FIR system identification based on a nonparametric Bayesian model using the Indian buffet process

Hiroki Tanji, Ryo Tanaka, Takahiro Murakami, Yoshihisa Ishida

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3 Citations (Scopus)


In this paper, we propose a nonparametric Bayesian model combined with the Indian buffet process (IBP) for a finite impulse response (FIR) system. We develop an FIR system identification method that can simultaneously estimate the number of FIR taps and coefficients. In the proposed model, each FIR tap consists of a coefficient and a gain, and the gain is a binary value. An infinite-dimensional binary vector is composed of binary values, and we assume that this binary vector is generated by the IBP. To identify the FIR system, we specify the likelihood function and prior distributions of the parameters and derive their posterior distributions. We can simultaneously estimate the number of FIR taps and coefficients by sampling from posterior distributions using the Gibbs sampler. Our simulations demonstrate that although the number of FIR taps is unknown, the identification performance of the proposed method in a high signal-to-noise ratio environment is similar to or better than that of the conventional least square solution.

Original languageEnglish
Pages (from-to)1105-1112
Number of pages8
JournalSignal, Image and Video Processing
Issue number6
Publication statusPublished - 1 Sep 2016



  • FIR system
  • Gibbs sampler
  • Indian buffet process
  • Nonparametric Bayesian models
  • System identification

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