Knowledge-aided adaptive beamforming

In array processing, when the available snapshot number is comparable with or even smaller than the sensor number, the sample covariance matrix R is a poor estimate of the true covariance matrix R. To estimate R more accurately, we can make use of prior environmental knowledge, which is manifested as knowing an a priori covariance matrix R₀. In this paper, we consider both modified general linear combinations (MGLC) and modified convex combinations (MCC) of the a priori covariance matrix R₀, the sample covariance matrix R, and an identity matrix I to get an enhanced estimate of R, denoted as R. Numerical examples are provided to demonstrate the type of achievable performance by using R instead of R in the standard Capon beamformer.