CN113095504A - Target track prediction system and prediction method - Google Patents

Target track prediction system and prediction method Download PDF

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CN113095504A
CN113095504A CN202110394691.8A CN202110394691A CN113095504A CN 113095504 A CN113095504 A CN 113095504A CN 202110394691 A CN202110394691 A CN 202110394691A CN 113095504 A CN113095504 A CN 113095504A
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邵雷
赵锦
雷虎民
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Air Force Engineering University of PLA
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Abstract

一种目标轨迹预测系统,包括目标机动特征模型集模块,目标机动特征集中包括若干目标的典型机动局部模型;前向轨迹预测模块,定义性能指标函数并建立目标轨迹预测模型,根据各典型机动局部模型与目标实际飞行轨迹的匹配度确定目标轨迹预测模型的参数,采用数值积分法进行前向轨迹预测;后向反馈修正模块,建立全局重要性地图模型,根据全局重要性地图模型建立前向预测轨迹与飞行重要节点的关联关系,确定目标机动意图信息,采用后验条件概率法对目标轨迹预测模型的参数进行修正后,重新进行轨迹预测。本发明突破了传统轨迹预测方法开环预测局限性,将基于博弈对抗的预测轨迹反馈修正融入轨迹预测过程,实现轨迹闭环预测,提高了轨迹预测精度。

Figure 202110394691

A target trajectory prediction system includes a target maneuvering feature model set module, the target maneuvering feature set includes typical maneuvering local models of several targets; the forward trajectory prediction module defines a performance index function and establishes a target trajectory prediction model, according to each typical maneuvering local model. The matching degree between the model and the actual flight trajectory of the target determines the parameters of the target trajectory prediction model, and the numerical integration method is used to predict the forward trajectory; the backward feedback correction module establishes a global importance map model, and establishes a forward prediction according to the global importance map model. The relationship between the trajectory and the important flight nodes is determined, and the target maneuvering intention information is determined. After the parameters of the target trajectory prediction model are modified by the posterior conditional probability method, the trajectory prediction is performed again. The invention breaks through the open-loop prediction limitation of the traditional trajectory prediction method, integrates the prediction trajectory feedback correction based on game confrontation into the trajectory prediction process, realizes the trajectory closed-loop prediction, and improves the trajectory prediction accuracy.

Figure 202110394691

Description

一种目标轨迹预测系统及预测方法A target trajectory prediction system and prediction method

技术领域technical field

本发明属于目标轨迹预测技术领域,尤其涉及一种飞行目标的轨迹预测系统及预测方法。The invention belongs to the technical field of target trajectory prediction, and in particular relates to a trajectory prediction system and a prediction method of a flying target.

背景技术Background technique

目标轨迹预测是一种根据探测系统获取的目标运动状态参数来估计目标未来运动状态或者可达范围的过程,其实质就是基于已知历史信息,依照一定的方法和规律对未来信息进行估计。通过有效的轨迹预测,可以提高对目标后续状态的了解,为相关任务规划与管理提供依据,在航空管制、空天目标拦截等领域具有广阔的应用前景。Target trajectory prediction is a process of estimating the future motion state or reachable range of the target according to the target motion state parameters obtained by the detection system. Through effective trajectory prediction, the understanding of the subsequent state of the target can be improved, and the basis for related mission planning and management can be provided. It has broad application prospects in the fields of aviation control and aerospace target interception.

目前轨迹预测方法主要分为解析法和数值积分法两大类。解析法是一种较为直观的轨迹预测方法,其主要原理是以特定运动模式或运动关系为条件进行简化,但当目标运动模式复杂多变时,往往很难获得解析解,如果是针对运动模式未知的非合作目标进行预测,该方法则更难以适应。数值积分法是通过对动力学模型前向积分来获得目标预测轨迹,同样的,当目标运动模式发生变化时,尤其是对于机动模式未知的非合作目标进行轨迹预测时,由于数值积分法的预测过程是开环过程,没有外来信息对预测轨迹进行修正,会导致预测过程中的误差不断积累,快速发散,难以满足非合作目标实际轨迹预测需求。At present, trajectory prediction methods are mainly divided into two categories: analytical method and numerical integration method. Analytical method is a relatively intuitive trajectory prediction method. Its main principle is to simplify on the condition of a specific motion pattern or motion relationship. However, when the target motion pattern is complex and changeable, it is often difficult to obtain an analytical solution. Unknown non-cooperative targets are predicted, the method is more difficult to adapt. The numerical integration method obtains the target predicted trajectory by forward integration of the dynamic model. Similarly, when the target movement mode changes, especially when the trajectory is predicted for the non-cooperative target whose maneuvering mode is unknown, due to the prediction of the numerical integration method. The process is an open-loop process, and there is no external information to correct the predicted trajectory, which will lead to the continuous accumulation of errors in the prediction process and rapid divergence, which is difficult to meet the actual trajectory prediction requirements of non-cooperative targets.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种可以提高轨迹预测精度的基于博弈对抗的目标轨迹预测系统及预测方法。The purpose of the present invention is to provide a target trajectory prediction system and prediction method based on game confrontation which can improve the trajectory prediction accuracy.

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:

一种目标轨迹预测系统,包括:用于建立目标机动特征模型集的目标机动特征模型集模块,所述目标机动特征集中包括若干目标的典型机动局部模型;用于对目标进行前向轨迹预测的前向轨迹预测模块,所述前向轨迹预测模块定义性能指标函数并建立目标轨迹预测模型,通过性能指标函数计算目标机动特征模型集合中的各典型机动局部模型与目标实际飞行轨迹的匹配度,根据各典型机动局部模型与目标实际飞行轨迹的匹配度确定目标轨迹预测模型的参数,将求得的参数代入目标轨迹预测模型中,采用数值积分法对目标进行前向轨迹预测;A target trajectory prediction system, comprising: a target maneuvering feature model set module for establishing a target maneuvering feature model set, the target maneuvering feature set including typical maneuvering local models of several targets; a target maneuvering feature set for predicting the forward trajectory of the target A forward trajectory prediction module, the forward trajectory prediction module defines a performance index function and establishes a target trajectory prediction model, and calculates the matching degree between each typical maneuvering local model in the target maneuvering feature model set and the target actual flight trajectory through the performance index function, Determine the parameters of the target trajectory prediction model according to the matching degree between each typical maneuvering local model and the actual flight trajectory of the target, substitute the obtained parameters into the target trajectory prediction model, and use the numerical integration method to predict the target's forward trajectory;

用于对目标轨迹进行修正的后向反馈修正模块,所述后向反馈修正模块根据飞行重要节点建立全局重要性地图模型,然后根据全局重要性地图模型建立前向预测轨迹与飞行重要节点的关联关系,确定目标机动意图信息,利用目标机动意图信息,采用后验条件概率方法对目标轨迹预测模型的参数进行修正,再根据参数修正后的目标轨迹预测模型进行轨迹预测,得到修正后的轨迹预测结果。A backward feedback correction module for correcting the target trajectory, the backward feedback correction module establishes a global importance map model according to the important flight nodes, and then establishes the association between the forward predicted trajectory and the flight important nodes according to the global importance map model relationship, determine the target maneuver intention information, use the target maneuver intention information, use the posterior conditional probability method to modify the parameters of the target trajectory prediction model, and then perform trajectory prediction according to the target trajectory prediction model after parameter correction, and obtain the modified trajectory prediction. result.

进一步的,所述目标机动特征模型集模块在建立目标机动特征集时,首先确定表征目标机动模式的机动特征模型,然后对目标机动状态进行分解,建立目标的典型机动局部模型,各典型机动局部模型组成目标机动特征模型集。Further, when establishing the target maneuvering feature set, the target maneuvering feature model set module first determines the maneuvering feature model representing the target maneuvering mode, and then decomposes the target maneuvering state to establish a typical maneuvering partial model of the target, each typical maneuvering partial model. The model constitutes the target maneuvering feature model set.

进一步的,所述机动特征模型包括CA模型、CV模型、典型轨迹数据模型、动力学模型。Further, the maneuvering feature model includes a CA model, a CV model, a typical trajectory data model, and a dynamic model.

本发明还提供了一种目标轨迹预测方法,包括以下步骤:The present invention also provides a target trajectory prediction method, comprising the following steps:

S1、建立目标机动特征模型集,首先确定目标的机动特征模型,然后对目标机动状态进行分解,基于机动特征模型建立目标的典型机动局部模型,典型机动局部模型组成目标机动特征模型集;S1. Establish a target maneuvering feature model set, first determine the maneuvering feature model of the target, then decompose the target maneuvering state, establish a typical maneuvering local model of the target based on the maneuvering feature model, and the typical maneuvering local model constitutes the target maneuvering feature model set;

S2、目标前向轨迹预测,定义性能指标函数并建立目标轨迹预测模型,通过性能指标函数评价目标机动特征模型集合中的各典型机动局部模型与目标实际飞行轨迹的匹配度,根据各典型机动局部模型与目标实际飞行轨迹的匹配度确定目标轨迹预测模型的参数,采用数值积分法基于目标轨迹预测模型对目标进行前向轨迹预测;S2, target forward trajectory prediction, define the performance index function and establish the target trajectory prediction model, and evaluate the matching degree of each typical maneuvering local model in the target maneuvering feature model set with the target actual flight trajectory through the performance index function. The matching degree between the model and the actual flight trajectory of the target determines the parameters of the target trajectory prediction model, and the numerical integration method is used to predict the forward trajectory of the target based on the target trajectory prediction model;

S3、目标轨迹修正,首先确定飞行重要节点,然后根据飞行重要节点建立全局重要性地图模型,然后根据全局重要性地图模型建立前向预测轨迹与飞行重要节点的关联关系,确定目标机动意图信息,再利用目标机动意图信息采用后验条件概率方法对目标轨迹预测模型的参数进行修正,再根据参数修正后的目标轨迹预测模型对目标轨迹进行轨迹预测,得到修正后的轨迹预测结果。S3, target trajectory correction, first determine the important flight nodes, then establish a global importance map model according to the flight important nodes, and then establish the relationship between the forward predicted trajectory and the flight important nodes according to the global importance map model, and determine the target maneuvering intention information, Then, the parameters of the target trajectory prediction model are modified by the posterior conditional probability method using the target maneuver intention information, and then the target trajectory is predicted according to the target trajectory prediction model after parameter correction, and the modified trajectory prediction result is obtained.

进一步的,步骤S2中采用加权最小二乘方法求解目标轨迹预测模型的参数。Further, in step S2, a weighted least squares method is used to solve the parameters of the target trajectory prediction model.

进一步的,步骤S2中采用跟踪误差加权法定义性能指标函数,典型机动局部模型与目标实际飞行轨迹的匹配度等于典型机动局部模型的权值

Figure BDA0003018128920000031
式中的Ji(k)表示目标机动特征模型集中第i个典型机动局部模型的性能指标值,M为目标机动特性模型集中典型机动局部模型的数量。Further, in step S2, the tracking error weighting method is used to define the performance index function, and the matching degree between the typical maneuvering local model and the actual flight trajectory of the target is equal to the weight of the typical maneuvering local model.
Figure BDA0003018128920000031
In the formula, J i (k) represents the performance index value of the ith typical maneuvering local model in the target maneuvering characteristic model set, and M is the number of typical maneuvering partial models in the target maneuvering characteristic model set.

进一步的,步骤S3中采用贝叶斯推理方法确定目标机动意图信息,目标机动意图信息为

Figure BDA0003018128920000032
式中的p(xk-1=j)为k-1时刻第j个典型机动局部模型的先验概率,p(xk=i,xk-1=j)为典型机动局部模型对应的预测轨迹后验概率。Further, in step S3, the Bayesian inference method is used to determine the target maneuvering intention information, and the target maneuvering intention information is:
Figure BDA0003018128920000032
where p(x k-1 =j) is the prior probability of the j-th typical maneuvering local model at time k-1, p(x k =i, x k-1 =j) is the corresponding value of the typical maneuvering local model Predicted trajectory posterior probability.

进一步的,步骤S3中目标轨迹预测模型的参数的修正方法如下:利用目标机动意图信息p(xk=i|xk-1=j)对目标预测状态进行修正,有:p(xk|x1:k-1)=∫p(xk|xk-1)p(xk-1|x1:k-1)dxk-1,再根据修正结果p(xk|x1:k-1)对目标轨迹预测模型的参数进行修正,有:

Figure BDA0003018128920000033
其中,
Figure BDA0003018128920000034
为目标机动特征模型集中的典型机动局部模型的参数。Further, the method for modifying the parameters of the target trajectory prediction model in step S3 is as follows: using the target maneuvering intention information p(x k =i|x k-1 =j) to correct the target prediction state, there are: p(x k | x 1:k-1 )=∫p(x k |x k-1 )p(x k-1 |x 1:k-1 )dx k-1 , and then according to the correction result p(x k |x 1 : k-1 ) Modify the parameters of the target trajectory prediction model, including:
Figure BDA0003018128920000033
in,
Figure BDA0003018128920000034
The parameters of the typical maneuvering local model in the target maneuvering feature model set.

由以上技术方案可知,本发明通过建立由典型机动局部模型构成的目标机动特征模型集,前向轨迹预测模块在基于目标运动特征约束轨迹预测的基础上,通过后向反馈修正模块引入基于博弈对抗的目标机动意图评估,对预测轨迹后向修正,形成了一套运动特征约束下前向预测与博弈机动意图评估下预测轨迹反馈修正相结合的轨迹闭环预测系统,突破了传统轨迹预测方法开环预测局限性,将基于博弈对抗的预测轨迹反馈修正融入轨迹预测过程,实现轨迹闭环预测,提高了轨迹预测精度。而且,本发明的闭环预测过程中,采用目标机动特征与博弈对抗意图约束轨迹预测范围,由于合作目标具有更丰富的机动意图信息,从而具有更好预测效果。本发明既适用合作目标轨迹预测,又适用于非合作目标轨迹预测,预测系统具有较好的兼容性,能够在各模块中兼容不同类型数据滤波与处理算法。As can be seen from the above technical solutions, the present invention establishes a target maneuvering feature model set composed of typical maneuvering local models, and the forward trajectory prediction module, on the basis of constraining the trajectory prediction based on the target motion feature, introduces the game-based confrontation through the backward feedback correction module. The target maneuvering intention evaluation is based on the target maneuvering intention, and the predicted trajectory is corrected backward, forming a closed-loop trajectory prediction system that combines forward prediction under the constraint of motion characteristics and prediction trajectory feedback correction under the game maneuvering intention evaluation, breaking through the traditional open-loop trajectory prediction method. Due to the limitations of prediction, the prediction trajectory feedback correction based on game confrontation is integrated into the trajectory prediction process to achieve closed-loop trajectory prediction and improve the trajectory prediction accuracy. Moreover, in the closed-loop prediction process of the present invention, target maneuvering characteristics and game confrontation intention are used to constrain the trajectory prediction range, and since the cooperative target has richer maneuvering intention information, it has a better prediction effect. The present invention is suitable for both cooperative target trajectory prediction and non-cooperative target trajectory prediction, the prediction system has good compatibility, and can be compatible with different types of data filtering and processing algorithms in each module.

附图说明Description of drawings

图1为本发明实施例的框图;1 is a block diagram of an embodiment of the present invention;

图2为再入滑翔目标的飞行轨迹与其局部模型表征的关系示意图;Figure 2 is a schematic diagram of the relationship between the flight trajectory of the re-entry gliding target and its local model representation;

图3为再入滑翔目标的机动特征模型集合对轨迹空间覆盖示意图;3 is a schematic diagram of the trajectory space coverage of the maneuvering feature model set of the re-entry gliding target;

图4为重要性地图的示意图;4 is a schematic diagram of an importance map;

图5为预测轨迹与飞行重要节点的关联关系图;Figure 5 is a diagram showing the relationship between the predicted trajectory and important flight nodes;

图6为采用本发明方法预测的误差变化曲线图;Fig. 6 is the error change curve diagram that adopts the method of the present invention to predict;

图7为模型隶属度关系变化曲线图;Fig. 7 is a graph showing the variation curve of the membership degree of the model;

图8为模型倾侧角β的变化对比曲线图;Fig. 8 is the change contrast curve diagram of the model inclination angle β;

图9为模型KL的变化对比曲线图;Fig. 9 is the change contrast curve diagram of model KL ;

图10为模型KD的变化对比曲线图。Fig. 10 is a graph showing the comparison of changes of the model K D.

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

下面结合附图对本发明进行详细描述,在详述本发明实施例时,为便于说明,表示器件结构的附图会不依一般比例做局部放大,而且所述示意图只是示例,其在此不应限制本发明保护的范围。需要说明的是,附图采用简化的形式且均使用非精准的比例,仅用以方便、清晰地辅助说明本发明实施例的目的。The present invention will be described in detail below with reference to the accompanying drawings. When describing the embodiments of the present invention in detail, for the convenience of explanation, the drawings representing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not be limited here. The scope of protection of the present invention. It should be noted that the accompanying drawings are in a simplified form and all use inaccurate scales, and are only used to facilitate and clearly assist the purpose of explaining the embodiments of the present invention.

图1为本发明系统的框图,本发明的目标轨迹预测系统包括目标机动特征模型集模块、基于机动特征约束的前向轨迹预测模块以及基于博弈对抗的后向反馈修正模块。1 is a block diagram of the system of the present invention. The target trajectory prediction system of the present invention includes a target maneuver feature model set module, a forward trajectory prediction module based on maneuver feature constraints, and a backward feedback correction module based on game confrontation.

目标机动特征模型集模块用于建立描述目标典型机动样式的目标机动特征模型集,通过目标机动特征模型集的构建覆盖目标机动空间,并作为目标轨迹预测过程的先验信息来约束目标轨迹预测过程,通过目标先验信息的引入来提高目标轨迹预测精度。The target maneuvering feature model set module is used to establish a target maneuvering feature model set that describes the typical maneuvering style of the target. The target maneuvering space is covered by the construction of the target maneuvering feature model set, and it is used as the prior information of the target trajectory prediction process to constrain the target trajectory prediction process. , through the introduction of target prior information to improve the target trajectory prediction accuracy.

目标机动特征模型集模块的工作过程如下:首先,确定表征目标机动模式的机动特征模型,比较常见的机动特征模型包括CA模型(恒定速度模型)、CV模型(恒定加速度模型)、典型轨迹数据模型、动力学模型等,在实际应用中,采用什么机动特征模型来表征目标的机动模式可根据目标的运动特点来相应选择。The working process of the target maneuvering feature model set module is as follows: First, determine the maneuvering feature model representing the target maneuvering mode. The more common maneuvering feature models include CA model (constant velocity model), CV model (constant acceleration model), typical trajectory data model , dynamic model, etc. In practical applications, what maneuvering feature model is used to characterize the maneuvering mode of the target can be selected according to the motion characteristics of the target.

在确定好表征目标机动模式的机动特征模型后,对目标进行机动模式、飞行状态、机动能力等机动状态分解,建立目标的典型机动局部模型,由得到的若干典型机动局部模型组成目标机动特征模型集。例如,如果采用CA模型、CV模型来表征目标的机动模式,可以根据目标能力范围对目标进行机动状态分解,确定模型中不同的局部特征模型参数,从而建立起目标的多个典型机动局部模型,这些典型机动局部模型组成目标机动特征模型集。又比如,采用典型轨迹数据模型作为机动特征模型来表征目标的机动模式,可以结合目标不同条件、不同状态下的典型机动样式对目标进行机动状态分解,利用动力学原理生成目标样本轨迹,建立起目标的典型机动局部模型,进而形成目标机动特征模型集。After determining the maneuvering feature model representing the maneuvering mode of the target, decompose the maneuvering state of the target, such as maneuvering mode, flight state, maneuvering ability, etc., to establish a typical maneuvering local model of the target, and the target maneuvering feature model is composed of several typical maneuvering partial models obtained. set. For example, if CA model and CV model are used to represent the maneuvering mode of the target, the maneuvering state of the target can be decomposed according to the target capability range, and different local characteristic model parameters in the model can be determined, thereby establishing multiple typical maneuvering local models of the target. These typical maneuvering local models constitute the target maneuvering feature model set. For another example, the typical trajectory data model is used as the maneuvering feature model to represent the maneuvering mode of the target, and the maneuvering state of the target can be decomposed according to the typical maneuvering patterns under different conditions and states of the target, and the target sample trajectory can be generated by using the dynamic principle to establish the target sample trajectory. The typical maneuvering local model of the target, and then the target maneuvering feature model set is formed.

前向轨迹预测模块用于建立目标轨迹预测模型,基于目标轨迹预测模型对目标进行前向轨迹预测,其以目标的轨迹跟踪数据为输入,同时结合目标机动特征模型集,采用数据融合的方法确定目标的典型机动局部模型的概率分布,基于目标轨迹预测模型采用数值积分方法进行前向轨迹预测。目标的轨迹跟踪数据是由目标轨迹跟踪系统探测、跟踪以及处理后得到的数据。The forward trajectory prediction module is used to establish a target trajectory prediction model, and based on the target trajectory prediction model, the forward trajectory prediction of the target is performed. The probability distribution of the typical maneuvering local model of the target, based on the target trajectory prediction model, the numerical integration method is used to predict the forward trajectory. The trajectory tracking data of the target is the data obtained after being detected, tracked and processed by the target trajectory tracking system.

前向轨迹预测模块的工作过程如下:定义性能指标函数并建立目标轨迹预测模型,性能指标函数用于评价目标机动特征模型集中的各典型机动局部模型与目标实际飞行轨迹的匹配度(关联关系),建立起轨迹预测的运动特征关联约束机制;结合目标的运动特征建立用于轨迹预测的运动学或动力学模型,用于目标轨迹预测。可采用多种形式来定义性能指标函数,比较常用的方法有跟踪误差加权法、跟踪数据交互式多模型滤波法等。The working process of the forward trajectory prediction module is as follows: define the performance index function and establish the target trajectory prediction model. The performance index function is used to evaluate the matching degree (correlation relationship) between each typical maneuvering local model in the target maneuvering feature model set and the actual flight trajectory of the target. , establishes a motion feature association constraint mechanism for trajectory prediction; establishes a kinematics or dynamics model for trajectory prediction combined with the motion features of the target, which is used for target trajectory prediction. A variety of forms can be used to define the performance index function, and the more commonly used methods are the tracking error weighting method, the tracking data interactive multi-model filtering method, and so on.

然后利用性能指标函数,基于相似度原则计算目标机动特征模型集合中的各典型机动局部模型与目标实际飞行轨迹的匹配度,以匹配度为依据,提炼目标机动特征变化规律,也就是确定目标轨迹预测模型的参数,将求得的参数代入目标轨迹预测模型中,然后采用数值积分法进行目标的前向轨迹预测。可采用加权最小二乘方法来求解目标轨迹预测模型的参数。Then use the performance index function to calculate the matching degree of each typical maneuvering local model in the target maneuvering feature model set and the actual flight trajectory of the target based on the similarity principle. Predict the parameters of the model, substitute the obtained parameters into the target trajectory prediction model, and then use the numerical integration method to predict the forward trajectory of the target. The weighted least squares method can be used to solve the parameters of the target trajectory prediction model.

后向反馈修正模块用于对前向轨迹预测模块预测的目标轨迹进行修正。后向反馈修正模块利用前向轨迹预测模块的轨迹预测结果,通过构建博弈机动意图评估重要性地图模型,对目标博弈机动意图进行评估,提取具有目标博弈机动意图评估的预测轨迹修正信息,并将其引入目标轨迹前向预测模块所建立的目标轨迹预测模型,对预测轨迹进行修正。The backward feedback correction module is used to correct the target trajectory predicted by the forward trajectory prediction module. The backward feedback correction module uses the trajectory prediction results of the forward trajectory prediction module to evaluate the target game maneuver intention by constructing a game maneuver intention evaluation importance map model, extracts the predicted trajectory correction information with the target game maneuver intention evaluation, and then calculates the target game maneuver intention. It introduces the target trajectory prediction model established by the target trajectory forward prediction module, and corrects the predicted trajectory.

后向反馈修正模块的工作过程如下:首先根据飞行重要节点建立全局重要性地图模型,可采用多模型“分解-合成”法建立全局重要性地图模型,即先确定目标飞行所在空域各节点的重要程度,建立以飞行重要节点为中心的局部模型,通过拓扑映射建立全局描述关系,得到全局重要性地图模型。飞行重要节点根据应用场景相应设定,例如可将对目标飞行任务有较大影响的重要政治、军事或经济场所、设施等设置为飞行重要节点。The working process of the backward feedback correction module is as follows: Firstly, a global importance map model is established according to the important flight nodes, and the global importance map model can be established by the multi-model "decomposition-combination" method, that is, the importance of each node in the airspace where the target flight is located is determined first. degree, establish a local model centered on important flight nodes, establish a global description relationship through topology mapping, and obtain a global importance map model. The important flight nodes are set accordingly according to the application scenarios. For example, important political, military or economic places and facilities that have a greater impact on the target flight mission can be set as flight important nodes.

然后根据全局重要性地图模型,建立前向预测轨迹与飞行重要节点的关联关系,确定目标机动意图信息,更具体的,可以利用全局重要性地图模型与前向轨迹预测区域(目标轨迹预测空间分布)之间的覆盖匹配准则来建立前向预测轨迹与飞行重要节点的关联关系来确定目标机动意图信息。假定关联度越大的目标威胁度越大,可采用贝叶斯推理方法确定目标机动意图信息。前向轨迹预测区域通过目标轨迹预测模型预测的轨迹确定。Then, according to the global importance map model, the relationship between the forward predicted trajectory and the important flight nodes is established, and the target maneuvering intention information is determined. More specifically, the global importance map model and the forward trajectory prediction area (target trajectory prediction spatial distribution ) to establish the relationship between the forward predicted trajectory and the important flight nodes to determine the target maneuver intention information. Assuming that the greater the correlation degree is, the greater the threat degree of the target, the Bayesian inference method can be used to determine the target maneuver intention information. The forward trajectory prediction area is determined by the trajectory predicted by the target trajectory prediction model.

利用目标机动意图信息,采用后验条件概率方法对目标轨迹预测模型的参数进行修正,再根据参数修正后的目标轨迹预测模型进行轨迹预测,得到修正后的轨迹预测结果。Using the target maneuvering intention information, the parameters of the target trajectory prediction model are modified by the posterior conditional probability method, and then the trajectory prediction is carried out according to the target trajectory prediction model after parameter correction, and the modified trajectory prediction result is obtained.

下面以一个具体实施例对本发明目标轨迹预测系统的轨迹预测过程作进一步的说明,本实施例是对一再入滑翔目标进行轨迹预测。The trajectory prediction process of the target trajectory prediction system of the present invention will be further described below with a specific embodiment. This embodiment is to predict the trajectory of a re-entry gliding target.

首先,建立该再入滑翔目标的目标机动特征模型集,目标机动特征模型集模块根据再入滑翔目标的运动特点,采用运动学原理即可得到目标典型运动轨迹,因此采用典型轨迹数据模型作为再入滑翔目标的机动特征模型,选择具有N个LGR节点的状态变量,采用Radau伪谱法离散点对不同阶段不同状态的局部模型进行表征,T={K1,K2,...,KN},其中,

Figure BDA0003018128920000061
Xl为目标第l个LGR节点的状态量,Ul为目标第l个LGR节点的飞行控制量。用典型轨迹数据模型实现对目标在特定条件下轨迹的表征,如图2所示。Firstly, the target maneuvering feature model set of the re-entry gliding target is established. The target maneuvering feature model set module can obtain the typical motion trajectory of the target by using the kinematics principle according to the motion characteristics of the re-entry gliding target. Therefore, the typical trajectory data model is used as the re-entry gliding target. Enter the maneuvering characteristic model of the gliding target, select the state variable with N LGR nodes, and use the Radau pseudospectral method to discrete points to characterize the local models of different states at different stages, T={K 1 ,K 2 ,...,K N }, where,
Figure BDA0003018128920000061
X l is the state quantity of the lth LGR node of the target, and U l is the flight control quantity of the lth LGR node of the target. A typical trajectory data model is used to characterize the trajectory of the target under specific conditions, as shown in Figure 2.

然后,针对目标的不同机动模式、不同飞行状态,采用动力学原理得到多条特征轨迹样本数据,采用多模型建模方法对目标进行机动状态分解,得到目标的典型机动局部模型,典型机动局部模型组成目标机动特征模型集。本实施例对d维约束空间中每一个维度的状态分别定义为pj,j=1,2,…,d,每一个维度的状态可能取值的集合为实数空间的子集,即

Figure BDA0003018128920000071
进而建立目标机动特征模型集Pc如下:
Figure BDA0003018128920000072
对覆盖约束空间的元素进行求解,即可得到覆盖目标轨迹空间的机动特征模型集。本实施例选择再入高度为72km,再入速度为4000m/s,终端高度约束为10km,当纵向飞行距离发生变化时,对不同纵向飞行距离约束进行离线轨迹优化构成覆盖飞行范围的轨迹集如图3所示,由典型机动局部模型构成的机动特征模型集合可覆盖目标不同机动模式下的飞行空域。Then, according to the different maneuvering modes and different flight states of the target, the dynamic principle is used to obtain multiple characteristic trajectory sample data, and the multi-model modeling method is used to decompose the maneuvering state of the target to obtain the typical maneuvering local model of the target and the typical maneuvering local model. A set of target maneuvering feature models is formed. In this embodiment, the state of each dimension in the d-dimensional constraint space is defined as p j , j=1, 2, .
Figure BDA0003018128920000071
Then, the target maneuvering feature model set Pc is established as follows:
Figure BDA0003018128920000072
By solving the elements covering the constraint space, the maneuvering feature model set covering the target trajectory space can be obtained. In this embodiment, the re-entry height is selected as 72 km, the re-entry speed is 4000 m/s, and the terminal height constraint is 10 km. When the longitudinal flight distance changes, offline trajectory optimization is performed for different longitudinal flight distance constraints to form a trajectory set covering the flight range. As shown in Figure 3, the maneuvering feature model set composed of typical maneuvering local models can cover the flight airspace of the target under different maneuvering modes.

建立目标机动特性模型集后,前向轨迹预测模块进行前向轨迹预测。前向轨迹预测模块定义性能指标函数和建立目标轨迹预测模型,本实施例采用跟踪误差加权法来定义性能指标函数,得到的性能指标函数为:

Figure BDA0003018128920000073
式中的ei(k)表示目标机动特征模型集中第i个典型机动局部模型在时刻k的轨迹偏差,M为目标机动特性模型集中典型机动局部模型的数量,exp表示指数运算,a0和a1分别为当前误差权重因子和历史误差权重因子,a0和a1均大于0,决定了当前误差和历史误差在性能指标中的权重,用于确定当前时刻误差和过去时刻误差组合对性能指标的相对重要程度,τ>0为遗忘因子,表示性能指标的记忆效应,h为所选取的有限时域长度,
Figure BDA0003018128920000074
Figure BDA0003018128920000075
为第i个典型机动局部模型在时刻k的轨迹状态,y(k)为在时刻k目标的实际飞行轨迹状态,rdl表示计算欧式距离;利用当前目标的轨迹跟踪数据与飞行过程历史信息构成性能指标,通过性能指标函数来评价机动特征模型集中的各典型机动局部模型与目标实际飞行轨迹(目标当前运动状态)的匹配度,性能指标值Ji(k)越大,说明第i个典型机动局部模型与实际目标状态的匹配度越低,基于性能指标值Ji(k),用典型机动局部模型的权值
Figure BDA0003018128920000081
来表示典型机动局部模型与目标实际飞行轨迹的匹配度,wi(k)为第i个典型机动局部模型与目标实际飞行轨迹的匹配度。After establishing the target maneuver characteristic model set, the forward trajectory prediction module performs forward trajectory prediction. The forward trajectory prediction module defines the performance index function and establishes the target trajectory prediction model. In this embodiment, the tracking error weighting method is used to define the performance index function, and the obtained performance index function is:
Figure BDA0003018128920000073
where e i (k) represents the trajectory deviation of the ith typical maneuvering local model in the target maneuvering feature model set at time k, M is the number of typical maneuvering local models in the target maneuvering feature model set, exp represents the exponential operation, a 0 and a 1 is the weight factor of the current error and the weight factor of the historical error respectively, a 0 and a 1 are both greater than 0, which determine the weight of the current error and the historical error in the performance index, and are used to determine the combination of the current time error and the past time error on the performance The relative importance of the index, τ>0 is the forgetting factor, indicating the memory effect of the performance index, h is the selected finite time domain length,
Figure BDA0003018128920000074
Figure BDA0003018128920000075
is the trajectory state of the i-th typical maneuvering local model at time k, y(k) is the actual flight trajectory state of the target at time k, rd l represents the calculated Euclidean distance; the trajectory tracking data of the current target and the historical information of the flight process are used to form The performance index is used to evaluate the matching degree of each typical maneuvering local model in the maneuver feature model set and the actual flight trajectory of the target (the current motion state of the target) through the performance index function. The lower the matching degree of the maneuvering local model and the actual target state is, based on the performance index value J i (k), the weight of the typical maneuvering local model is used.
Figure BDA0003018128920000081
to represent the matching degree between the typical maneuvering local model and the actual flight trajectory of the target, w i (k) is the matching degree between the ith typical maneuvering local model and the actual flight trajectory of the target.

本实施例对再入滑翔目标半速度坐标系下运动模型进行简化,建立的目标轨迹预测模型为:

Figure BDA0003018128920000082
式中的v为当地速度,γ为当地航迹倾角,χ为当地航迹偏角,r为目标地心距离,θ为纬度,
Figure BDA0003018128920000083
为经度,ρ为目标所在大气环境下的空气密度,g为目标所在位置的重力加速度,β为倾侧角,
Figure BDA0003018128920000084
s为受力参考面积,CD和CL分别为目标的阻力系数和升力系数,m为目标质量。This embodiment simplifies the motion model in the half-velocity coordinate system of the re-entry gliding target, and the established target trajectory prediction model is:
Figure BDA0003018128920000082
where v is the local speed, γ is the local track inclination, χ is the local track declination, r is the target geocentric distance, θ is the latitude,
Figure BDA0003018128920000083
is the longitude, ρ is the air density in the atmospheric environment where the target is located, g is the gravitational acceleration at the location of the target, β is the tilt angle,
Figure BDA0003018128920000084
s is the force reference area, CD and CL are the drag coefficient and lift coefficient of the target, respectively, and m is the target mass.

最后,利用性能指标函数计算典型机动局部模型与目标实际飞行轨迹的匹配度wi(k),根据wi(k)采用加权最小二乘方法拟合目标轨迹预测模型的参数(KD,KL,β),将拟合得到的参数代入目标轨迹预测模型中,采用四阶“龙格-库塔”数值积分方法积分得到目标的前向预测轨迹。Finally, use the performance index function to calculate the matching degree wi (k) of the typical maneuvering local model and the actual flight trajectory of the target, and use the weighted least squares method to fit the parameters of the target trajectory prediction model (K D , K according to wi (k) L , β), the parameters obtained by fitting are substituted into the target trajectory prediction model, and the fourth-order "Runge-Kutta" numerical integration method is used to obtain the forward predicted trajectory of the target.

后向反馈修正模块用于对前向轨迹预测模块预测的轨迹进行修正。首先,采用多模型“分解-合成”法建立全局重要性地图模型,多模型“分解-合成”法是已知的方法,不是本发明的创新之处,在此不再赘述。本实施例假设有4个要地区域(飞行重要节点),且服从高斯分布,则可在根据高斯分布计算各飞行重要节点局部分布的基础上,采用联合分布概率函数fm形成全局重要性地图模型,如图4所示。The backward feedback correction module is used to correct the trajectory predicted by the forward trajectory prediction module. First, the multi-model "decomposition-combination" method is used to establish the global importance map model. The multi-model "decomposition-combination" method is a known method and is not an innovation of the present invention, and will not be repeated here. In this embodiment, it is assumed that there are 4 important areas (flight important nodes) and they obey the Gaussian distribution. On the basis of calculating the local distribution of each important flight node according to the Gaussian distribution, the joint distribution probability function f m can be used to form a global importance map model, as shown in Figure 4.

其次,将目标轨迹预测模型预测的前向轨迹映射到全局重要性地图模型上,通过特征约束下目标前向轨迹预测区域与全局重要性地图模型区域的关联关系,确定目标机动意图信息,如图5所示。利用全局重要性地图模型对目标机动意图进行评估,本实施例的要地区域(飞行重要节点)服从高斯分布,利用联合分布概率函数

Figure BDA0003018128920000091
形成全局重要性地图模型,式中的xm0为重要性地图中第m个飞行重要节点中心点位置,σm为第m个飞行重要节点覆盖范围,x表示全局重要性地图模型中其它点的位置。Secondly, the forward trajectory predicted by the target trajectory prediction model is mapped to the global importance map model, and the target maneuver intention information is determined through the relationship between the target forward trajectory prediction area and the global importance map model area under the feature constraints, as shown in the figure. 5 shown. The global importance map model is used to evaluate the target maneuvering intention. The important area (flight important node) in this embodiment obeys the Gaussian distribution, and the joint distribution probability function is used.
Figure BDA0003018128920000091
A global importance map model is formed, where x m0 is the center point position of the m-th important flight node in the importance map, σ m is the coverage of the m-th important flight node, and x represents the distance of other points in the global importance map model. Location.

本实施例根据目标轨迹预测模型得到的目标前向轨迹预测区域的空间分布为:fp1(x)和fp2(x),对应得到的预测轨迹的后验概率wa为:

Figure BDA0003018128920000092
式中的wp1和wp2分别为目标前向轨迹预测区域的两个空间分布的后验概率,x1、x2、x3、x4为飞行重要节点分布范围(见图5);然后根据预测轨迹的后验概率,用贝叶斯推理方法确定各典型机动局部模型的条件概率,即各典型机动局部模型所描述的目标机动意图信息为:
Figure BDA0003018128920000093
式中的p(xk-1=j)为k-1时刻第j个典型机动局部模型(j=1,2,…,M)的先验概率,典型机动局部模型的先验概率为典型机动局部模型与目标实际飞行轨迹的匹配度,在本实施例中典型机动局部模型的先验概率是典型机动局部模型的权值wi(k),p(xk=i,xk-1=j)为典型机动局部模型对应的预测轨迹后验概率,也就是预测轨迹的后验概率wa;基于贝叶斯预测原理,利用目标机动意图信息p(xk=i|xk-1=j)对目标预测状态进行修正,则有:p(xk|x1:k-1)=∫p(xk|xk-1)p(xk-1|x1:k-1)dxk-1,其中p(xk-1|x1:k-1)为k-1时刻的条件概率,也就是k-1时刻典型机动局部模型的目标机动意图信息,再根据修正结果p(xk|x1:k-1)对前向轨迹预测模块建立的目标轨迹预测模型的参数进行修正,有:
Figure BDA0003018128920000101
其中,
Figure BDA0003018128920000102
为目标机动特征模型集中的典型机动局部模型的参数(本实施例中典型机动局部模型的参数包括KD,KL,β),M为目标机动特征模型集中典型机动局部模型的数量,在此基础上,利用参数修正后的目标轨迹预测模型进行轨迹预测,得到修正后的轨迹预测结果。The spatial distribution of the target forward trajectory prediction area obtained according to the target trajectory prediction model in this embodiment is: f p1 (x) and f p2 (x), and the posterior probability wa of the corresponding obtained predicted trajectory is:
Figure BDA0003018128920000092
In the formula, w p1 and w p2 are the posterior probabilities of the two spatial distributions of the target forward trajectory prediction area, respectively, and x 1 , x 2 , x 3 , and x 4 are the distribution ranges of important flight nodes (see Figure 5); then According to the posterior probability of the predicted trajectory, the Bayesian inference method is used to determine the conditional probability of each typical maneuvering local model, that is, the target maneuvering intention information described by each typical maneuvering local model is:
Figure BDA0003018128920000093
where p(x k-1 =j) is the prior probability of the jth typical maneuvering local model (j=1,2,...,M) at time k-1, and the prior probability of the typical maneuvering partial model is The matching degree between the maneuvering local model and the actual flight trajectory of the target. In this embodiment, the prior probability of the typical maneuvering partial model is the weight w i (k) of the typical maneuvering partial model, p(x k =i, x k-1 =j) is the posterior probability of the predicted trajectory corresponding to the typical maneuvering local model, that is, the posterior probability wa of the predicted trajectory; based on the Bayesian prediction principle, the target maneuvering intention information p(x k =i|x k-1 =j) modify the target prediction state, then there are: p(x k |x 1:k-1 )=∫p(x k |x k-1 )p(x k-1 |x 1:k-1 )dx k-1 , where p(x k-1 |x 1:k-1 ) is the conditional probability at time k-1, that is, the target maneuver intention information of the typical maneuvering local model at time k-1, and then according to the correction result p(x k |x 1:k-1 ) modifies the parameters of the target trajectory prediction model established by the forward trajectory prediction module, as follows:
Figure BDA0003018128920000101
in,
Figure BDA0003018128920000102
is the parameter of the typical maneuvering local model in the target maneuvering feature model set (in this embodiment, the parameters of the typical maneuvering partial model include K D , K L , β ), M is the number of typical maneuvering partial models in the target maneuvering feature model set, here On the basis, the target trajectory prediction model after parameter correction is used for trajectory prediction, and the corrected trajectory prediction result is obtained.

图6至图10为采用修正后的目标轨迹预测模型对再入滑翔目标进行轨迹预测的效果图,图6中预测误差变化曲线表明,本发明方法在对目标200秒预测中具有较好的预测精度。图7中模型隶属度关系变化曲线表明,预测过程中局部特征模型的关联关系时收敛的。图8至图10的模型参数变化对比曲线表明,预测过程中预测模型参数时收敛的,同时具有较好的预测性能。Fig. 6 to Fig. 10 are the effect diagrams of using the revised target trajectory prediction model to predict the trajectory of the re-entry gliding target. The change curve of the prediction error in Fig. 6 shows that the method of the present invention has better prediction in the 200-second target prediction. precision. The variation curve of the model membership relationship in Fig. 7 shows that the correlation of the local feature models converges during the prediction process. The comparison curves of the model parameters in Fig. 8 to Fig. 10 show that the prediction of the model parameters is convergent in the prediction process, and has good prediction performance at the same time.

本发明还提供了一种目标轨迹预测方法,包括以下步骤:The present invention also provides a target trajectory prediction method, comprising the following steps:

S1、建立目标机动特征模型集;S1. Establish a target maneuvering feature model set;

首先确定目标的机动特征模型,然后对目标进行机动状态分解,基于机动特征模型建立目标的典型机动局部模型,典型机动局部模型组成目标机动特征模型集;Firstly, the maneuvering feature model of the target is determined, then the maneuvering state of the target is decomposed, and a typical maneuvering local model of the target is established based on the maneuvering feature model, and the typical maneuvering partial model constitutes the target maneuvering feature model set;

S2、目标前向轨迹预测;S2, target forward trajectory prediction;

定义性能指标函数,并建立目标轨迹预测模型,性能指标函数用于评价目标机动特征模型集合中的各典型机动局部模型与目标实际飞行轨迹的匹配度,根据各典型机动局部模型与目标实际飞行轨迹的匹配度确定目标轨迹预测模型的参数,将求得的参数代入目标轨迹预测模型中,采用数值积分法利用目标轨迹预测模型进行目标的前向轨迹预测;Define the performance index function and establish the target trajectory prediction model. The performance index function is used to evaluate the matching degree of each typical maneuvering local model in the target maneuvering feature model set and the actual flight trajectory of the target. According to each typical maneuvering local model and the actual flight trajectory of the target The matching degree of the target trajectory prediction model determines the parameters of the target trajectory prediction model, and the obtained parameters are substituted into the target trajectory prediction model, and the numerical integration method is used to use the target trajectory prediction model to predict the forward trajectory of the target;

S3、目标轨迹修正;S3, target trajectory correction;

首先建立飞行重要节点的全局重要性地图模型,根据全局重要性地图模型,建立前向预测轨迹与全局重要性地图模型中飞行重要节点的关联关系,确定目标机动意图信息;利用目标机动意图信息,采用后验条件概率方法对目标轨迹预测模型的参数进行修正,再根据参数修正后的目标轨迹预测模型对目标轨迹进行轨迹预测,得到修正后的轨迹预测结果。Firstly, a global importance map model of important flight nodes is established. According to the global importance map model, the relationship between the forward predicted trajectory and the flight important nodes in the global importance map model is established to determine the target maneuvering intention information; using the target maneuvering intention information, The parameters of the target trajectory prediction model are modified by the posterior conditional probability method, and then the target trajectory is predicted according to the target trajectory prediction model after parameter correction, and the modified trajectory prediction result is obtained.

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 (8)

1. A target trajectory prediction system, comprising:
the system comprises a target maneuvering characteristic model set module used for establishing a target maneuvering characteristic model set, wherein the target maneuvering characteristic set comprises typical maneuvering local models of a plurality of targets;
the forward track prediction module is used for performing forward track prediction on the target, defining a performance index function and establishing a target track prediction model, calculating the matching degree of each typical maneuvering local model in a target maneuvering characteristic model set and the target actual flight track through the performance index function, determining parameters of the target track prediction model according to the matching degree of each typical maneuvering local model and the target actual flight track, substituting the obtained parameters into the target track prediction model, and performing forward track prediction on the target by adopting a numerical integration method;
the backward feedback correction module is used for correcting the target track, a global importance map model is established according to flight important nodes, then an incidence relation between a forward prediction track and the flight important nodes is established according to the global importance map model, target maneuver intention information is determined, parameters of the target track prediction model are corrected by the target maneuver intention information through a posterior conditional probability method, track prediction is carried out according to the target track prediction model after the parameters are corrected, and a corrected track prediction result is obtained.
2. The target trajectory prediction system of claim 1, wherein: when the target maneuvering characteristic model set module establishes a target maneuvering characteristic set, firstly, a maneuvering characteristic model for representing a target maneuvering mode is determined, then, maneuvering state decomposition is carried out on a target, a typical maneuvering local model of the target is established, and the obtained typical maneuvering local model forms a target maneuvering characteristic model set.
3. The target trajectory prediction system of claim 2, wherein: the maneuvering characteristic model comprises a CA model, a CV model, a typical track data model and a dynamic model.
4. A target trajectory prediction method is characterized by comprising the following steps:
s1, establishing a target maneuvering characteristic model set, firstly determining a maneuvering characteristic model of a target, then carrying out maneuvering state decomposition on the target, establishing a typical maneuvering local model of the target based on the maneuvering characteristic model, and forming the target maneuvering characteristic model set by the typical maneuvering local model;
s2, predicting a target forward track, defining a performance index function, establishing a target track prediction model, evaluating the matching degree of each typical maneuvering local model in the target maneuvering characteristic model set and the target actual flight track through the performance index function, determining parameters of the target track prediction model according to the matching degree of each typical maneuvering local model and the target actual flight track, and predicting the forward track of the target based on the target track prediction model by adopting a numerical integration method;
s3, correcting the target track, namely firstly determining flight important nodes, then establishing a global importance map model according to the flight important nodes, then establishing an incidence relation between a forward predicted track and the flight important nodes according to the global importance map model, determining target maneuver intention information, correcting parameters of the target track prediction model by using the target maneuver intention information and adopting a posterior conditional probability method, and predicting the target track according to the target track prediction model after parameter correction to obtain a corrected track prediction result.
5. The target trajectory prediction method according to claim 4, characterized in that: in step S2, parameters of the target trajectory prediction model are solved by a weighted least squares method.
6. The target trajectory prediction method according to claim 4, characterized in that: in step S2, a tracking error weighting method is adopted to define a performance index function, and the matching degree of the typical maneuvering local model and the target actual flight path is the weight of the typical maneuvering local model
Figure FDA0003018128910000021
Wherein J isi(k) And the performance index value of the ith typical maneuvering local model in the target maneuvering characteristic model set is represented, and M is the number of the typical maneuvering local models in the target maneuvering characteristic model set.
7. The target trajectory prediction method according to claim 6, characterized in that: in step S3, a Bayesian inference method is adopted to determine target maneuver intention information
Figure FDA0003018128910000022
In the formula, p (x)k-1J) is the prior probability of the j-th typical mobile local model at time k-1, p (x)k=i,xk-1J) is the predicted trajectory posterior probability corresponding to a typical maneuver local model.
8. The target trajectory prediction method according to claim 7, characterized in that: the method for correcting the parameters of the target trajectory prediction model in step S3 is as follows: eyes of userTarget movement intention information p (x)k=i|xk-1J) correcting the target predicted state, there are: p (x)k|x1:k-1)=∫p(xk|xk-1)p(xk-1|x1:k-1)dxk-1Then based on the corrected result p (x)k|x1:k-1) Parameters of the target track prediction model are corrected, and the parameters comprise:
Figure FDA0003018128910000031
wherein,
Figure FDA0003018128910000032
parameters of a typical maneuvering local model in the target maneuvering characteristic model set.
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