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基于到达方位角(DOA)和到达时间差(DTOA)等观测信息,实现了单个固定观测站对运动辐射源无源定位和跟踪。为克服扩展卡尔曼滤波(EKF)算法线性化过程对预测误差放大,导致定位结果发散或不稳定的缺陷,引出了迭代扩展卡尔曼滤波(IEKF)算法。在获得某个预测值后,通过多次更新状态估计,使之逐渐拟合当前观测量,以提高目标状态估计的精度。仿真结果表明,在一定的观测误差条件下,IEKF算法比EKF算的定位跟踪收敛速度更快、精度更高。
Based on the observation information such as DOA and DTOA, a single stationary observatory is used to locate and track the moving radiation sources. In order to overcome the shortcoming that EKF algorithm linearizes the prediction error and cause the localization result to be divergent or unstable, an iterative Extended Kalman Filter (IEKF) algorithm is introduced. After a certain prediction value is obtained, the state estimation is updated several times so as to gradually fit the current observation to improve the accuracy of the target state estimation. The simulation results show that the IEKF algorithm converges more quickly and has higher accuracy than the EKF algorithm under some observational errors.