Project Description

The contribution of this work is the development of signal enhancement for underwater channels against ambient noise. The methods adapted for the signal enhancement is the use of adaptive algorithms that have fast and stable convergence and implementation of the adaptive algorithms in hardware. The efficacy of the proposed techniques is verified with real time noise data, collected from the shallow region of the Bay of Bengal. A family of adaptive filtering algorithms is applied for noise reduction in an underwater environment. The Least Mean Square (LMS), Normalized LMS (NLMS) and Kalman based Normalized Least Mean Square (KLMS) adaptive algorithms are analyzed in terms of their performance, with the aid of performance measure characteristics such as Signal to Noise Ratio (SNR) and Mean Square Error (MSE). The analysis is carried out for source signals with a frequency range of 100 Hz to 10 KHz, and the algorithms are designed to reconstruct the source signals against the presence of ambient noise in this frequency range. Finally, the KLMS adaptive filtering algorithms are implemented in hardware for real time applications and the results are validated.