Underwater Acoustic Noise Model for Shallow Water Communications

Abstract

Resumo-Este artigo apresenta um modelo empírico para o ruído do canal acústico submarino em águas rasas a partir da análise de dados provenientes de medições realizadas em campo. Uma função de densidade de probabilidade para a distribuição da amplitude do ruído é proposta e funções de verossimilhança são obtidas. Em decorrência, uma expressão para a probabilidade de erro de símbolo para sinalização binária no canal é apresentada. Além disso, são fornecidos os resultados de simulação realizada com as amostras reais de ruído coletadas em campo, de modo a verificar o efeito do ruído no desempenho de sistemas acústicos de comunicação submarina com sinalização binária. Palavras-Chave-Comunicação Submarina, Ruído Acústico Submarino, Distribuição de Ruído.Abstract-This article presents an empirical model for the noise of the acoustic underwater channel in shallow water from the analysis of field data measurements. A probability density function for the noise amplitude distribution is proposed and the associated likelihood functions are derived. As a result, an expression to the probability of symbol error for binary signaling is presented for the channel. Additionally, the results of simulations conducted using the field collected noise samples are presented, in order to verify the noise effect on the performance of underwater acoustic communication binary signaling systems.

Review Article: Multicarrier Communication for Underwater Acoustic Channel

“…An empirical model for the noise of the acoustic underwater channel in shallow water from the analysis of field data measurements has been presented in [15]. In that paper, a probability density function for the noise amplitude distribution is proposed and the associated likelihood functions are derived.…”

Filter orthogonal frequency‐division multiplexing scheme based on polar code in underwater acoustic communication with non‐Gaussian distribution noise

“…Several studies assessed various locations for UWA noise and revealed that noise did not adhere to the Gaussian distribution [4,[15][16][17][18][19]. The use of a probability density function (PDF), along with a wide tail, could identify the performance and type of noise [18][19][20]. A study confirmed that the algorithm proposed based on Gaussian mixture (GM) successfully estimated the noise PDF [21].…”

“…These noises are viewed as complex non-Gaussian noises that can increase the probability error of code words at the receiver end in a communication system. Several studies assessed various locations for UWA noise and revealed that noise did not adhere to the Gaussian distribution [4,[15][16][17][18][19]. The use of a probability density function (PDF), along with a wide tail, could identify the performance and type of noise [18][19][20].…”

Improved techniques for detection and localization of underwater acoustic sources using acoustic vector sensors

“…The ST distribution is considered a natural choice when data that has been modeled using the Gaussian pdf is remodeled to take the impulsiveness of collected data into account. This distribution finds widespread application in a variety of fields including underwater acoustics [37], [174].…”

“…It has been widely employed in several applications such as statistical analysis of blood flow, genomics, Chapter 3 Data models 58 economics, traffic, etc. [37] and has been shown to be effective in modeling the data in underwater acoustics as well [174].…”