NEW FAST RECURSIVE ALGORITHMS FOR SIMULTANEOUS RECONSTRUCTION AND IDENTIFICATION OF AR PROCESSES WITH MISSING OBSERVATIONS

Rawad Zgheib (1) , Gilles Fleury (1) , Elisabeth Lahalle (1)

1 SUPELEC-Campus Gif

This paper deals with the problem of adaptive reconstruction and identification of AR processes with randomly missing observations. The performances of a previously proposed real time algorithm are studied. Two new alternatives, based on other predictors, are proposed. They offer an unbiased estimation of the AR parameters. The first algorithm, based on the h-step predictor, is very simple but suffers from a large reconstruction error. The second one, based on the incomplete past predictor, offers an optimal reconstruction error in the least mean square sense.

Domains: Computer Science [cs]; Signal and Image Processing; Mathematics [math]; Statistics [math.ST]; Statistics [stat]; Statistics Theory [stat.TH]; Engineering Sciences [physics]; Signal and Image processing; Statistics [stat]; Applications [stat.AP]

Rawad Zgheib, Gilles Fleury, Elisabeth Lahalle. NEW FAST RECURSIVE ALGORITHMS FOR SIMULTANEOUS RECONSTRUCTION AND IDENTIFICATION OF AR PROCESSES WITH MISSING OBSERVATIONS. 14th European Signal Processing Conference, Sep 2006, Florence, Italy. pp.CD-ROM Proceedings. ⟨hal-00258338⟩