自适应粒子滤波在紫外导航中的应用

Application of Adaptive Particle Filtering in Ultraviolet Sensors

基于紫外敏感器的自主导航系统是典型的非线性系统,针对一般粒子滤波缺乏在线自适应调整能力等问题,文章提出了将基于正交性原理的自适应强跟踪滤波器(STF)和UKF相融合产生重要密度函数,应用于基于紫外敏感器自主导航粒子滤波器新方法,该方法通过UKF构造粒子群,对粒子群中的每一个粒子的每一个sigma点用STF进行更新,使得算法自适应。为了说明算法的有效性,结合模拟的轨道数据和测量数据进行了仿真,并与其他滤波方法的仿真结果进行了对比,结果说明了所提算法的有效性。

Autonomous navigation system based on ultraviolet sensors is a typical nonlinear system. For the general particle filter lacks the adaptive capacity. A new particle filtering algorithm called adaptive particle filtering was proposed which adopts a new method combining the unscented Kalman filter with the strong tracking filter to produce important density functions. The proposed algorithm adopts UKF to produce particles, in which each sigma point of each particle was updated by STF to make the algorithm have adaptive. Simulation was done based on simulated orbit and measurement data-and was compared with results of other filtering algorithms to illuminate the effectiveness of the navigation method.