Kalman-like filtering with intermittent observations and non-Gaussian noise

被引：13

作者：

Battilotti, Stefano ^{[1
]}

Cacace, Filippo ^{[2
]}

d'Angelo, Massimiliano ^{[1
]}

Germani, Alfredo ^{[3
]}

Sinopoli, Bruno ^{[4
]}

机构：

[1] Sapienza Univ Rome, Dipartimento Ingn Informat Automat & Gest, Rome, Italy

[2] Univ Campus Biomed Roma, Rome, Italy

[3] Univ Aquila, Dipartimento Ingn & Sci Informaz & Matemat, Laquila, Italy

[4] Washington Univ, Elect & Syst Engn Dept, St Louis, MO 63110 USA

来源：

IFAC PAPERSONLINE | 2019年 / 52卷 / 20期

关键词：

Kalman filtering; intermittent observations; non-Gaussian systems; STABILITY;

D O I：

10.1016/j.ifacol.2019.12.127

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

引用

页码：61 / 66

页数：6

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