Identification of sparse FIR systems using a general quantisation scheme |
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| Authors: | Boris I Godoy Juan C Agüero Rodrigo Carvajal Graham C Goodwin Juan I Yuz |
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| Affiliation: | 1. Centre for Complex Dynamic Systems &2. Control, The University of Newcastle, Australiaboris.godoy@newcastle.edu.au;4. Control, The University of Newcastle, Australia;5. Electronics Department, Universidad T. Federico Santa María, Valparaíso, Chile |
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| Abstract: | This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an ?1 a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm. |
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| Keywords: | system identification quantised systems maximum likelihood sparsity |
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