Source Power Estimation for Array Processing Applications under Low Sample Size Constraints

This paper proposes a new power estimation technique for array processing applications in the low sample size regime. The technique is especially suitable for applications where the direction of arrival (DoA) detection is performed using subspace identification techniques, because the eigenvalues and eigenvectors of the sample covariance matrix are already computed for DoA estimation and are therefore available for power estimation as well. The performance of the algorithm is similar to that of the traditional maximum likelihood (ML) power estimation technique, but it is more robust to the presence of outliers in the direction of arrival (DoA) detection process. This is because, contrary to the ML estimator, the proposed power estimator only depends on the signature of the source of interest.