Multi-wavelet detection and denoising of low-frequency chirp signals using adaptive wavelet methods

Abstract

The detection and classification of underwater acoustic signals embedded in noise is a fundamental problem of interest to the signal processing community. The use of wavelet transforms is a recent development in digital signal processing which has been applied in many different areas. A particular type of wavelet is the chirplet, which includes frequency variation as well as time shift and scaling. Both linear and polynomial chirp signals are present in underwater acoustic signals generated by such sources as biologics, ships, and submarines. Distinguishing the features of these chirps relative to other ambient noise shows promise as an initial step in classification of underwater acoustic signals. Removal of unwanted broadband signal components via wavelet methods has been shown to outperform other noise removal processes such as low-pass and high-pass filtering and Weiner filtering. Examples of low-frequency simulated chirp signals with additive noise have been generated. A multi-wavelet packet method for detection and denoising low-frequency signals containing multiple chirps embedded in noise using a specific wavelet designed for polynomial chirp signals is shown. [Research supported in part by ONR.]