KEYWORDS: Associative arrays, Signal detection, Reconstruction algorithms, Compressed sensing, Signal to noise ratio, Signal processing, Interference (communication), Electroluminescence, Surveillance systems, Algorithm development
Detection and estimation of wideband radio frequency signals are major functions of persistent surveillance systems and
rely heavily on high sampling rates dictated by the Nyquist-Shannon sampling theorem. In this paper we address the
problem of detecting wideband signals in the presence of AWGN and interference with a fraction of the measurements
produced by traditional sampling protocols. Our approach uses learned dictionaries in order to work with less restriction
on the class of signals to be analyzed and Compressive Sensing (CS) to reduce the number of samples required to
process said signals. We apply the K-SVD technique to design a dictionary, reconstruct using a recently developed
signal-centric reconstruction algorithm (SSCoSaMP), then use maximum likelihood estimation to detect and estimate the
carrier frequencies of wideband RF signals while assuming no prior knowledge of the frequency location. This solution
relaxes the assumption that signals are sparse in a fixed/predetermined orthonormal basis and reduces the number of
measurements required to detect wideband signals all while having comparable error performance to traditional detection
schemes. Simulations of frequency hopping signals corrupted by additive noise and chirp interference are presented.
Other experimental results are included to illustrate the flexibility of learned dictionaries whereby the roles of the chirps
and the sinusoids are reversed.