摘要
在卡尔曼滤波的基础上,引入粒子群优化算法,对卡尔曼滤波方法进行改进,提出基于粒子群优化的卡尔曼滤波器模型,从而提高水下机器人测量数据的精度,降低系统噪声和量测噪声所带来的误差。水池仿真试验结果表明改进的滤波方法有效、实用。
A Kalman filter model based on particle swarm optimization （PSO） algorithm was proposed, and the filtering precision of underwater vehicle measurements was improved to reduce the measurement noise. This filtering method is proved to be effective by pool simulation test for Gyro Sensor of OUTLAND1000 underwater vehicles.
出处
《船海工程》
2010年第1期99-102,共4页
Ship & Ocean Engineering
关键词
卡尔曼滤波器
粒子群算法
水下机器人
信号处理
Kalman filter
particle swarm optimization
underwater vehicles
signal processing
