基于SKF 多传感器融合的无人机航迹规划

针对无人机在未知环境下航迹规划难的问题，提出一种基于开关卡尔曼滤波器(switching Kalman filter， SKF)无人机定位和航迹规划的方法。根据多假设理论，建立地图观测数据，结合基于INS/GPS/GIS 多传感器融合 的航迹匹配方法，使用SKF 算法完成多传感器融合和多模型的参数估计，实现无人机的自主定位和航迹规划。仿真 试验结果表明：该算法稳定性好、收敛速度较快、计算复杂度小，具有较高的航迹估计精度。

UAV Route Planning Based on SKF Multi-sensor Fusion

Aiming at the difficulty of UAV route planning in unknown environment, a method of positioning and route planning based on switched Kalman filter (SKF) is proposed. According to multi-hypothesis theory, the map observation data is established. Combined with the route matching method based on INS/GPS/GIS multi-sensor fusion, the multi-sensor fusion and multi-model parameter estimation are completed by using SKF algorithm to realize the autonomous positioning and route of the UAV planning. Simulation results show that the proposed algorithm has good stability, fast convergence speed, low computational complexity and high accuracy of track estimation.