Improved estimation of minimum variance beamformer with small number of samples
Antoine Souloumiac · 2002
We address the problem of using an array of sensors for separating the signal emitted by a narrowband source from unwanted disturbance signals (jammers and noise). The source of interest steering vector is assumed to be known exactly and the goal is to estimate the signal of interest with the maximum signal to interference plus noise ratio (SINR). The classical solution to this problem is the minimum variance beamformer (MVB). But the classical estimator of this spatial filter shows poor performance for small number of samples. We demonstrate by means of a Cramer-Rao bound calculation that every unbiased estimator of the MVB achieves low SINR, and we propose a new biased estimator which shows improved performance with small number of data.