A new method is introduced for sequential estimation of TDOA (time delay of arrival) and FDOA (frequency delay of arrival) in a two sensor array. The proposed scheme is basically a two step algorithm utilizing 1-dimensional slice functions of the third order cumulants between two signal measurements, and is capable of suppressing the effect of correlated Gaussian measurement noises. It is demonstrated that the proposed algorithm outperforms existing TDOA/FDOA estimation algorithms from the viewpoint of computational burden and in the sense of mean squared error as well.

This paper concentrates on the model useful for analyzing the error performance of M-estimators of a single unknown signal parameter: that is the error intensity model. We develop the point process representation for the estimation error, the conditional distribution of the estimator, and the distribution of error candidate point process. Then the error intensity function is defined as the probability density of the estimate and the general form of the error intensity function is derived. We compute the explicit form of the intensity functions based on the local maxima model of the error generating point process. While the methods described in this paper are applicable to any estimation problem with continuous parameters, our main application will be time delay estimation. Specifically, we will consider the case where coherent impulsive interference is involved in addition to the Gaussian noise. Based on numerical simulation results, we compare each of the error intensity model in terms of the accuracy of both error probability and mean squared error (MSE) predictions, and the issue of extendibility to multiple parameter estimation is also discussed.