超视距目标跟踪的卡尔曼滤波算法研究

Research on Kalman filtering algorithm for target tracking over the horizon

针对有界范围内的运动目标进行超视距跟踪时出现的量测不可靠问题,提出一种卡尔曼滤波初值选取方案,对采用三点法求得超出界限的目标初始运动状态进行修正,再将其作为新的滤波初值,这一方法称为投影修正法。对比传统初值确定方法,并结合3种传统非线性卡尔曼滤波算法,分别在目标与观测台的初始距离为600和1000时进行仿真验证。仿真结果显示,与传统方法相比,应用该初值确定方法能在探测距离相对误差为1%,探测角度误差为0.01rad时,明显提高滤波初期收敛速度且滤波精度不下降。此外,研究还发现,在利用投影修正法进行跟踪滤波时,同等条件下选用零值修正,收敛效果更好。

The unreliable problem of measurement in over the horizon tracking of the objects whose motion states are upper bounded is mainly focused on.A new approach of initial value selection for Kalman tracking filter is pro?posed,which is called projection correction method.If the initial value selection of the target obtained by the three-point method is out of the range,it will be repaired and is used as a new filtering initial value.It??s a new ap?proach of initial value selection for Kalman tracking filter,which is called projection correction method.Compared to the traditional initialization method,the proposed initialization method is combined with three traditional non?lin?ear Kalman filter algorithms in the simulation.The tests are conducted when the initial distance between the target and the observatory are 600 and 1 000 km,respectively.The simulation results show that compared with the tradi?tional method,the initialization method can significantly improve the initial convergence speed and the filtering ac?curacy when the relative error of detection distance is 1%and the detection angle error is 0.01 rad.In addition,it is also found that when the projection correction method is used for tracking filtering,correction with zero-value can get better convergence effect under the same conditions.