The purpose of this paper is to improve active sonar detection performance in shallow water. A stochastic‐like model multivariate elliptically contoured (MEC) distributions is defined to model reverberation, which helps to reveal structure information of target signatures.
Active sonar systems have been developed with wider transmission bandwidths and larger aperture receiving array, which improve the signal‐to‐noise ratio and reverberation power ratio after matched filtering and beamforming. But, it has changed the statistical distribution of the reverberation‐induced envelope from the traditionally assumed Rayleigh distribution. The MEC is a kind of generalized non‐Gaussian distribution model. The authors theoretically derive the compound Gaussian, Rayleigh‐mixture, Weibull, K distributions are all special cases of MEC. It is known that Weibull and K distributions have obvious heavy‐tail than Rayleigh distribution. MEC is a suitable model to characterize non‐Rayleigh heavy‐tailed distribution of reverberation.
The analysis of test data shows reverberation envelopes obviously deviate Rayleigh distribution. In a broad non‐Gaussian framework, reverberation is modelled as MEC distribution, which is suitable to characterize non‐Rayleigh reverberation. The received data in trials validate the effectiveness of MEC model. The real data envelops follows K distribution, which is a special case of MEC. So, the MEC can be applied to develop novel signal‐processing algorithms that mitigate or account for the effects of the heavy‐tailed reverberation distributions on the target detection.
The limited sea test data are the main limitation to prove model validation in further.
A very useful model for representing reverberation in shallow‐water.
The MECs in fact represent an attractive set data model for adaptive array, and it provides a theoretic framework to design an optimal or sub‐optimal detector.
Wang, Q. and Gong, X. (2009), "Analysis of non‐Rayleigh reverberation model with ocean experiment data in coastal area", Kybernetes, Vol. 38 No. 10, pp. 1669-1675. https://doi.org/10.1108/03684920910994024Download as .RIS
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