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.