机动目标跟踪的滤波方法研究

A Filtering Approach for Maneuvering Target Tracking

目标跟踪应用中,一类常见的混合估计问题是:目标运动建模在直角坐标系下且是非线性的,同时量测数据由传感器直接获得。通常处理该问题的做法是使用推广卡尔曼滤波器,但效果欠佳。为此,通过将无迹变换(UT)和BLUE算法相结合,提出了一种新型的UT-BLUE滤波器。该滤波器首先利用无迹变换对直角坐标系中的目标状态及其协方差作出预测,然后在保持传感器坐标系(极坐标系)下所固有的量测误差的同时,直接对它们作出更新估计。通过仿真,将UT-BLUE滤波方法和EKF滤波方法进行比较,表明了该滤波方法的有效性和优越性。

In tracking maneuvering targets application , a type of problem encountered frequently is to estimate the state of the hybrid system in which nonlinear target motion is modeled in Cartesian coordinate system and target measurements are obtained directly in the original sensor coordinates. An approach for this problem is usually to use EKF. For this reason, a new filter, named UT - BLUE filter, is provided. In this filter, unscented transformation is used firstly to predict the state of the system and its covariance in Cartesian coordinate system, then prediction estimation is directly updated while keeping the measurement error in sensor （polar） coordinate system. Simulation is conducted to compare UT - BLUE with EKF and the results indicate that the new filter is more effective.