Bispectrum estimation using a MISO autoregressive model. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Erdem, A.; Ercan, Ali
• Is Part Of: Signal, image and video processing. Volume 10:Issue 7(2016)
• Page Start: 1249
• Page End: 1256
• Publication Date: 2016-10
• Subjects: Bispectrum estimation -- Bicumulant sequence -- MISO autoregressive system -- System identification; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-016-0888-3
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature.