ANALYSIS OF THE COMBINED EFFECTS OF FINITE SAMPLES AND MODEL ERRORS ON ARRAY-PROCESSING PERFORMANCE

The principal sources of estimation error in sensor array signal proce ssing applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. While the effects of these errors have been studied individually, thei r combined effect has not yet been rigorously analyzed. In this paper, we undertake such an analysis for the class of so-called subspace fit ting algorithms. In addition to deriving; first-order asymptotic expre ssions for the estimation error, we show that an overall optimal weigh ting exists for a particular array and noise covariance error model. I n a companion paper, the optimally weighted subspace fitting method is shown to be asymptotically equivalent with the more complicated maxim um a posteriori estimator. Thus, for the model in question, no other m ethod can yield more accurate estimates for large samples and small mo del errors. Numerical examples and computer simulations are included t o-illustrate the obtained results and to verify the asymptotic analysi s for realistic scenarios.