自适应卡尔曼滤波技术在水下机器人运动控制中的应用

Application of Adaptive Kalman Filter in Underwater Vehicle Motion Control

水下机器人的位置和速度传感器受环境影响较大,数据滤波问题是运动控制的核心问题之一。建立了水下机器人运动的状态方程和量测方程,并在此基础上采用自适应卡尔曼滤波方法对水下机器人的传感器数据进行了滤波分析,并引入渐消记忆指数加权方法,避免了系统误差和量测误差统计特性的不准确对系统滤波效果的影响。结果表明此方法能达到很好的滤波效果。

Underwater vehicle（UV） position and velocity sensors are affected by environment. Data filtering is one of the most important problems of AUV motion control. AUV state equation and measuration equation are founded. Data from UV sensors are disposed by adaptive Kalman filter. With fading exponent, this method avoids inaccuracy caused by system error and measuration error. The result shows this method is effective.