Gaussian Mixture Model for Marine Reverberations

Abstract

Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation data has revealed a predominant adherence to the Rayleigh distribution, signifying its departure from specific distribution forms like the Gaussian distribution. This study introduces the Gaussian mixture model, capable of simulating random variables conforming to a wide array of distributions through the integration of an adequate number of components. Leveraging the unique statistical attributes of reverberation, we initiate the Gaussian mixture model’s parameters via the frequency histogram of the reverberation data. Subsequently, model parameters are estimated using the expectation–maximization (EM) algorithm and the most suitable statistical model is selected based on robust model selection criteria. Through a comprehensive evaluation that encompasses both simulated and observed data, our results underscore the Gaussian mixture model’s effectiveness in accurately characterizing the distribution of reverberation data, yielding a mean squared error of less than 4‰.

Attention‐generative adversarial networks for simulating rain field

“…The codec structure has also been introduced into the image rain removal task [36]. However, many of these methods still rely on oscillation parameters to synthesize rainfall, using methods such as manual rain prior distribution [18, 19], physical rainfall factor distribution [20], and 2‐D Gaussian shape distribution [28] to construct training data sets. These simple synthetic datasets do not correctly reflect real‐world rain field distributions.…”