基于AKPSO算法的加速度计快速标定方法

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KalmanPSO（ 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。

Rapid calibration method for accelerometer based on AKPSO algorithm

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.