A Novel Closed-Form Estimator for AOA Target Localization Without Prior Knowledge of Noise Variances

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.