A Novel Autocalibration of 3-Axis MEMS Accelerometers Based on Improved PSO Algorithm
This paper presents a novel procedure to calibrate the strap-down 3-axis MEMS accelerometers for UAV navigation. Firstly, we establish an explicit calibration model with the measurement values of accelerometers, where the calibration is realized via geometric transformations. Secondly, the transformation parameters are calculated through particle swarm optimization (PSO). For the problem of slower convergence rates near the global optimum, the classical PSO algorithm is improved. Based on the numerical optimization idea, the steepest descent method is introduced to PSO. The parameters are searched in the rough by adopting PSO and the precision ones are found by using steepest descent method. Then, the optimal transformation is achieved by the minimum distance function based on this improved PSO(IPSO) algorithm. Finally, the calibration procedure is tested by comparing the attitude produced by the 3-axis accelerometers with that measured by a turntable. The results show that the IPSO algorithm can significantly improve the performance of the classical PSO algorithm, and the maximum attitude error is reduced to 6％ of that before calibration. In addition, the proposed procedure does not rely on prior knowledge of the accelerometers and any equipment. So, it is suitable for calibration in field. Such a method is especially useful in UAV applications.
MEMS Accelerometer Calibration Particle swarm optimization Steepest descent algorithm
Renhao Liu Hua Wang
School of Astronautics Beijing University of Aeronautics and Astronautics Beijing, China
国际会议
2010 6th International Conference on MENS NANO,and Smart System(2010年微机电纳米、智能系统国际会议 ICMENS 2010)
长沙
英文
89-93
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）