基于移动长基线和误差修正算法的多UUV协同导航

在移动长基线（MLBL）定位结构中,虽可利用基于水声传播延迟（TOF）原理获取的量测信息和贝叶斯滤波器（如扩展卡尔曼滤波（EKF））提高低自定位能力无人水下航行器（UUV）的定位精度,但较高的测量误差会降低这种提高的幅度.根据水声通信的特点提出了一种相关性假设并构建了误差修正算法（ECA）,在设定条件下利用误差间的相关性减小量测误差,从而实现量测的粗估计.仿真结果表明,先粗估计量测值再结合贝叶斯滤波器,可显著提高配备低精度自定位传感器的UUV的定位精度.

Cooperative navigation based on moving long baselines and error correction algorithm for multiple UUVs

In the localization structure of moving long baselines(MLBL),although the measurement information,which can be got by using the acoustic propagation time of flight(TOF) and Bayesian filters such as the extended Kalman filter(EKF),is utilized to improve the localization accuracy of a low self-localization capability unmanned underwater vehicle(UUV),the higher measurement errors will reduce the extent of this improvement.A correlation assumption is proposed and the error correction algorithm(ECA) is constructed according to the characteristics of the underwater acoustic communication.Under the setting conditions,the measurement errors are depressed by using of the correlation between the errors,and the rough estimates of the measurements are achieved.The simulation results show that the localization accuracy of the UUV equipped with low precise proprioceptive localization sensors can be improved significantly by combining the measurement rough estimates with one of Bayesian filters.