一种用于目标跟踪的UT-BLUE滤波方法

雷达机动目标跟踪问题中,通常目标运动模型可精确地在直角坐标系下建模,但大多数情形下模型是非线性的,同时在传感器坐标系下所获得目标量测又是直接可用的.通过将无迹变换与最优线性无偏滤波器有机结合,提出一种新的BLUE(Best Linear Unbiased Estimator) 滤波算法,以便解决上述非线性跟踪问题.首先,该算法利用无迹变换对经由直角坐标系下非线性目标运动模型得到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,直接对它们作出状态估计.在算法推导及Monte-Carlo仿真过程中,将新的BLUE滤波算法和EKF(Extended Kalman Filter)、UKF(Unscented Kalman Filter)滤波算法进行比较,结果表明新算法的有效性和适用性.

UT-BLUE filter for target tracking

In tracking maneuvering targets application with radar,target dynamics are usually modeled in the Cartesian coordinates.In the cases target motion model are always very accurate but nonlinear,while target measurements are directly available in the original sensor coordinates.By means of combinatin of unscented transformation and best linear unbiased filter,a new filter named unscented transformation-best linear unbiased estimator(UT-BLUE) filter was proposed to solve the above nonlinear tracking problem.In this filter,by way of nonlinear target motion model,unscented transformation was first used to predict state of the true target and its covariance,and then they were directly estimated while keeping the measurement error in sensor(polar) coordinate system.Algorithm analysis and simulation were conducted to compare it with extended Kalman filter(EKF) and unscented Kalman filter(UKF),and results indicate that the new filter is more effective and available.