基于AKPSO算法的加速度计快速标定方法  

Rapid calibration method for accelerometer based on AKPSO algorithm

作　　者：戴邵武[1] 王克红[1] 钱俭学 

机构地区：[1]海军航空工程学院控制工程系 [2]92349部队

出　　处：《传感器与微系统》2015年第2期69-72,共4页Transducer and Microsystem Technologies

基　　金：中国博士后科学基金资助项目(2013M532173);航空科学基金资助项目(20135184007)

摘　　要：针对粒子群优化(PSO)算法在加速度计标定优化后期出现的早熟、陷入局部最优的不足,以及Kalman PSO(KPSO)算法在设计与应用过程中存在的缺陷,提出了基于自适应Kalman滤波的改进PSO(AKPSO)算法,并将其成功应用于加速度计快速标定。利用粒子群状态空间Markov链模型,建立了粒子群系统状态方程和观测方程;采用指数加权的自适应衰减记忆Kalman滤波来对粒子的位置进行估计。加速度计标定仿真结果表明:所提出的算法在收敛速度、收敛精度方面都要优于PSO,KPSO算法,有效地提高了加速度计的标定精度。Aiming at premature and trapped in a local optimum which appeared in calibration optimization of accelerometer based on particle swarm optimization（ PSO） algorithm and insufficience which existed in design and application process of Kalman PSO（ KPSO） algorithm,an improved PSO algorithm based on adaptive Kalman filtering（ AKPSO） is proposed and it is applied successfully to fast calibration of accelerometer. Using particle swarm state space Markov chain model,state equation and observation equation of particle swarm system are established; exponentially weighted adaptive attenuation memory Kalman filtering is used to estimate position of particle. Simulation result of accelerometer calibration shows that the proposed algorithm is better than PSO and KPSO algorithm in both convergence speed and convergence precision,and it can effectively improve calibration precision of accelerometer.

关 键 词：加速度计标定 自适应Kalman粒子群优化 MARKOV链模型 

分 类 号：U666.1[交通运输工程—船舶及航道工程] TP18[交通运输工程—船舶与海洋工程]