水下组合导航UKF/PF自适应滤波算法

Adaptive UKF/PF-based Filtering Algorithm for Underwater Integrated Navigation

为了避免粒子滤波(PF)可能存在的粒子退化问题,提出了一种Unscented卡尔曼滤波(UKF)和PF的混合滤波算法.将PF的所有粒子分为随机性粒子和确定性粒子两部分,利用UKF状态估计为PF确定重要性函数,并从重要性函数中抽取随机粒子,而确定性粒子则由UKF的Sigma点构成.利用标准粒子滤波的退化程度指标构造自适应权函数,根据权函数权值大小的变化,UKF/PF混合滤波算法自适应地进化为PF滤波算法或退化为UKF滤波算法.仿真结果验证了UKF/PF混合滤波算法用于DR/INS组合滤波器设计的有效性.

UKF/PF-based filtering algorithm is proposed to avoid the particle degradation of the particle tilter. The particles are divided into random particles and determined particles. Using state estimation of UKF constructs importance function. The random particles are extracted from importance function. The determined particles are composed of sigma points of UKF. An adaptive proportion function is constructed to reflect the degradation of standard particle filter, and the proposed UKF/PF filtering algorithm can evolve from PF or degrade to UKF. It has been verified to be effective to apply the UKF/PF filtering algorithm to DR/INS integrated navigation system by simulation.