Robust blind adaptive beamforming by maximum likelihood with EM algorithm

In array signal processing, blind beamforming plays a crucial role nowadays, since it does not depend on the assumption of known source signal bandwidth and the interference/noise spectral shape. Blind beamforming can be achieved in the sense of maximum likelihood. However, it would involve a complicated adaptation procedure. We derive an expectation-maximization (EM) blind beamforming algorithm and successfully avoid the high computational complexity usually associated with other existing EM procedures. In a wide variety of signal-to-noise ratio conditions, our new algorithm greatly outperforms the popular blind beamformer based on the constant-modulus criteria according to the Monte Carlo experiments.