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Paper data
Evaluation of sequential importance sampling for blind deconvolution via a simulation study

Ali R. Ayesha, University of Washington
Richardson Thomas S., University of Washington
Murua Alejandro, University of Washington
Roy Sumit, University of Washington

Page numbers in the proceedings:
Volume II pp 315-318

Applications of Particle Filtering in Communications (2/2)

Paper abstract
Sequential techniques for the canonical blind deconvolution problem have attracted the attention of computational Bayesians such as Lui & Chen (1995) who applied Sequential Importance Sampling (SIS) to this problem. Subsequently, several extensions have been proposed (e.g. Rejuvenation, Rejection Control, Fixed-Lag Smoothing, Metropolis-Hastings Importance Resampling, etc.) as improvements to SIS, but some of the drawbacks inherent in SIS persist. A comparison of variants of the Viterbi (VA), List Viterbi (LVA), BCJR (for Bahl, Cocke, Jelinek and Raviv) and SIS algorithms was conducted with inconclusive results. Although SIS can be helpful in certain circumstances, it shows signs of instability, and therefore, may not be useful in practice. In conclusion, one should be cautious in using SIS or Rejuvenation for blind deconvolution problems.

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