基于规划路径约束的地形辅助导航算法

Terrain aided navigation algorithm based on planned path

为了提高地形辅助导航算法的精度,充分利用航行器任务规划中的先验信息,提出了基于规划路径约束的地形辅助导航算法。首先针对航行任务中的规划路径提出了"概率隧道"约束模型,将物体运动不确定性与确定性规划路径有机结合。在此基础上,提出了基于概率隧道约束的粒子滤波算法。该算法较之传统的带有等式、不等式约束的滤波器,具有更好的模型适配性,而且计算量上与普通不带约束的粒子滤波器相当。实验表明,该算法有效地利用了先验路径信息,比非约束算法有15 m左右的精度提升。

To improve the accuracy of terrain aided navigation and make full use of prior information in vehicle＇s planned task, this paper proposed a novel terrain aided navigation algorithm based on a planned path. First, this paper proposed a probability tunnel constraint model for the planned path, which makes an efficient combination of the movement uncertainty and the path certainty. Based on this model, the paper proposed a probability tunnel constrained particle filter algorithm. This algorithm has better model suitability compared to the traditional filters with an equality or inequality constraint. And its computation is almost the same as that of normal particle filter. Simulation results show that this algorithm makes efficient use of the prior path information and increase the positioning accuracy by about 15 m compared to the non-constrained filters.