Full spectrum signal processing

In order to effectively deal with quiet source emissions and elevated ambient noise level in littoral waters, it is important that we understand the background ambient noise characteristics and exploit the underlying signal microstructure. Our experiences indicate that thorough understanding of noise and signal structures is a key to designing robust shallow water signal processing algorithms. Therefore, we design classify-before-detect algorithms and evaluate their performance with passive broad-band (PBB) data corrupted by the SWell-EX1 shallow water ambient noise collected near San Diego, CA. Our processing strategy is based on (1) exploitation of any microstructure present in target signature by projecting raw data onto appropriate low dimensional projection spaces, (2) identification of key parameters or “features” crucial in determining the presence of signal, and (3) designing a classifier topology that best matches the underlying feature distribution. Full spectrum signal processing algorithm design is facilitated by the use of an integrated classification paradigm that takes advantage of an inherent relationship between low dimensional features and classifier architecture. Our analyses show that taking advantage of the PBB microstructure improves detection performance by an average of 5 to 15 dB over that of a traditional energy detector