文章基本信息

标题：Kalman-like filtering with intermittent observations and non-Gaussian noise
本地全文：下载
作者：Stefano Battilotti ; Filippo Cacace ; Massimiliano d’Angelo 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：20
页码：61-66
DOI：10.1016/j.ifacol.2019.12.127
语种：English
出版社：Elsevier
摘要：The paper concerns the sub-optimal filtering problem when the measurement signal is sent through an unreliable channel and the noise signals are not necessarily Gaussian. In particular, we assume that the measurement packet losses are modeled by an i.i.d. Bernoulli sequence with known probability mass function, and the moments of the (generally) non-Gaussian noise sequences up to the fourth order are known. By mean of a suitable rewriting of the system through an output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, an optimal solution among the quadratic transformations of the output is provided. Numerical simulations show the effectiveness of the proposed method.
关键词：KeywordsKalman filteringintermittent observationsnon-Gaussian systems