Detection of weak signals hidden beneath the noise floor with a modified principal components analysis

Detecting signals hidden beneath the noise floor is a challenging task. As the signal-to-noise ratio (S/N) dips below 0, false alarms and detection misses become a serious problem. Furthermore, to satisfy the real-time or near real-time requirement, detection schemes that are computationally intensive do not enjoy wide-spread adoption. In this paper, we present a new detection algorithm consisting of phase-space reconstruction technique and principal components analysis. The goal is to achieve the detection of weak signals in noisy environments. With the new algorithm, our study shows that in addition to detection, the frequency of the signal can be extracted even when the S/N reaches negative value and the FFT power spectrum shows no trace of its spectral characteristics. The signal detection scheme is insensitive to the nature of the background noise, making it viable to achieve good performance in various signal application domains. In this paper, we chose to report on the results pertaining to the analysis of time series from IPIX radar. The new detection algorithm is also computationally lean, thus enabling its use in real-time applications