K-Rayleigh mixture model for sparse active sonar clutter

Abstract

Abstract-The mixture of a Rayleigh probability density function (PDF) and a K PDF is proposed for representing active sonar data comprising clutter sparsely observed in a Rayleigh-distributed background. While both the Rayleigh and K distributions have been shown to accurately represent the statistics of certain types of active sonar data, it is common to observe data containing both echoes from distinct clutter objects and more diffuse, Rayleigh-distributed reverberation. While the K distribution can often still capture the behavior of such data, the K-Rayleigh mixture is seen to provide improved PDF fits and inference on the clutter statistics. A parameter estimation algorithm for the K-Rayleigh mixture PDF based on the expectation-maximization (EM) technique is proposed and shown to provide adequate performance in representing the PDF of very heavy tailed real sonar data.

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

“…It is well established in the SONAR literature that K and Rayleigh distributions are suitable for describing background clutter in active SONAR such as biological and environmental effects in the underwater environment [27]. Our approach of introducing a signal model with residuals has parallels with the matched filter envelope described by the K-Rayleigh mixture in [27]. As such, we retain these components in our compound mixture model.…”

Empirical estimation of probability distribution for electric field strength in automotive cabin

“…In the fields of wireless communications or sensors, Rayleigh, Nakagami or Ricean distributions are often used to express the probability distribution of received signal intensity [15]- [16]. Try to fit the ensemble data set to linear Rayleigh distribution shows good matching as in fig.8 for low-value data range.…”

“…And it is also deduced that the basic statistical structure is the same between individual condition data and ensemble data. Here, mixture distribution is a distribution which consists of compounding of several distinct component distributions [11]- [15].…”

“…In this paper, expectationmaximization (EM) algorithm is used to calculate the mixture weights and the parameters for each distribution [10]- [15].…”

“…Using the EM algorithm for mixture distribution described in [10]- [15] with the equations (4) and (6) above, estimation of all parameters for the mixture distribution can be obtained.…”