Combined AOA and TDOA Target Localization Method Under Distance-Dependent Noise Model

This article focuses on the problem of target localization in 2-D using a combination of angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements. Unlike most existing studies, the measurement noises are assumed as the distance-dependent model in this article. Specifically, the variances of the noises are functions of the distance between the target and the sensor. The maximum likelihood estimator (MLE) and the Cramer–Rao lower bound (CRLB) are derived under the distance-dependent noise. By linearizing the nonlinear measurement equations for the unknown target, an AOA/TDOA pseudo-linear estimator (PLE) is established. Then, a new estimator with a closed-form solution is proposed by taking full advantage of the instrumental variable (IV) approach and the generalized pseudo-linear equation, which can improve the localization performance in the absence of noise variances. The simulation results demonstrate that the proposed estimator is superior to the MLE, IV estimator (IVE), and PLE.