ARRAY-PROCESSING IN CORRELATED NOISE FIELDS BASED ON INSTRUMENTAL VARIABLES AND SUBSPACE FITTING

Accurate signal parameter estimation from sensor array data is a probl em which has received much attention in the last decade, A number of p arametric estimation techniques have been proposed in the literature, In general, these methods require knowledge of the sensor-to-sensor co rrelation of the noise, which constitutes a significant drawback, This difficulty can be overcome only by introducing alternative assumption s that enable separating the signals from the noise, In some applicati ons, the raw sensor outputs can be preprocessed so that the emitter si gnals are temporally correlated with correlation length longer than th at of the noise. An instrumental variable (TV) approach can then be us ed for estimating the signal parameters without knowledge of the spati al color of the noise, A computationally simple IV approach has recent ly been proposed by the authors, Herein, a refined technique that can give significantly better performance is derived, A statistical analys is of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities, A lower bound on the attainable error variance is also presented, The proposed optimal IV method is s hown to attain the bound if the signals have a quasideterministic char acter.