A multiresolution generalized maximum likelihood approach for the detection of unknown transient multichannel signals in colored noise with unknown covariance

The problem of detecting transient signals of unknown waveforms has been widely studied in recent years due to the numerous applications associated with it. In particular, the detection of neural spikes using a recording array of closely spaced sensors in the brain is one such application. In this paper, we propose a new approach for solving this problem when no apriori knowledge is given about the signal and/or the noise processes. By extending our previous multiresolution analysis framework for noise suppression and source identification [1,2], we show that it is feasible to achieve reasonable detection performance in low SNR scenarios. Comparison to traditional detection schemes is presented and the overall performance is evaluated.