Abstract—Acoustic signals on transmission in underwater channels are often prone to corruption by ambient noises, wind interference and other random sources of disturbance. Adaptive filters can be used to extenuate the effects of ambient noise in acoustic signals. An effective technique for denoising the degraded modulated acoustic signals using adaptive filters has been proposed. Adaptive 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 ranging from 2m/s to 6m/s. From the simulation, it is observed that the KLMS filter converges to the desired useful signal faster than the other adaptive filter techniques. This result is further supported by the fast converging Mean Square Error (MSE) signal of KLMS compared to the other adaptive filter techniques discussed.

 

Index Terms—adaptive filters, mean square error, signal to noise ratio, underwater acoustic signal

Cite: S. S. Murugan, S. Prethivika, and V. Natrajan, "Analysis on Extraction of Modulated Signal Using Adaptive Filtering Algorithms against Ambient Noises in Underwater Communication," International Journal of Signal Processing Systems, Vol. 3, No. 1, pp. 25-29, June 2015. doi: 10.12720/ijsps.3.1.25-29