双机协同无源定位算法

相较于有源定位,只能利用到达角(Angle of Arrival,AOA)的双机协同无源定位对目标的观测维度很低,这会导致对目标的定位精度低或迭代收敛速度慢等问题发生。对此,利用有限的测量值研究更优秀的算法以提高定位精度具有实际意义。首先,介绍了普通最小二乘法、加权最小二乘法、总体最小二乘法 3种基于最小二乘算法的定位算法；然后,介绍了高斯-牛顿迭代法(Gauss-Newton iteration method,GNIM)和加权工具变量伪线性估计法 (Weighted Instrumental Variable Pseudolinear Estimation Method,WIVPLE)2种基于最大似然估计的定位算法；最后,使用蒙特卡洛法在不同误差条件和几何构型下对这些算法的定位精度进行仿真实验。

Dual-Aircraft Cooperative Passive Localization Algorithm

Compared with active localization, the dual-aircraft cooperative passive localization that can only use the angle ofarrival(AOA) has a very low observation dimension to the target, which will lead to problems such as low localization accura.cy of the target or slow iterative convergence speed. Therefore, it is of practical significance to use limited measurement val.ues to study better algorithms to improve localization accuracy. Firstly, three least squares-based localization algorithms, theOrdinary Least Squares, the Weighted Least Squares and the Total Least Squares, are introduced. Then, two localization al.gorithms based on maximum likelihood estimation, the Gauss-Newton iteration method and Weighted Instrumental VariablePseudo-linear Estimation method, are introduced. Finally, the Monte Carlo method is used to simulate the localization accu.racy of these algorithms under different error conditions and geometric configurations.