Improved signal de-noising in underwater acoustic noise using S-transform: A performance evaluation and comparison with the wavelet transform

Sound waves propagate well underwater making it useful for target locating and communication. Underwater acoustic noise (UWAN) affects the reliability in applications where the noise comes from multiple sources. In this paper, a novel signal de-noising technique is proposed using S-transform. From the time-frequency representation, de-noising is performed using soft thresholding with universal threshold estimation which is then reconstructed. The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones. The comparison is made with the more conventionally used wavelet transform de-noising method. Two types of signals are evaluated: fixed frequency signals and time-varying signals. The results demonstrate that the proposed method shows better signal to noise ratio (SNR) by 4 dB and lower root mean square error (RMSE) by 3 dB achieved at the Nyquist sampling frequency compared to the previously proposed de-noising method like wavelet transform. (C) 2017 Shanghai Jiaotong University. Published by Elsevier B.V

Error Performance Analysis in Underwater Acoustic Noise With Non-Gaussian Distribution

There is a high demand for underwater communication systems due to the increase in current social underwater activities. The assumption of Gaussian noise allows the use of Traditional communication systems. However, the non-Gaussian nature of underwater acoustic noise (UWAN) results in the poor performance of such systems. This study presents an experimental model for the noise of the acoustic underwater channel in tropical shallow water at Desaru beach on the eastern shore of Johor in Malaysia, on the South China Sea with the use of broadband hydrophones. A probability density function of the noise amplitude distribution is proposed and its parameters defined. Furthermore, an expression of the probability of symbol error for binary signalling is presented for the channel in order to verify the noise effect on the performance of underwater acoustic communication binary signalling systems

Diurnal Variability Of Underwater Acoustic Noise Characteristics in Shallow Water

The biggest challenge in the underwater communication and target locating is to reduce the effect of underwater acoustic noise (UWAN). An experimental model is presented in this paper for the diurnal variability of UWAN of the acoustic underwater channel in tropical shallow water. Different segments of data are measured diurnally at various depths located in the Tanjung Balau, Johor, Malaysia. Most applications assume that the noise is white and Gaussian. However, the UWAN is not just thermal noise but a combination of turbulence, shipping and wind noises. Thus, it is appropriate to assume UWAN as colored rather than white noise. Site-specific noise, especially in shallow water often contains significant non-Gaussian components. The real-time noise segments are analyzed to determine the statistical properties such as power spectral density (PSD), autocorrelation function and probability density function (pdf). The results show the UWAN has a non-Gaussian pdf and is colored. Moreover, the difference in UWAN characteristics between day and night is studied and the noise power at night is found to be more than at the day time by around (3-8dB)

Study of Absorption Loss Effects on Acoustic Wave Propagation in Shallow Water Using Different Empirical Models

Efficient underwater acoustic communication and target locating systems require detailed study of acoustic wave propagation in the sea. Many investigators have studied the absorption of acoustic waves in ocean water and formulated empirical equations such as Thorp’s formula, Schulkin and Marsh model and Fisher and Simmons formula. The Fisher and Simmons formula found the effect associated with the relaxation of boric acid on absorption and provided a more detailed form of absorption coefficient which varies with frequency. However, no simulation model has made for the underwater acoustic propagation using these models. This paper reports the comparative study of acoustic wave absorption carried out by means of modeling in MATLAB. The results of simulation have been evaluated using measured data collected at Desaru beach on the eastern shore of Johor in Malaysia. The model has been used to determine sound absorption for given values of depth (D), salinity (S), temperature (T), pH, and acoustic wave transmitter frequency (f). From the results a suitable range, depth and frequency can be found to obtain best propagation link with low absorption loss

Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation

In many applications, Image de-noising and improvement represent essential processes in presence of colored noise such that in underwater. Power spectral density of the noise is changeable within a definite frequency range, and autocorrelation noise function is does not like delta function. So, noise in underwater is characterized as colored noise. In this paper, a novel image de-noising method is proposed using multi-level noise power estimation in discrete wavelet transform with different basis functions. Peak signal to noise ratio (PSNR) and mean squared error represented performance measures that the results of this study depend on it. The results of various bases of wavelet such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that denoising process that uses in this method produces extra prominent images and improved values of PSNR than other methods

Enhancement signal detection in underwater acoustic noise using level dependent estimation time-frequency de-noising technique

In sonar and underwater digital communication, optimal signal detection is imperative. In many applications, additive white Gaussian noise (AWGN) is assumed; thus, a linear correlator (LC), which is known to be optimal in the presence of AWGN, is normally used. However, underwater acoustic noise (UWAN) affects the reliability of signal detection in applications in which the noise originates from multiple sources and doesn’t follow the AWGN assumption. As a result, an LC detector performs poorly in tropical shallow waters. Accordingly, this study aims to develop a detection method for improving detection probability (PD) by using a time–frequency denoising method based on the S-transform with multi–level threshold estimation. The UWAN used for the validation is sea truth data collected at Desaru beach on the eastern shore of Johor in Malaysia with the use of broadband hydrophones. The performances of four different detectors, namely, the proposed Gaussian noise injection detector (GNID), a locally optimal (LO) detector, a sign correlation (SC) detector, and a conventional LC detector, are evaluated according to their PD values. For a time-varying signal, given a false alarm probability of 0.01 and a PD value of 90 percent, the energy-to-noise ratios of the GNID, LO detector, SC detector, and LC detector are 8.89, 10.66, 12.7, and 12.5 dB, respectively. Among the four detectors, the GNID using the S-transform denoising method achieves the best performance

Underwater acoustic noise characteristics of shallow water in tropical seas

In the underwater communication and target locating, the biggest challenge is to reduce the effect of underwater acoustic noise (UWAN). An experimental model is presented in this paper to characterize noise in the acoustic underwater channel in shallow water. Data is measured from different depths located in the Tanjung Balau, Johor, Malaysia. Most applications assume the UWAN as additive and Gaussian. However, the UWAN is not just thermal noise but a combination of turbulence, shipping and wind noises. Site-specific noise in shallow water often contains significant non-Gaussian components. Thus, it is appropriate to assume UWAN as colored rather than white with non-Gaussian probability density function (pdf). The real-time noise data are analyzed for different depths to determine the statistical properties such as power spectral density (PSD), autocorrelation function and pdf. The results show the UWAN has a non-Gaussian pdf, and is colored with a power spectral density that decays at a rate of approximately 20 dB/decade. Also, the power decreases with increasing depth as the distance from the surface at approximately 10 dB