船舶组合导航自适应迭代粒子滤波方法及应用

Adaptive iterative particle filter and its application for ship integrated navigation

针对多传感器观测信息较多、粒子采样效率较低的问题,提出了一种自适应迭代粒子滤波(adaptive iterated particle filter,AIPF)算法并应用于船舶全球定位系统/惯性导航系统组合导航系统。首先通过粒子滤波自身迭代进行其重要性密度函数的更新。其次,采用自适应退火参数的模拟退火算法,使当前量测量能够快速进入到采样过程,进而大大提高了采样效率。最后,进行了仿真对比计算以及实船试验,结果表明,AIPF算法不仅可以提供精度较高的导航精度,而且增强了滤波性能。

In multi-sensor integrated navigation,extensive observation information and low sampling efficiency will lead to poor navigation performance.An adaptive iterated particle filter is presented and applied in global positioning system/inertial navigation system integrated navigation.Firstly,importance density function is updated iteratively by the particle filter itself.Secondly,by using a simulated annealing algorithm with an adaptive annealing parameter,the current measurement can be quickly incorporated into the sampling process,resulting in greatly improved sampling efficiency.Finally,the performance of the proposed method is examined on the sea trail and the simulation experiment,respectively.The results show that the method can provide better navigation accuracy and improve performance of the filter.