Deterministic Performance Bounds on the Mean Square Error for Near Field Source Localization

This correspondence investigates lower bounds on estimator's mean square error applied to the passive near field source localization. More precisely, we focus on the so-called threshold prediction for which these bounds are known to be useful. We give closed form expressions of the McAulay-Seidman, the Hammersley-Chapman-Robbins, the McAulay-Hofstetter bounds and also, a recently proposed bound, the so-called Todros-Tabrikian bound, for the deterministic observation model (i.e., parameterized mean) and the stochastic observation model (i.e., parameterized covariance matrix). Finally, numerical simulations are given to assess the efficiency of these lower bounds to approximate the estimator's mean square error and to predict the threshold effect.