随机机动目标拦截中的多模型自适应估计算法

Multi-model adaptive estimation algorithm for interception of randomly maneuvering targets

针对随机机动目标末端制导拦截问题,提出了一种快速有效的多模型自适应估计算法。该算法充分挖掘了单元滤波器组对应假设空间的特殊结构,引入聚合和剪裁手段简化了传统多模型自适应估计算法。为有效处理非线性,不敏卡尔曼滤波器被用于设计依赖假设的单元滤波器。为满足应用要求,给出了该算法的数值鲁棒实现方法。仿真结果验证了所提出算法的有效性。

A fast and efficient algorithm is proposed for multi-model adaptive estimation in the terminal interception of randomly maneuvering target scenario.The traditional multi-model adaptive estimation is simplified by means of aggregation and pruning,based on the exploitation of the special structure of the hypothesis space corresponding to a bank of elemental filters.To efficiently handle the nonlinearity,unscented Kalman filter is introduced into the design of elemental filter that is hypothesis dependent.Numerically robust implementation of the algorithm is presented to meet the need of practical application.Simulation results demonstrate the feasibility of the proposed algorithm.