基于交互式多模型鲁棒滤波的目标机动估计算法

针对雷达导引头的测量信息带有闪烁噪声的问题，研究了交互式多模型和鲁棒滤波在雷达导引头目标机动估计中的应用.采用Huber Based滤波理论改进高阶容积卡尔曼滤波，提出高阶容积鲁棒滤波算法，选取Singer模型、“当前”统计模型、常加速度模型作为目标机动模型，建立雷达导引头测量模型，结合交互式多模型算法框架，设计目标机动估计滤波器.蒙特卡洛数字仿真结果表明，所提算法的鲁棒性较强，与传统高斯滤波相比，所提算法对闪烁噪声具有更高的滤波精度.

Acceleration Estimation of a Maneuvering Target Using InteractingMultiple Model Robust Filter

To cope with the problem of the radar seeker measurement contains glint noise, interacting multiple model and robust filter application in the acceleration estimation of maneuvering target are studied. A novel algorithm named High degree Cubature Robust filter is proposed, in which Huber Based filter theory is used to develop High degree Cubature Kalman filter. Singer Model, Current Statistical Model, and Constant Acceleration Model are selected as mobile model of target. And the radar seeker measurement model is established. The target acceleration estimation filter is designed, which combines the High degree cubature robust filter with the interactive multiple model algorithm framework. The Monte Carlo simulation results show that the algorithm presented in this paper has a stronger robustness, and better accuracy in comparison with the Gaussian filter for the case of glint noise.