NAR ESTIMATORS OF SPATIAL COVARIANCE MATRICES FOR ADAPTIVE ARRAY DETECTION

The maximum likelihood estimator of a noise field spatial covariance matrix is much perturbed by signal presence. The use of this estimator in adaptive array detection causes SNR loss that increases monotonically with input SNR. This correspondence extends the theory of noise-alone-reference (NAR) power estimation to the estimation of spatial covariance matrices. A NAR covariance estimator insensitive to signal presence is derived. The SNR loss incurred by using this estimator is independent of the input SNR and is less than that encountered with the maximum likelihood covariance estimator given that the same number of uncorrelated snapshots is available to both estimators. The analysis assumes first a deterministic signal. The results are extended and generalized to signals with unknown parameters or random signals. For the random signal case, generalized and quasi-NAR covariance estimators are presented.