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

2022

IJEECS

Self Cite

<p>The greatest challenge in underwater acoustic communication systems is the minimization of underwater impact noise. This article offers an empirical example for determining the statistical properties of underwater acoustic noise in the in Lake Hamrin. The data are measured from various depths reached in Lake Hamrin, Diyala, Iraq. In most communication systems, noise is assumed to be additive as well as Gaussian. Underwater acoustic noise (UWAN) isn't only thermal noise, it also includes other components to the UWAN: turbulence, wind and shipping noises. Thus, it should be assumed that the acoustic noise is colored noise instead of white noise. Intermittent noise in the oceans and seas frequently includes significant Non-Gaussian elements. The samples noise data are analyzed in actual time for various depths of 1 meter, 3 meter and 5 meter in order to limit the statistical properties of underwater acoustic noise in Lake Hamrin, Diyala, Iraq such as the autocorrelation function (ACF), the probability density function (PDF) and also power spectral density (PSD). The experimental results showed that the noise of the Tigris river is a color noise and does not follow the Gaussian distribution.</p>

Section: Probability Density Functionmentioning

confidence: 98%

mentioning

confidence: 98%

Statistical properties of underwater acoustic noise in Lake Hamrin, Diyala, Iraq

Murad

2022

IJEECS

Self Cite

“…Where x is a vector of M discrete amplitude levels of noise, and bo / EN is the energy per bit to noise power spectral density ratio. Some researchers have indicated that the noise distribution in shallow water follows the gaussian distribution while others assume the noise distribution in UWA is non-white and non-gaussian [7], [8].…”

Section: Channel Model In Uwamentioning

confidence: 99%

“…The code rate, R=u/v gives the coding efficiency with values of u and v ranging from 1 to 8 and 2 to 10, respectively. One CC defined as CC (7,5) was used. Using trellis representation by employing four states following the CC order, viterbi algorithm was utilized for the decoding.…”

Section: Convolution Codementioning

confidence: 99%

A comparative study on channel coding scheme for underwater acoustic communication

Ahmed¹,

Shah

et al. 2023

Bulletin EEI

There are various challenges in underwater acoustic communication (UWA) however bit error rate (BER) is considered as the main challenge as it significantly affects the UWA communication. In this paper, different coding schemes such as convolution, turbo, low density parity check (LDPC), and polar coding based on the t-distribution noise channel are investigated, and binary phase-shift keying (BPSK) modulation with a code rate of 1/2 has considered in the evaluation and analyses. The evaluation of these channel coding schemes is performed based on BER, computational complexity as well as latency. The results have shown the outperform of polar coding in UWA over other channel coding schemes as it has lower BER and lower computational complexity.

“…The UWAN in the low water with the biological noise is non-Gaussian distributed, and the characteristics presented impulsive behaviour [4][5][6]. Which is why, bub-optimum MF detector performances in the UWAN has created considerable potentials for the enhancement of the performances in the under-water condition [2,7].Gaussian noise injection detector proposed by [8,9]to improve the detection in underwater media using different time-frequency de-noising methods.…”

Section: Near-optimum Detection Of Signals In Underwater Acoustic Noi...mentioning

confidence: 99%

Near–optimum Detection of Signals in Underwater Acoustic Noise Using Locally Optimal Detector in Tigers River

Ahmed

Sahrab

2023

jeasd

Signal detection has been considered important in under-water signal processing and digital communications, and depending upon noise statistics’ knowledge, near-optimum signal detections in the under-water acoustic noises (UWANs) may be realized in a more effective manner. The theory of the normal (i.e. Gaussian) noise permits using matched filter (MF) detectors; for that reason, a locally optimal (LO) detector has been designed in the present work for improving the probability of the detection ( based on knowing the probability density function (PDF) of noise. Under-water noise that has been utilized for the validation represents the real data that had been gathered from the sea with the use of the broad-band hydrophones at Abo Dali district -Kazem Al Ali Village-Tigris Beaches-Baghdad-Iraq. The LO detector performance is compared after that to the conventional matched filter detector and those have been assessed based on their values. For time-varying signals, the probability of false alarms has been identified as 0.010, and of 90%, energy-to-noise ratios (ENRs) of LO are more efficient compared to the ones of Matched Filter by 4.1dB and for the signals with a fixed frequency, LO is more efficient compared to matched filter by 4.7dB.