基于非对称交互多模型弹道导弹跟踪

弹道导弹飞行三个阶段的动力学模型异常复杂且非线性强,在未知导弹任何先验信息前提下如何连续地跟踪整个弹道已成为目前亟待解决的难题。针对该问题,提出了一种实时非对称交互多模型跟踪算法,它根据弹道不同阶段的受力情况建立相应的跟踪模型。该算法采用非对称的状态交互以及基于熵信息变化的模型概率更新方法对导弹进行实时状态估计,并将其中一个无迹滤波器的非线性状态估计用改进离散方法实现,提高了实时性。仿真实验表明,该跟踪算法能够较精确地从弹道主动段连续地跟踪到再入段,并且能较大幅度地减小模型切换之间的跟踪误差。     

基于MCMC的导弹主动段突防仿真及灵敏度分析

能否对抗预警卫星的早期预警探测是弹道导弹主动段突防的关键。结合导弹主动段飞行仿真、主动段红外特性计算和预警卫星红外探测仿真,基于马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）方法构建了导弹主动段突防仿真模型,并建立了导弹主动突防参数灵敏度分析的响应面近似函数模型。通过导弹主动段突防仿真与参数分析算例,定量给出了导弹主动段性能参数对其突防能力的影响,验证了方法的有效性。     

基于IMM-CSRF的多平台机动目标被动跟踪方法

针对仅有角度测量信息条件下,单平台的机动目标被动跟踪存在固有的强非线性和弱可观测性问题,研究多平台融合机动目标被动跟踪技术,提出了集中式漂移瑞利滤波器(centralized shifted Rayleigh filter,CSRF)来解决多平台目标被动跟踪问题,进一步提出并推导了基于交互式多模型的集中式漂移瑞利滤波器(interacting multiple model centralized shifted Rayleigh filter,IMM CSRF),实现对机动目标的被动跟踪。仿真实验表明,该算法跟踪精度高,稳定性好,具有良好的实际应用价值。     

基于IMM-UKF方法的主动段目标分级与关机识别

主动段目标的分级与关机识别对于系统把握目标模型切换并部署后续跟踪具有重要意义。建立了主动段目标的分级和关机所涉及的两级主动段模型和中段模型;引入了交互式多模型框架以应对不确定模式下的跟踪问题,引入了无迹卡尔曼滤波以解决非线性估计问题。在天基观测条件下进行了仿真实验,结果表明,利用模型概率和总体估计误差的异常变化,可有效识别主动段目标的分级和关机。     

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.     

一种适用于助推段弹道导弹的跟踪方法研究

弹道导弹在助推段的运动特性较为复杂,且由于各级助推器的脱落导弹的加速度存在突变点,在导弹关机时刻的加速度突变最大,因此在跟踪时容易产生较大滤波偏差。本文主要对弹道导弹助推段的加速度特性进行了分析,选择CJ模型来描述其运动特性,采用交互式多模型(IMM)和无敏滤波(UF)算法对处于助推段的弹道目标进行跟踪。由仿真结果可以看出本文所提出的基于CJ模型和CA模型组合的IMM-UF跟踪方法可以较好地实现对助推段弹道导弹的跟踪。同时通过模型集合中各模型的概率变化情况可以很好地完成对导弹关机时刻的判定,这为后续尽早定轨和轨道预报等提供了前提。该算法结构简单实现稳定,具有较好的工程应用价值。     

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.