UKF方法及其在方位跟踪问题中的应用

采用UKF(Unscented Kalman Filter)方法处理了平面内地面站对目标的方位跟踪的估计问题。目标的位置和速度由选定的高斯分布采样点来近似，在每个更新过程中，采样点随着状态方程传播并随着非线性测量方程变换，由此不但得到目标位置和速度的均值及较高的计算精度，而且避免了对非线性方程的线性化过程。仿真结果表明，UKF方法比传统的扩展卡尔曼滤波(EKF)算法有更高的估计精度，并能有效地克服非线性严重时，方位跟踪问题中很容易出现的滤波发散问题。

UKF and Its Application to Bearings-Only Tracking Problem

An application of the unscented Kalman filter (UKF) to the two dimensional bearings only tracking (BOT) problem in passive target tracking from a ground station is presented. The target position and velocity estimates are approximated by a Gaussian distribution which is specified by a set of deterministically chosen sample points. At each update, the sample points are propagated through the state equation and then transformed through the nonlinear bearings measurement equation. From these sample points, the posterior mean and covariance of the target position and velocity are computed accurately to the second order. The linearization of the nonlinear equations necessary for the extended Kalman filter( EKF) is not needed. The simulation results show that in the BOT problem this UKF outperforms the standard EKF in accuracy and divergence performance.