Abstract :
The statistical characterization of the conditioned signal-to-noise ratio (SNR) of the sample matrix inversion (SMI) method has been known for some time. An eigenanalysis-based detection method, referred to as the eigencanceler, has been shown to be a useful alternative to SMI, when the interference has low rank. In this work, the density function of the conditioned SNR is developed for the eigencanceler. The development is based on the asymptotic expansion of the distribution of the principal components of the covariance matrix. It is shown that, unlike the SMI method, the eigencanceler yields a conditional SNR distribution that is dependent on the covariance matrix, It is further shown that simpler, covariance matrix-independent approximations can be found for the large interference-to-noise case. The new distribution is shown to be in good agreement with the numerical data obtained from simulations.
Keywords :
adaptive antenna arrays; array signal processing; covariance matrices; eigenvalues and eigenfunctions; probability; asymptotic distribution; conditional signal-to-noise ratio; covariance matrix; density function; eigenanalysis-based adaptive array; eigencanceler; matrix-independent approximations; Adaptive arrays; Colored noise; Covariance matrix; Density functional theory; Detectors; Interference constraints; Maximum likelihood detection; Signal to noise ratio; Subspace constraints; Vectors;
