多普勒测速声呐的自适应Kalman滤波算法

An adaptive Kalman filter algorithm for a Doppler velocity log

针对小型自主水下无人潜器的运动特点,设计了一种加速度信息辅助的自适应Kalman滤波器,用于多普勒测速声呐的数据处理.首先基于时间序列分析法,建立了多普勒测速声呐噪声信号的AR模型,并根据当前统计模型设计了多普勒测速声呐Kalman滤波方程.然后针对滤波方程特点,设计了基于S面的自适应Kalman滤波器.实验结果表明,在已知加速度先验信息的条件下,基于S面的自适应Kalman滤波器能够根据小型水下机器人的运动特点实时调整滤波参数,滤波精度优于0.04m/s,且能够有效消除时间延迟,为小型水下机器人控制系统提供准确及时的速度信息.

An adaptive Kalman filter aided by acceleration information was designed for small autonomous underwa- ter vehicle manoeuvre characteristics. First, an AutoRegressive （AR） model of Doppler velocity log noise was a- chieved using the time series analysis method. Second, based on the current statistical model, the Kalman filter e- quation for a Doppler velocity log was designed. Finally, an adaptive Kalman filter based on the S plane was de- signed for the former filter~ equation characteristics. The experimental results show that when the prior information on acceleration is known, the adaptive Kalman filter based on the S plane has the ability of adjusting filter parame- ters in real time according to the manoeuvre characteristics with an accuracy that is within 0.04m/s. Furthermore, the time delay can be effectively eliminated, and accurate and timely rate information can be provided for small au- tonomous underwater vehicles.