ABSTRACT

In passive underwater target tracking, the observer uses bearings-only measurements generated by radiation of the target. The measurements are always corrupted with noise, which creates an uncertainty zone around the target position. Unscented Kalman filter (UKF) is proved to be efficient nonlinear estimator to estimate the target motion parameters. It is of interest to know in many practical situations, regarding the convergence of the solution in terms of the reduction of the uncertainty zone within some specified limit. An effort is made to reduce and find the range uncertainty ellipse zone of the target using UKF covariance matrix in Monte Carlo simulation. Once the range uncertainty ellipse zone becomes less than a specified value, the solution is said to be converged.

ABBREVIATIONS: RUEZ: Range Uncertainty Ellipse Zone; UKF: Unscented Kalman Filter; UT: Unscented Transformation; LHMA: Length of Half Major Axis

摘要

在被动水下目标跟踪中，观察者使用由目标辐射产生的仅方位测量。测量总是被噪声破坏，这会在目标位置周围产生一个不确定区域。无迹卡尔曼滤波器（UKF）被证明是一种有效的非线性估计器来估计目标运动参数。在许多实际情况下，了解在某个特定限制内不确定性区域减少方面的解决方案收敛性是很有趣的。在蒙特卡罗模拟中使用UKF协方差矩阵，努力减少和找到目标的距离不确定性椭圆区。一旦范围不确定性椭圆区域变得小于指定值，则称解是收敛的。

缩写： RUEZ：范围不确定性椭圆区；UKF：无味卡尔曼滤波器；UT：无味转型；LHMA：半长轴的长度