自适应粒子滤波在卫星紫外导航中的应用

基于紫外敏感器的自主导航系统是典型的非线性和噪声非高斯分布的系统,针对扩展卡尔曼滤波(EKF)和Unscented 卡尔曼滤波(UKF)不适于噪声非高斯分布的系统,和一般粒子滤波缺乏在线自适应调整能力等问题,提出了将基于正交性原理的自适应强跟踪滤波器(STF)和UKF相融合作为重要密度函数,应用于基于紫外敏感器自主导航粒子滤波器新方法,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,使得算法的鲁棒性增强,有极强的对突变状态的跟踪能力,具有强的自适应能力.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,仿真结果说明了所提算法的有效性.

Application of Adaptive Particle Filtering in Satellite Autonomous Navigation Based on Ultraviolet Sensors

Autonomous navigation system is a typical nonlinear system. And non - Gaussian distribution of state and measurement error exists in satellite autonomous navigation. Because extended Kalman filter and unscented Kalman filter are not good at dealing with non -Gaussian problem, and the general particle filter lacks the adaptive capacity, this paper proposes a new particle filtering algorithm called adaptive particle filtering which adopts a new method combining the unscented Kalman filter with the strong tracking filter to produce the important density function. This algorithm adopts UKF to produce particles, in which each sigma point of every particle is updated by STF to make the algorithm have the adaptive capacity. Simulation was done based on simulative measured data to illuminate the effectiveness of navigation method.