Efficient Sub-Regional Multiple-Source Detection Based on Subspace Matrix Filtering

We present an efficient approach for the sub-regional multiple-source detection. The essence of this approach is to extract the characteristic components of the signals of interest (SOIs) from the estimated signal-plus-interference subspace by a matrix filter. Compared with some other spatial filtering-based approaches, it has two significant advantages. First, since the power of each source in the signal-plus-interference subspace is normalized, the proposed approach is effective to filter out interferences regardless of their strengths. Second, the matrix filter would not reduce the dimension of SOIs, thus, the proposed approach is able to distinguish multiple SOIs from the output of the matrix filter.