Multi-Sensor Beamsteering Based on the Asymptotic Likelihood for Colored Signals
In this paper, the authors derive a maximum likelihood formula for beam-steering in a multi-sensor array. The impinging signal and noises are Wide Sense Stationary (WSS) time series with unknown power spectral densities, unlike in previous work that typically considers white signals. Their approach naturally provides a way of fusing frequency-dependent information to obtain a broadband beamformer. In order to obtain the compressed likelihood, it is necessary to find the maximum likelihood estimates of the unknown parameters. However, this problem turns out to be an ML estimation of a block-Toeplitz matrix, which does not have a closed-form solution. To overcome this problem, they derive the asymptotic likelihood, which is given in the frequency domain.