自调节鲁棒H_∞滤波目标机动估计

Target maneuvering estimations using H_∞ filter and self-tuning second-order Markov Process Model

将鲁棒H∞滤波理论应用到具有自调节特性的二阶马尔科夫模型下的目标机动估计问题中。初始滤波机动模型参数采用给定值,滤波开始后采用目标运动状态的滤波估计值实时计算模型参数。基于自调节目标机动模型,本文选择卡尔曼滤波和H∞滤波进行了目标机动突变估计仿真研究。结果表明,采用具有自调节特性的二阶马尔科夫目标机动模型时,H∞滤波精度高,适应目标机动突变的能力强,估计精度能够满足精确制导系统的指标要求。

We studied the target maneuvering robustness estimations for high-performance terminal guidance systems using H∞ filtering technique and a self-tuning second-order Markov process target model. The preset parameters of the target model are only used as the initial guess. In the filtering process, the target model parameters are ealenlated according to the estimated target states. With this self-tuning target maneuvering model, H∞ filter and Kalman filter schemes are used in the target state estimations and the simulation results are compared with each other. For high-g realistic target maneuvering, H∞ filter scheme using self-tuning second-order Markov process target model can provide more precise and robust target state estimations than Kalman filter.