A Novel Autocalibration of 3-Axis MEMS Accelerometers Based on Improved PSO Algorithm

Abstract

This paper presents a novel procedure to calibrate the strap-down 3-axis MEMS accelerometers for UAV navigat-ion. Firstly, we establish an explicit calibration model with the measurement values of accelerometers, where the calibration is realized via geometric transformations. Secondly, the transfor-mation 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.