Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region

Abstract

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.