Attitude Fusion Method of Unmanned Vehicle Dual IMU Based on PSO
In order to improve the attitude angle solving accuracy of Micro-Electro-Mechanical System Inertial Measurement Unit(MEMS IMU)in unmanned vehicle system,this paper proposed a Particle Swarm Optimization(PSO)based algorithm and a Strong Tracking Adaptive Unscented Kalman Filter(STAUKF)data fusion method.Firstly,two kinds of IMU modules with different precision were filtered by STAUKF algorithm.Secondly,two kinds of error functions were constructed and PSO algorithm was introduced to fuse the two kinds of IMU posterior estimation.Finally,the test was carried out on the built unmanned vehicle platform.Experimental results show that,compared with the data solved by two single IMU sensors,the root mean square error of the transverse roller shaft and pitch shaft angle solved by the proposed algorithm is reduced by 56.67％and 58.94％,respectively,and the data solved is reduced by 36.55％and 52.15％respectively compared with direct weighted average of the redundant dual IMU system.Therefore,the algorithm proposed in this paper is more accurate and robust.
万方数据
维普
