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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | White, L.B. Carravetta, F. |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Univ. of Adelaide, Adelaide, SA, Australia (White, L.B.) || Ist. di Analisi dei Sist. ed Inf. `A. Ruberti', Consiglio Naz. delle Ric., Rome, Italy (Carravetta, F.) |
| Abstract | The present work is a sequel of our paper [1] where a Bayesian unnormalised smoother was proposed for the so-called class of partially observed reciprocal chains (RC). Within this Bayesian setting, an issue remained unsolved concerning practical implementation due to the unnormalised feature of the smoother. Here a normalised Bayesian smoother is developed for a class of signals even more general than RCs, termed Generalised Reciprocal Chains (GRC) which are relevant from an application point of view. A simple numerical example involving target tracking in one dimension is presented which illustrates that a potential benefit of the new models and associated optimal smoothers can be obtained, albeit with increased computational cost. More work is needed to ascertain classes of problems where the new models yield significant benefit. |
| Starting Page | 205 |
| Ending Page | 208 |
| File Size | 112965 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479949755 |
| DOI | 10.1109/SSP.2014.6884611 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-29 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Yttrium Markov processes Numerical models Random variables Equations Signal processing Bayes methods |
| Content Type | Text |
| Resource Type | Article |
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