Impulsive noise model based on stable \alpha-sub-Gaussian distribution

• Published in: 2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC) pp. 356 - 359
• Main Authors: Lei, Zhichu, Xie, Long, He, Xutao, Zhang, Lei, Li, Yuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.12.2023
• Subjects: Computational modeling; Data models; Distributed databases; Gaussian noise; Heavily-tailed distribution; Location awareness; non-Gaussian distributed noise; Shape; Stationary α sub-Gaussian noise; Underwater noise modeling
• DOI: 10.1109/EIECC60864.2023.10456620

Noise modeling is crucial for underwater acoustic signal identification, separation and localization. The traditional Gaussian noise model is unable to fit the heavy-tailed distribution characteristics of non-Gaussian noise field. In the past, the Alpha stable distribution model was used to simulate the non-Gaussian noise. Although the Alpha stable distribution model can fit the amplitude statistics of the impulse noise well, it cannot establish the dependence between the noise samples. A stable \alpha sub-Gaussian distribution model (\mathrm{S}\alpha \text{SG}) is proposed to address the sample dependence of underwater non-Gaussian impulse noise. Based on the DeepShip real underwater ship noise, the modeling effects of the Alpha stable distribution model and the \mathrm{S}\alpha \text{SG} model are compared. The results show that both the Alpha stable distribution model and the \mathrm{S}\alpha \text{SG} model can fit the empirical amplitude distribution of the impulse noise well, but the Alpha stable distribution can only provide four-tailed shaped scatter plots; while the \mathrm{S}\alpha \text{SG} model can provide elliptical shaped scatter plots, which is consistent with the performance of real underwater noise samples.