claim 1, wherein: in step S4, a least square method is adopted to fit the target characteristic parameters.
4. The method for generating a trajectory prediction pipeline for a gliding target according to claim 1, wherein: in the step S5-2, a Runge-Kutta method is adopted to carry out numerical solution on the target track based on the predicted value of the target characteristic parameter, and a track prediction pipeline is formed.
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