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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
The limited sea test data are the main limitation to prove model validation in further.
Practical implications
A very useful model for representing reverberation in shallow‐water.
Originality/value
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.
Details
Keywords
Zhao Duan, Yajuan He and Yuan Zhong
Based on the text mining tools, this paper aims to propose a new method to evaluate the subjectivity and objectivity of corporate social responsibility information disclosure.
Abstract
Purpose
Based on the text mining tools, this paper aims to propose a new method to evaluate the subjectivity and objectivity of corporate social responsibility information disclosure.
Design/methodology/approach
The authors build up a text subjectivity evaluation model of corporate social responsibility reports through meta-analysis; a text mining is conducted to all sample CSR reports released by Chinese listed companies untill March 2016[1]. Furthermore, the authors made an overall and quantitative analysis of the situation which contained changing state, characteristics and abnormal value on the subjectivity and objectivity of information disclosure.
Findings
The results show that the subjectivity scores of social responsibility reports of Chinese listed companies are generally in a normal distribution. The diagram turns out to be a rising trend over the years and increases linearly from 2011 to 2013. Also, the industry heterogeneity and policy control are the main reasons for the formation of the differences, which are significant between different industries and different years.
Originality/value
This paper provides not only an important empirical basis for the research of corporate social responsibility but also a new idea for the non-financial information disclosure as well as objective evaluation of normative text.