Detection of narrowband random signals via spectrum matching

Detection of narrowband signals is of interest in many areas such as radar and communications. The Bartlett method is the conventional method to tackle this problem but it uses solely the peak information in the periodogram even when the signal structure is partially known. Using a priori knowledge of the signal power spectral density (PSD), a spectrum matching approach which effectively utilizes the available signal spectral shape is developed for narrowband signal detection in this paper. Two spectrum matching detector structures, which are practically implemented by correlogram and periodogram respectively, are examined. Theoretical calculation of their false alarm rates is derived and confirmed by simulations. It is also demonstrated that the proposed detectors outperform the standard periodogram and Bartlett method under constant false alarm rate (CFAR) condition for different models of narrowband signals.