This paper addresses the problem of target localization using angle-of-arrival (AOA) measurements when the prior information of the AOA measurement noise variance is unavailable. At first, a maximum likelihood estimator (MLE) and the Cramér–Rao lower bound are derived for the case where the unknown noise variance is a function of the target-to-sensor distance. Then, a novel estimator is proposed to obtain a closed-form solution without the knowledge of noise variance. The proposed estimator can efficiently improve the localization performance by fully exploiting the desirable advantages of the instrumental variable (IV) method and the set of generalized pseudolinear equation. The simulation results show the superior performance of the proposed estimator compared with the MLE, the IV estimator and the pseudolinear estimator.

当无法获得AOA测量噪声方差的先验信息时，本文解决了使用到达角（AOA）测量进行目标定位的问题。首先，针对未知噪声方差是目标到传感器距离的函数的情况，得出最大似然估计器（MLE）和Cramér-Rao下限。然后，提出了一种新颖的估计器，以在不了解噪声方差的情况下获得封闭形式的解决方案。所提出的估计器可以通过充分利用工具变量（IV）方法和广义伪线性方程组的理想优势来有效地提高定位性能。仿真结果表明，与MLE，IV估计器和伪线性估计器相比，该估计器具有更好的性能。