伪线性卡尔曼滤波的观测器轨迹优化算法

Algorithm of Optimizing the Observer Trajectory based on PLKF

单站被动式跟踪存在强非线性和弱可观测性,即便观测器的运动满足可观测性条件,滤波仍然可能发散。针对此问题,使用伪线性卡尔曼滤波器,应用费希尔信息量、罗美达下限等方法对轨迹进行优化,该优化方案避免了对目标与观测器的径距信息的近似。仿真表明:该方法与传统方法比较,计算量较小,优化得到的轨迹简单,滤波精度得到改善。最后给出了在一定条件下,观测器的“最优”运动规则。

Strongly nonlinear and weak observability are keys in passive tracking. Even if the motion of observer satisfied the condition of observability, it would not be convergence. Aimed at the problem, applying PLEKF, we advanced the function of optimizing based on CRLB and FIM to optimize the observer trajectory. Because of not using the distance between the target and the observer, the optimizing function is simple. Simulation results show that it is simple and convenience compared with traditional method . It also improves the precision of filter. Finally, we give the rules of the observer moving in such condition.