Michigan State University - Research Groups

The problem of detecting the number of sources impinging on an array of sensors has received wide interest in many research problems. In particular, the detection of the number of distinct neural sources using a recording array of closely spaced sensors in the brain is one such application. The special case of transient source signals of unknown waveforms corrupted by Gaussian noise is the focus of this paper. We propose a new approach for solving this problem when no apriori knowledge is given about the neural sources and/or the noise processes. By extending our previous array multiresolution analysis framework for noise suppression, signal detection and identification [1-4], we show that it is feasible to achieve reasonable source detection performance in moderate to low SNR scenarios. Comparison to traditional detection schemes is presented.