Optimal and Near-Optimal Signal Detection in Snapping Shrimp Dominated Ambient Noise

Abstract

Abstract-The optimal detection of signals requires detailed knowledge of the noise statistics. In many applications, the assumption of Gaussian noise allows the use of the linear correlator (LC), which is known to be optimal in these circumstances. However, the performance of the LC is poor in warm shallow waters where snapping shrimp noise dominates in the range 2-300 kHz. Since snapping shrimp noise consists of a large number of individual transients, its statistics are highly non-Gaussian. We show that the noise statistics can be described accurately by the symmetric -stable family of probability distributions. Maximum-likelihood (ML) and locally optimal detectors based on the detailed knowledge of the noise probability distribution are shown to demonstrate enhanced performance. We also establish that the sign correlator, which is a nonparametric detector, performs better than the LC in snapping shrimp noise. Although the performance of the sign correlator is slightly inferior to that of the ML detector, it is very simple to implement and does not require detailed knowledge of the noise statistics. This makes it an attractive compromise between the simple LC and the complex ML detector.

Diurnal Variability Of Underwater Acoustic Noise Characteristics in Shallow Water

“…Instead, it follows pdf with extended tails shape, reflecting an accentuated impulsive behavior due to the high incidence of large amplitude noise event [5]. The noise follows the alpha-stable PDF where the characteristic equation has an inverse.…”

“…Typical shallow-water environments cover water depths down to 200m while typical deep-water environments in all oceans are considered at depths exceeding 2000m [8]. In [5,6], three major noise sources in shallow water environments namely wind noise, biological noise (especially noise created by snapping shrimp whose noise signature has a high amplitude and wide bandwidth) and shipping noise are identified. Furthermore, the UWAN power also decreases with the increasing depth as the distances between the surface, the shipping and wind noises become larger.…”

“…As the attenuation of sound in the ocean is a frequency-dependent process, the ocean acts as a low-pass filter for ambient noise. The ambient noise PSD is thus usually where noise has more power at lower frequencies and less power at higher frequencies [5]. Diurnal variability shows that the ship noise is greater at day than night but the biological noise on the contrary occurs more strongly at night than during the day.…”

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

“…The assumptions of Gaussian distribution for UWAN are described in [8]. However, recent work suggested that the UWAN follows t-distribution [9] and stable alpha distribution [10]. The power spectrum density (PSD) of white Gaussian noise is a constant over the complete frequency range, all frequencies range with a magnitude of .…”

Optimum Bit Rate for Image Transmission over Underwater Acoustic Channel

“…Shallow water UWA noise is neither Gaussian nor white distributed [32][33][34]. In [35] the authors reported a model of probability density function (pdf) for the underwater acoustic noise issue based on real measurements at sea.…”