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

一种滑翔类目标的轨迹预测管道的生成方法A Trajectory Prediction Pipeline Generation Method for Glide-like Targets

技术领域technical field

本发明属于制导与控制技术领域，尤其涉及一种滑翔类目标的轨迹预测管道的生成方法。The invention belongs to the technical field of guidance and control, and in particular relates to a method for generating a trajectory prediction pipeline of a gliding target.

背景技术Background technique

滑翔类目标具有高空、高速以及随机不确定机动的特点，从而使得非合作滑翔类目标的轨迹预测精度难以得到保障。目前对于非合作滑翔类目标的轨迹预测的方法研究主要集中于两个方面：一是对特定飞行参数的变化规律进行描述，研究特定控制参数对目标轨迹的影响；二是对目标历史状态信息的规律性进行研究，挖掘目标运动轨迹的特征，采用合理的预测算法对目标轨迹进行预测。对于前一种方法，通常是假定目标在预测过程中飞行参数不发生变化或变化较小，通过对气动参数建模，来描述目标飞行过程中气动参数变化规律，进而实现轨迹预测；但当目标的飞行参数变化较大或飞行模式发生变化时，参数变化规律将不能反映目标的实际运动状态，会导致预测误差迅速增大。对于后一种方法，一般是通过对目标跟踪轨迹进行统计分析，提取目标轨迹的变化特征，并根据变化特征进行拟合性预测或统计性预测，以避免目标运动模式不匹配以及参数估计不准确带来的轨迹预测误差，可在一定程度上提高轨迹预测过程的鲁棒性；但统计特征的建立通常以大量的先验样本信息为输入，当针对可获取信息较少的非合作目标进行预测时，往往会特征统计不准确，带来较大的预测误差。The gliding targets have the characteristics of high altitude, high speed and random and uncertain maneuvering, which makes it difficult to guarantee the trajectory prediction accuracy of non-cooperative gliding targets. At present, the research on the trajectory prediction method of non-cooperative gliding targets mainly focuses on two aspects: one is to describe the variation law of specific flight parameters, and to study the influence of specific control parameters on the target trajectory; the other is to analyze the historical state information of the target. The regularity is studied, the characteristics of the target trajectory are excavated, and a reasonable prediction algorithm is used to predict the target trajectory. For the former method, it is usually assumed that the flight parameters of the target do not change or change slightly during the prediction process, and the aerodynamic parameters are modeled to describe the change law of the aerodynamic parameters during the flight of the target, so as to achieve trajectory prediction; When the flight parameters of the target vary greatly or the flight mode changes, the parameter change rule will not reflect the actual motion state of the target, which will lead to a rapid increase in the prediction error. For the latter method, the variation characteristics of the target trajectory are generally extracted by statistical analysis of the target tracking trajectory, and fit prediction or statistical prediction is performed according to the variation characteristics, so as to avoid the mismatch of target motion patterns and inaccurate parameter estimation. The resulting trajectory prediction error can improve the robustness of the trajectory prediction process to a certain extent; however, the establishment of statistical features usually takes a large amount of prior sample information as input, when predicting non-cooperative targets with less available information , the feature statistics are often inaccurate, resulting in large prediction errors.

基于上述分析可知，对目标轨迹进行预测时预测误差不可避免的存在，如何在具有一定预测误差的情况下得到目标运动的不确定范围变得至关重要。但目前关于目标轨迹预测的研究主要集中于预测方法的研究，缺乏对目标可能出现范围的研究。Based on the above analysis, it can be seen that the prediction error inevitably exists when the target trajectory is predicted, and how to obtain the uncertainty range of the target motion under the condition of a certain prediction error becomes very important. However, the current research on target trajectory prediction mainly focuses on the research on the prediction method, and lacks the research on the possible scope of the target.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种滑翔类目标的轨迹预测管道生成方法，可以以一定概率生成滑翔类目标的轨迹分布范围，该轨迹预测分布范围即为轨迹预测管道。The purpose of the present invention is to provide a method for generating a trajectory prediction pipeline of a gliding target, which can generate the trajectory distribution range of the gliding target with a certain probability, and the trajectory prediction distribution range is the trajectory prediction pipeline.

为了实现上述目的，本发明采取如下的技术解决方案：In order to achieve the above object, the present invention adopts the following technical solutions:

一种滑翔类目标的轨迹预测管道的生成方法，包括以下步骤：A method for generating a trajectory prediction pipeline of a gliding target, comprising the following steps:

S1、获取目标的历史跟踪数据，目标的历史跟踪数据包括目标地心距离、经度、纬度、当地速度、当地航迹倾角以及当地航迹偏角；S1. Obtain the historical tracking data of the target. The historical tracking data of the target includes the target geocentric distance, longitude, latitude, local speed, local track inclination and local track declination;

S2、建立目标半再入动力学模型；S2. Establish a target semi-reentry kinetic model;

目标半再入动力学模型为：

Figure BDA0003257557940000021
式中的ri、
Figure BDA0003257557940000022
θi、vi、γi、χi分别表示在第i个跟踪时刻跟踪得到的目标地心距离、经度、纬度、当地速度、当地航迹倾角以及当地航迹偏角，i＝1,2,…,N，N为目标跟踪时刻数量，ρ为目标所在大气环境下的空气密度，βi为目标机动控制量，KDi为阻力质量归一化气动参数，KLi为升力质量归一化气动参数，g为当地重力加速度，
Figure BDA0003257557940000023
分别表示目标地心距离变化量、经度变化量、纬度变化量、当地速度变化量、当地航迹倾角变化量以及当地航迹偏角变化量；The target semi-reentry kinetic model is:
Figure BDA0003257557940000021
ri in the formula,
Figure BDA0003257557940000022
θ i , v i , γ i , and χ i respectively represent the target geocentric distance, longitude, latitude, local speed, local track inclination and local track declination obtained at the ith tracking time, i=1,2 ,…,N, N is the number of target tracking times, ρ is the air density in the atmospheric environment where the target is located, β i is the target maneuver control quantity, K Di is the drag mass normalized aerodynamic parameter, and K Li is the lift mass normalized Aerodynamic parameters, g is the local gravitational acceleration,
Figure BDA0003257557940000023
Represents 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 and the variation of the local track declination;

S3、计算目标特征参数的估计值；S3. Calculate the estimated value of the target feature parameter;

目标特征参数包括目标机动控制量βi、阻力质量归一化气动参数KDi和升力质量归一化气动参数KLi，目标机动控制量的估计值

Figure BDA0003257557940000024
阻力质量归一化气动参数的估计值
Figure BDA0003257557940000025
和升力质量归一化气动参数的估计值
Figure BDA0003257557940000026
分别通过以下公式计算：The target characteristic parameters include the target maneuver control amount β i , the drag mass normalized aerodynamic parameter K Di and the lift mass normalized aerodynamic parameter K Li , and the estimated value of the target maneuver control amount
Figure BDA0003257557940000024
Estimates of drag mass-normalized aerodynamic parameters
Figure BDA0003257557940000025
and lift mass normalized estimates of aerodynamic parameters
Figure BDA0003257557940000026
are calculated by the following formulas:

Figure BDA0003257557940000031
Figure BDA0003257557940000031

Figure BDA0003257557940000032
Figure BDA0003257557940000032

Figure BDA0003257557940000033
Figure BDA0003257557940000033

式中的

Figure BDA0003257557940000034
为当地航迹偏角变化量的估计值，
Figure BDA0003257557940000035
为当地速度变化量的估计值，
Figure BDA0003257557940000036
为地航迹倾角变化量的估计值；in the formula
Figure BDA0003257557940000034
is the estimated value of the local track declination change,
Figure BDA0003257557940000035
is the estimated value of the local velocity change,
Figure BDA0003257557940000036
is the estimated value of the change in the inclination of the ground track;

S4、对目标特征参数进行拟合，计算目标特征参数估计值及估计值的标准差；S4. Fit the target characteristic parameters, and calculate the estimated value of the target characteristic parameter and the standard deviation of the estimated value;

对于目标特征参数中的每一个参数，将估计时刻和该估计时刻所对应的估计值构成数据对，对预测时长内的所有数据对进行拟合，得到拟合公式，3个目标特征参数分别得到3个拟合公式，根据每一个拟合公式，分别计算每一个目标特征参数的拟合值及拟合值的标准差；For each parameter in the target feature parameters, the estimated time and the estimated value corresponding to the estimated time constitute a data pair, and fit all the data pairs within the predicted time period to obtain the fitting formula, and the three target feature parameters are obtained respectively. 3 fitting formulas, according to each fitting formula, respectively calculate the fitting value of each target feature parameter and the standard deviation of the fitting value;

S5、利用目标特征参数的拟合值以及标准差进行目标运动轨迹预测，生成轨迹预测管道，步骤如下：S5. Use the fitting value and standard deviation of the target feature parameters to predict the target motion trajectory, and generate a trajectory prediction pipeline. The steps are as follows:

S5-1、计算各目标特征参数的预测值：S5-1. Calculate the predicted value of each target feature parameter:

目标机动控制量的预测值βj＝fβ(tj)+k*σβ*rand，Predicted value of target maneuver control amount β j =f β (t j )+k*σ β *rand,

阻力质量归一化气动参数的预测值KKDj＝fKD(tj)+k*σKD*rand，The predicted value of the drag mass normalized aerodynamic parameter K KDj =f KD (t j )+k*σ KD *rand,

升力质量归一化气动参数的预测值KKLj＝fKL(tj)+k*σKL*rand，The predicted value of the lift mass normalized aerodynamic parameter K KLj =f KL (t j )+k*σ KL *rand,

上式中的fβ(ti)表示目标机动控制量的拟合函数，fKD(tj)表示阻力质量归一化气动参数KDi的拟合函数，fKL(tj)表示升力质量归一化气动参数KLi的拟合函数，k为误差管道置信度控制系数，σβ、σKD、σKL分别为βi、KDi、KLi拟合值的标准差，rand为均值为0、方差为1的正态分布随机数，j＝1,2,…,M，M为给定的目标轨迹数量；In the above formula, f β (t i ) represents the fitting function of the target maneuvering control quantity, f KD (t j ) represents the fitting function of the drag mass normalized aerodynamic parameter K Di , and f KL (t j ) represents the lift mass The fitting function of the normalized aerodynamic parameter K Li , k is the confidence control coefficient of the error pipeline, σ β , σ KD , and σ KL are the standard deviation of the fitting values of β i , K Di , and K Li respectively, and rand is the mean value 0. A normally distributed random number with a variance of 1, j=1,2,...,M, where M is the number of given target trajectories;

S5-2、基于目标特征参数的预测值进行数值求解生成预测轨迹，形成预测管道。S5-2, perform numerical solution based on the predicted value of the target feature parameter to generate a predicted trajectory, and form a prediction pipeline.

进一步的，步骤S3中，当地速度变化量的估计值

Figure BDA0003257557940000041
当地航迹倾角变化量的估计值
Figure BDA0003257557940000042
当地航迹偏角变化量的估计值
Figure BDA0003257557940000043
基于目标的历史跟踪数据采用微分跟踪法计算。Further, in step S3, the estimated value of the local speed change
Figure BDA0003257557940000041
Estimated value of local track inclination change
Figure BDA0003257557940000042
Estimated value of local track declination change
Figure BDA0003257557940000043
The target-based historical tracking data is calculated using the differential tracking method.

进一步的，步骤S4中，采用最小二乘法对以目标特征参数进行拟合。Further, in step S4, the least squares method is used to fit the target feature parameters.

进一步的，步骤S5-2中，采用龙格-库塔法基于目标特征参数的预测值对目标轨迹进行数值求解，形成轨迹预测管道。Further, in step S5-2, the Runge-Kutta method is used to numerically solve the target trajectory based on the predicted value of the target characteristic parameter, so as to form a trajectory prediction pipeline.

由以上技术方案可知，本发明将目标气动参数建模与建模误差概率统计相结合，针对非合作滑翔类目标再入飞行特点，将目标半再入动力学模型引入轨迹预测过程，通过统计气动参数估计误差，建立气动参数估计误差分布模型，利用误差分布的标准差与分布特点，生成预测轨迹边界，形成预测轨迹管道。本发明将目标机动参数模型与参数估计统计特性引入预测轨迹管道生成，所生成的轨迹预测管道可更准确反映目标轨迹运动特征；同时，通过控制采样过程中的误差分布标准差，可得到不同概率区域的轨迹预测管道，为目标运动区域预测提供了一种具有概率特征的滑翔类目标轨迹预测管道生成方法。It can be seen from the above technical solutions that the present invention combines target aerodynamic parameter modeling with modeling error probability statistics, aiming at the reentry flight characteristics of non-cooperative gliding targets, and introduces the target semi-reentry dynamics model into the trajectory prediction process. Parameter estimation error, establish aerodynamic parameter estimation error distribution model, use the standard deviation and distribution characteristics of the error distribution to generate the predicted trajectory boundary, and form the predicted trajectory pipeline. The invention introduces the target maneuvering parameter model and parameter estimation statistical characteristics into the prediction trajectory pipeline generation, and the generated trajectory prediction pipeline can more accurately reflect the target trajectory movement characteristics; meanwhile, by controlling the standard deviation of the error distribution in the sampling process, different probabilities can be obtained. The regional trajectory prediction pipeline provides a probabilistic feature of the gliding target trajectory prediction pipeline generation method for target movement region prediction.

附图说明Description of drawings

图1为本发明方法的流程图；Fig. 1 is the flow chart of the method of the present invention;

图2a和图2b为目标不进行侧向机动时的轨迹预测管道仿真结果图；Figure 2a and Figure 2b are the simulation results of the trajectory prediction pipeline when the target does not maneuver laterally;

图3a和图3b为目标进行侧向机动时的轨迹预测管道仿真结果图；Fig. 3a and Fig. 3b are the simulation result diagrams of the trajectory prediction pipeline when the target is maneuvering laterally;

图4a和图4b为对标准差进行不同倍数的控制时的轨迹预测管道仿真结果图。Figures 4a and 4b are simulation results of the trajectory prediction pipeline when the standard deviation is controlled by different multiples.

以下结合附图对本发明的具体实施方式作进一步详细地说明。The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下获得的所有其他实施例，都属于本发明保护的范围。The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

下面结合图1，对本发明方法进行说明，如图1所示，本发明的滑翔类目标的轨迹预测管道的生成方法包括以下步骤：The method of the present invention will be described below with reference to FIG. 1. As shown in FIG. 1, the method for generating a trajectory prediction pipeline of a gliding target of the present invention includes the following steps:

S1、获取目标的历史跟踪数据，目标的历史跟踪数据包括目标地心距离、经度、纬度、当地速度、当地航迹倾角以及当地航迹偏角；目标的历史跟踪数据可通过雷达跟踪测量，通过雷达获得目标在标准参考坐标系下的目标位置及速度信息后，就可以转换得到目标的历史跟踪数据

Figure BDA0003257557940000051
其中，ri、
Figure BDA0003257557940000052
θi、vi、γi、χi分别表示在第i个跟踪时刻跟踪得到的目标地心距离、经度、纬度、当地速度、当地航迹倾角以及当地航迹偏角，i＝1,2,…,N，N为目标跟踪时刻数量；S1. Obtain the historical tracking data of the target. The historical tracking data of the target includes the target geocentric distance, longitude, latitude, local speed, local track inclination and local track declination; the historical tracking data of the target can be measured by radar tracking, through After the radar obtains the target position and speed information of the target in the standard reference coordinate system, it can convert the historical tracking data of the target.
Figure BDA0003257557940000051
Among them, r i ,
Figure BDA0003257557940000052
θ i , v i , γ i , and χ i respectively represent the target geocentric distance, longitude, latitude, local speed, local track inclination and local track declination obtained at the ith tracking time, i=1,2 ,...,N, N is the number of target tracking moments;

S2、建立目标半再入动力学模型；S2. Establish a target semi-reentry kinetic model;

目标半再入动力学模型为：

Figure BDA0003257557940000053
式中的ρ为目标所在大气环境下的空气密度，βi为目标机动控制量，KDi为阻力质量归一化气动参数，KLi升力质量归一化气动参数，下标i表示在第i个跟踪时刻的取值，g为当地重力加速度，
Figure BDA0003257557940000054
分别表示目标地心距离变化量、经度变化量、纬度变化量、当地速度变化量、当地航迹倾角变化量以及当地航迹偏角变化量，其中，βi、KDi和KLi构成目标特征参数；The target semi-reentry kinetic model is:
Figure BDA0003257557940000053
In the formula, ρ is the air density in the atmospheric environment where the target is located, β i is the target maneuver control amount, K Di is the normalized aerodynamic parameter of the drag mass, K Li is the normalized aerodynamic parameter of the lift mass, and the subscript i represents the The value of the tracking time, g is the local gravitational acceleration,
Figure BDA0003257557940000054
respectively represent 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 and the variation of the local track declination, among which β i , K Di and K Li constitute the target feature parameter;

S3、计算目标特征参数的估计值；S3. Calculate the estimated value of the target feature parameter;

目标机动控制量的估计值

Figure BDA0003257557940000055
阻力质量归一化气动参数KDi的估计值
Figure BDA0003257557940000056
和升力质量归一化气动参数的估计值
Figure BDA0003257557940000057
分别通过以下公式计算：Estimated value of target maneuver control amount
Figure BDA0003257557940000055
Estimated value of drag mass normalized aerodynamic parameter K Di
Figure BDA0003257557940000056
and lift mass normalized estimates of aerodynamic parameters
Figure BDA0003257557940000057
are calculated by the following formulas:

Figure BDA0003257557940000058
Figure BDA0003257557940000058

Figure BDA0003257557940000059
Figure BDA0003257557940000059

Figure BDA0003257557940000061
Figure BDA0003257557940000061

式中的

Figure BDA0003257557940000062
为当地航迹偏角变化量的估计值，
Figure BDA0003257557940000063
为当地速度变化量的估计值，
Figure BDA0003257557940000064
为地航迹倾角变化量的估计值。这里，当地速度变化量的估计值
Figure BDA0003257557940000065
当地航迹倾角变化量的估计值
Figure BDA0003257557940000066
当地航迹偏角变化量的估计值
Figure BDA0003257557940000067
可基于目标的历史跟踪数据采用微分的方法计算得到，例如，可对目标的历史跟踪数据进行卡尔曼滤波处理后，采用微分跟踪法计算估计值，通过微分的方法计算估计值为现有方法，不是本发明的创新之处，此处不再赘述；in the formula
Figure BDA0003257557940000062
is the estimated value of the local track declination change,
Figure BDA0003257557940000063
is the estimated value of the local velocity change,
Figure BDA0003257557940000064
is the estimated value of the change in the inclination of the ground track. Here, the estimated value of the local velocity change
Figure BDA0003257557940000065
Estimated value of local track inclination change
Figure BDA0003257557940000066
Estimated value of local track declination change
Figure BDA0003257557940000067
It can be calculated by differential method based on the historical tracking data of the target. For example, after Kalman filtering is performed on the historical tracking data of the target, the differential tracking method can be used to calculate the estimated value, and the estimated value calculated by the differential method is the existing method. It is not the innovation of the present invention and will not be repeated here;

S4、对目标特征参数进行拟合，并计算目标特征参数估计值及估计值的标准差；S4. Fit the target characteristic parameters, and calculate the estimated value of the target characteristic parameter and the standard deviation of the estimated value;

对于目标特征参数中的每一个参数，将估计时刻和该时刻所对应的估计值构成数据对，然后对数据对进行拟合，得到拟合公式，目标特征参数有3个，可得到3个拟合公式，然后根据每一个拟合公式，分别计算每一个目标特征参数的拟合值及拟合值的标准差；For each parameter in the target feature parameters, the estimated time and the estimated value corresponding to the time are formed into a data pair, and then the data pair is fitted to obtain a fitting formula. There are 3 target feature parameters, and 3 fittings can be obtained. Then, according to each fitting formula, calculate the fitted value of each target feature parameter and the standard deviation of the fitted value;

以目标特征参数中的目标机动控制量βi为例，将估计时刻ti和与该估计时刻对应的目标机动控制量的估计值

Figure BDA0003257557940000068
构成数据对
Figure BDA0003257557940000069
采用最小二乘法进行拟合，得到最小二乘拟合公式：
Figure BDA00032575579400000610
式中的ti表示估计时刻，fβ(ti)表示目标机动控制量βi的拟合函数，根据拟合公式计算目标机动控制量的拟合值，并进一步计算拟合值的标准差，标准差
Figure BDA00032575579400000611
Taking the target maneuver control amount β i in the target characteristic parameter as an example, the estimated time t i and the estimated value of the target maneuver control amount corresponding to the estimated time
Figure BDA0003257557940000068
make up data pairs
Figure BDA0003257557940000069
The least squares method is used for fitting, and the least squares fitting formula is obtained:
Figure BDA00032575579400000610
In the formula, t i represents the estimated time, and f β (t i ) represents the fitting function of the target maneuvering control amount β i . According to the fitting formula, the fitting value of the target maneuvering control amount is calculated, and the standard deviation of the fitting value is further calculated. , the standard deviation
Figure BDA00032575579400000611

对于其余的目标特征参数KDi和KLi也采用相同的方法进行处理，得到拟合公式和拟合值的标准差；除了可以采用最小二乘法进行拟合外，也可以采用其他的拟合法进行拟合，拟合方法是常规的技术手段，不是本发明的创新点，此处不作一一赘述；The rest of the target characteristic parameters K Di and K Li are also processed in the same way to obtain the fitting formula and the standard deviation of the fitted values; in addition to the least squares method for fitting, other fitting methods can also be used. Fitting, the fitting method is a conventional technical means, not the innovation of the present invention, and will not be repeated here.

S5、利用拟合的目标特征参数以及标准差进行目标运动轨迹预测，生成轨迹预测管道，步骤如下：S5. Use the fitted target feature parameters and standard deviation to predict the target motion trajectory, and generate a trajectory prediction pipeline. The steps are as follows:

S5-1、计算各目标特征参数的预测值：S5-1. Calculate the predicted value of each target feature parameter:

目标机动控制量的预测值βj＝fβ(tj)+k*σβ*rand，Predicted value of target maneuver control amount β j =f β (t j )+k*σ β *rand,

阻力质量归一化气动参数的预测值KKDj＝fKD(tj)+k*σKD*rand，The predicted value of the drag mass normalized aerodynamic parameter K KDj =f KD (t j )+k*σ KD *rand,

升力质量归一化气动参数的预测值KKLj＝fKL(tj)+k*σKL*rand，The predicted value of the lift mass normalized aerodynamic parameter K KLj =f KL (t j )+k*σ KL *rand,

上式中的fβ(ti)表示目标机动控制量的拟合函数，fKD(tj)表示阻力质量归一化气动参数KDi的拟合函数，fKL(tj)表示升力质量归一化气动参数KLi的拟合函数，k为误差管道置信度控制系数，σβ、σKD、σKL分别为βi、KDi、KLi拟合值的标准差，rand为均值为0、方差为1的正态分布随机数，j＝1,2,…,M，M为给定的目标轨迹数量；通过k可以控制参数估计的概率为标准差的多少倍，进而控制轨迹预测管道的概率；In the above formula, f β (t i ) represents the fitting function of the target maneuvering control quantity, f KD (t j ) represents the fitting function of the drag mass normalized aerodynamic parameter K Di , and f KL (t j ) represents the lift mass The fitting function of the normalized aerodynamic parameter K Li , k is the confidence control coefficient of the error pipeline, σ β , σ KD , and σ KL are the standard deviation of the fitting values of β i , K Di , and K Li respectively, and rand is the mean value 0. A normally distributed random number with a variance of 1, j=1,2,...,M, where M is the number of given target trajectories; k can control how many times the probability of parameter estimation is the standard deviation, and then control the trajectory prediction the probability of the pipeline;

S5-2、基于目标特征参数的预测值进行数值求解生成预测轨迹，形成预测管道。对于数值求解中的数值积分，可采用经典的欧拉法、龙格-库塔法等数值计算方法进行求解。本实施例采用4阶标准龙格-库塔法基于目标特征参数的预测值对目标轨迹进行数值求解，得到M条预测轨迹，形成轨迹预测管道。S5-2, perform numerical solution based on the predicted value of the target feature parameter to generate a predicted trajectory, and form a prediction pipeline. For the numerical integration in the numerical solution, the classical Euler method, Runge-Kutta method and other numerical calculation methods can be used to solve the problem. In this embodiment, the fourth-order standard Runge-Kutta method is used to numerically solve the target trajectory based on the predicted value of the target characteristic parameter, and M predicted trajectories are obtained, forming a trajectory prediction pipeline.

为了验证本发明方法的预测性能，使用MATLAB软件针对某一滑翔目标分别进行侧向机动与不进行侧向机动的条件进行仿真。在仿真过程中，目标从70km高度再入，分别按照具有一定侧向机动与不具有侧向机动两种模式飞行生成目标运动轨迹。In order to verify the prediction performance of the method of the present invention, MATLAB software is used to simulate the conditions of lateral maneuvering and no lateral maneuvering for a gliding target respectively. In the simulation process, the target re-entered from a height of 70km, and the target trajectory was generated according to two modes of flight with certain lateral maneuvering and without lateral maneuvering.

同时，仿真时3个目标特征参数所选用的拟合函数分别为：At the same time, the fitting functions selected for the three target feature parameters during simulation are:

fβ(ti)＝β2τ2+β1τ1+β0，f β (t i )=β 2 τ 2 +β 1 τ 1 +β 0 ,

fKD(tj)＝KD2τ2+KD1τ1+KD0，f KD (t j )=K D2 τ 2 +K D1 τ 1 +K D0 ,

fKL(tj)＝KL2τ2+KL1τ1+KL0，f KL (t j )=K L2 τ 2 +K L1 τ 1 +K L0 ,

仿真过程中，首先采用UKF滤波算法对目标进行轨迹跟踪，得到目标的历史跟踪数据；然后进行轨迹预测，预测过程中，选择误差管道置信度控制系数k＝1，进行1倍标准差的预测，预测时长为200s，并利用50次蒙特卡洛仿真生成50条预测轨迹构成轨迹管道。In the simulation process, the UKF filtering algorithm is used to track the target's trajectory first, and the historical tracking data of the target is obtained; then trajectory prediction is performed. The prediction time is 200s, and 50 predicted trajectories are generated by 50 Monte Carlo simulations to form a trajectory pipeline.

图2a和图2b为目标不进行侧向机动时的轨迹预测管道仿真结果，图2a为纵向轨迹预测管道，图2b为横向轨迹预测管道。图3a和图3b为目标进行侧向机动时的轨迹预测管道仿真结果，图3a为纵向轨迹预测管道，图3b为横向轨迹预测管道。图2a、2b、3a、3b中的点a为对目标跟踪的起点，点b目标轨迹预测起点，实线曲线为目标轨迹跟踪曲线，虚线曲线为轨迹预测管道边界曲线，曲线s为目标实际轨迹参考曲线，黑色点为轨迹预测管道覆盖范围。从图2a至图3b可以看出，本发明方法生成的轨迹预测管道能够较好的覆盖目标运动区域，同时覆盖范围相对较小，预测轨迹管道边界稳定且能够反映目标运动特征。Figures 2a and 2b are the simulation results of the trajectory prediction pipeline when the target does not maneuver laterally, Figure 2a is the longitudinal trajectory prediction pipeline, and Figure 2b is the lateral trajectory prediction pipeline. Figures 3a and 3b are the simulation results of the trajectory prediction pipeline when the target is maneuvering laterally, Figure 3a is the longitudinal trajectory prediction pipeline, and Figure 3b is the lateral trajectory prediction pipeline. Point a in Figures 2a, 2b, 3a, and 3b is the starting point of target tracking, point b is the starting point of target trajectory prediction, the solid line curve is the target trajectory tracking curve, the dotted line curve is the trajectory prediction pipeline boundary curve, and the curve s is the actual target trajectory. Referring to the curve, the black points are the trajectory prediction pipeline coverage. It can be seen from Figures 2a to 3b that the trajectory prediction pipeline generated by the method of the present invention can better cover the target movement area, while the coverage is relatively small, and the boundary of the prediction trajectory pipeline is stable and can reflect the target movement characteristics.

图4a和图4b为对标准差进行不同倍数的控制时的轨迹预测管道仿真结果。从图4a和图4b可以看出来，当选择不同倍数的标准差对预测进行控制时，预测区域不同，倍数越高预测区域越大，包含目标未来运动区域的概率越大，但预测区域越大将带来目标的不确定性变大。Figure 4a and Figure 4b are the simulation results of the trajectory prediction pipeline when the standard deviation is controlled by different multiples. It can be seen from Figure 4a and Figure 4b that when different multiples of standard deviation are selected to control the prediction, the prediction area is different. The higher the multiple, the larger the prediction area and the greater the probability of including the target future motion area. Uncertainty about goals increases.

以上所述，仅是本发明的较佳实施例而已，并非对本发明做任何形式上的限制，虽然本发明已以较佳实施例揭露如上，然而并非用以限定本发明，任何熟悉本专业的技术人员，在不脱离本发明技术方案范围内，当可利用上述揭示的技术内容做出些许更动或修饰为等同变化的等效实施例，但凡是未脱离本发明技术方案的内容，依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化与修饰，均仍属于本发明技术方案的范围内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Technical personnel, within the scope of the technical solution of the present invention, can make some changes or modifications to equivalent examples of equivalent changes by using the technical content disclosed above, but any content that does not depart from the technical solution of the present invention, according to the present invention. The technical essence of the invention Any simple modifications, equivalent changes and modifications made to the above embodiments still fall within the scope of the technical solutions of the present invention.

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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