Conventional signal processing algorithms and detection criteria, ogtimised in presence of Gaussian noise, may degrade their performances in non-Gaussian environments. Higher Order Statistics @OS) theory is a powerful means both for characterizing non-Gaussian noise and, then, for designing eftlcient and robust signal detectors. In particular, a method for detecting signals in additive independent non-Gaussian background noise have been investigated, analysed and compared with the bispectrumbased Hinich test and with a conventional spectrum-based detection criterion. In order to compare their respective performances, the different approaches have been applied on real underwater acoustic data, recording the passage of a target ship, in presence of background shipping traffic noise.
COMPARISON BETWEEN DIFFERENT HOS-BASED TESTS FOR DETECTION OF SHIP-RADIATED SIGNALS IN NON-GAUSSIAN NOISE
REGAZZONI, CS;
1994-01-01
Abstract
Conventional signal processing algorithms and detection criteria, ogtimised in presence of Gaussian noise, may degrade their performances in non-Gaussian environments. Higher Order Statistics @OS) theory is a powerful means both for characterizing non-Gaussian noise and, then, for designing eftlcient and robust signal detectors. In particular, a method for detecting signals in additive independent non-Gaussian background noise have been investigated, analysed and compared with the bispectrumbased Hinich test and with a conventional spectrum-based detection criterion. In order to compare their respective performances, the different approaches have been applied on real underwater acoustic data, recording the passage of a target ship, in presence of background shipping traffic noise.
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