应答器未校准情况下的水下长基线定位方法研究

A New and Better Method of Underwater LBL Localization with Unsurveyed Transponders

针对应答器未校准情况下的水下长基线定位问题,提出了基于无迹卡尔曼滤波的同步定位与地图创建方法。应用随机地图技术,将自主水下航行器的位置坐标和应答器的位置坐标组成增广状态矢量,以到应答器的距离为测量值,用无迹卡尔曼滤波进行求解。该方法是一种实时在线算法,充分利用了速度和航向信息,克服了非线性方程方法存在的状态矢量维数高、求解易发散的问题,并且对水下航行器的运动方式没有约束。仿真结果表明,它能够抑制航位推算法定位的累积误差,提供水下长期的、误差有界的定位信息。

Newman et al studied the problem of underwater LBL （long base-line） localization of AUV （autonomous underwater vehicle） with unsurveyed transponders. Their method has many shortcomings discussed in the full paper. We aim to eliminate these shortcomings as much as possible with a different and we believe better method by using localization and mapping ） technique. the unscented Kalman filter （UKF） based SLAM （simultaneous Adopting the stochastic mapping method, we combine the coordinates of the AUV and transponders into one generalized state vector, which is estimated using UKF with measurements of the distances of the AUV to the transponders. This method is a real-time one and is superior to the method of Ref. 2, which solves the nonlinear measurement equation under constraints of the AUV＇s kinematics. The simulation results show preliminarily that： （1） our new method doses eliminate much of the shortcomings of Ref. 2; （2） the localization error of AUV is bounded and within ten meters, which is much smaller than those obtained by dead-reckoning and original LBL methods; （3） the estimation error of the location of each transponder also converges to a very small value of a few meters.