Optimal quadratic filtering of quantization noise in non-Gaussian systems
Optimal quadratic filtering of quantization noise in non-Gaussian systems
- Author(s): M. Dalla Mora ; C. Manes ; P. Palumbo
- DOI: 10.1049/cp:19960705
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
UKACC International Conference on Control. Control '96 — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): M. Dalla Mora ; C. Manes ; P. Palumbo Source: UKACC International Conference on Control. Control '96, 1996 p. 1091 – 1096
- Conference: UKACC International Conference on Control. Control '96
- DOI: 10.1049/cp:19960705
- ISBN: 0 85296 666 0
- Location: Exeter, UK
- Conference date: 2-5 Sept. 1996
- Format: PDF
This work deals with the problem of state estimation for a class of discrete time linear systems forced by non-Gaussian noise where the quantization on measured output is modeled, as usual, as additive noise having uniform probability distribution. The best linear estimate, computed through the Kalman filter, in this case may not give good results. To improve the covariance of the estimation error the best estimator with quadratic structure is developed in this paper. The optimal quadratic filter, proposed by Santis et al. (1995), is preliminarily introduced using a geometric approach. Then its application is shown in a case in which the state noise is strongly non-Gaussian to best appreciate the improvement w.r.t. standard linear filtering.
Inspec keywords: stochastic systems; probability; quantisation (signal); discrete time systems; state estimation; noise; linear systems
Subjects: Signal processing and detection; Other topics in statistics; Simulation, modelling and identification; Information theory; Time-varying control systems; Other topics in statistics; Discrete control systems
Related content
content/conferences/10.1049/cp_19960705
pub_keyword,iet_inspecKeyword,pub_concept
6
6