Gaussian Mixture (GM) Passive Localization using Time Difference of Arrival (TDOA)

This paper describes the passive emitter localization using Time Difference of Arrival (TDOA) measurements. It investigates various methods for estimating the solution of this nonlinear problem: the Maximum Likelihood Estimation (ML) as a batch algorithm, the Extended Kalman Filter (EKF) as an analytical approximation, the Unscented Kalman Filter (UKF) as a deterministic sampling approach and finally the Gaussian Sum Approximation (Gaussian Mixture, GM). Different scenarios are investigated in terms of estimation accuracy, described by the Cramér Rao Lower Bound. In Monte Carlo simulations, performance and consistency of the estimation methods are analysed and compared with the Cramér Rao Lower Bound.