Analysis of adaptive filtering techniques for fresh water dolphin signals in their natural habitat

Abstract

Tropical shallow waters typically present poor Signal to Noise Ratio (SNR) for any underwater system. Fresh water habitats experience heavy boat traffics due to significant human encroachments and sharing of habitat with other local species. The Irrawaddy dolphins are known to be facultative species with the significant human presence in these habitats. The boat traffic in their habitat is an important source of noise that degrades their acoustic habitat and even impacts the performance of sonars deployed for monitoring their activities and habitat of these species. The dynamic underwater channel fluctuations of the shallow tropical waters make the design of filters extremely complicated for any SNR enhancement initiative. The dynamic nature of the marine channel originates from the time, frequency and spatial fluctuations of the tropical shallow water environment with varying boundary conditions and multiple boundary interactions as the signal propagates from the source to the receiver. It is unable to track the changes of signal and noise using the fixed coefficient filter in such applications. This work attempts to compare the performance of two adaptive filters to enhance the SNR for dolphin signals in such ambient noise conditions. The two adaptive algorithms LMS (Least Mean Squares) and NLMS (Normalized Least Mean Squares) have been evaluated by comparing the performance parameters such as SNR (Signal-to-Noise Ratio) and MSE (Mean Square Error). The input signal in the work is Irrawaddy dolphin signal in Chilika Lake (19.8450° N, 85.4788° E), that is degraded due high boat traffic of dolphin watching tourist boats. The spectral and the temporal characteristics of recovered signal is verified using the spectrogram method. The simulation study is undertaken using available dolphin click signal and boat noise to be able to identify the precise SNR of the signal at receiver for accurate performance evaluation of the proposed noise mitigation algorithms. This comparative study shows that the NLMS is a better adaptive algorithm for filtering and thus, can improve the performance of a sonar system.