结合初值选取的最大似然估计被动定位算法(英文)

Maximum Likelihood Estimation Algorithm Combining with Initial Value Selection for Passive Localization

针对传统最大似然被动定位算法(MLE)在定位过程中需要设定初始位置的问题,提出了一种改进的MLE定位算法。首先采用最小二乘法计算目标初始位置。此外,为了适应量测误差,将其与传感器位置之间的差方根作为传感器测量误差的近似加权矩阵,再使用加权最小二乘公式,估计新的目标位置。最后,将该估计值作为初始值,使用传统MLE算法获得最终定位结果。改进算法无需设定初始目标位置,运算过程不易发散,时间复杂度不高,取得的定位精度和传统MLE算法相同。仿真结果表明了改进算法的有效性。

The problems of conventional Maximum Likelihood Estimation （MLE） algorithm are addressed for passive localization and an improved MLE passive localization algorithm is presented. Firstly, we estimate an initial target position using least square method. Moreover, in order to adapt the measurement error, the square root of the difference between the estimated position and the sensor＇s position is used as the approximate covariance matrix for measurement error. Then, a weighted least square formula is employed to estimate a new position. Finally, we regard the estimation value as the necessary initial value, and employ the conventional MLE to calculate the final results. The improved algorithm have some advantages, i.e., it does not need to set an initial target position, its localization results do not diverge easily, its computational complexity is low, and it has the same level in accuracy as that of conventional MLE. Experimental results show that the proposed algorithm is effective.