On the minimax robust Kalman Filter: A bounded estimation resources approach

Vadim Azhmyakov; Nelson Castano; Jose Perea Arango; Piotr Graczyk; Fabio Humberto Sepulveda Murillo

doi:10.1109/CCAC.2017.8276395

On the minimax robust Kalman Filter: A bounded estimation resources approach

Vadim Azhmyakov, Nelson Castano, Jose Perea Arango, Piotr Graczyk, Fabio Humberto Sepulveda Murillo

Research output: Contribution to conference › Paper

1 Scopus citations

Abstract

© 2017 IEEE. This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties.

Original language	American English
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/CCAC.2017.8276395
State	Published - 30 Jan 2018
Event	2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings - Duration: 30 Jan 2018 → …

Conference

Conference	2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings
Period	30/01/18 → …

Product types of Minciencias

Other informative publication

Access to Document

10.1109/CCAC.2017.8276395

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047341894&origin=inward

Cite this

@conference{bc31e8cfe52a46b3a5abb95b668ba232,

title = "On the minimax robust Kalman Filter: A bounded estimation resources approach",

abstract = "{\textcopyright} 2017 IEEE. This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties.",

author = "Vadim Azhmyakov and Nelson Castano and Arango, {Jose Perea} and Piotr Graczyk and Murillo, {Fabio Humberto Sepulveda}",

year = "2018",

month = jan,

day = "30",

doi = "10.1109/CCAC.2017.8276395",

language = "American English",

pages = "1--6",

note = "2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings ; Conference date: 30-01-2018",

}

TY - CONF

T1 - On the minimax robust Kalman Filter: A bounded estimation resources approach

AU - Azhmyakov, Vadim

AU - Castano, Nelson

AU - Arango, Jose Perea

AU - Graczyk, Piotr

AU - Murillo, Fabio Humberto Sepulveda

PY - 2018/1/30

Y1 - 2018/1/30

N2 - © 2017 IEEE. This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties.

AB - © 2017 IEEE. This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties.

U2 - 10.1109/CCAC.2017.8276395

DO - 10.1109/CCAC.2017.8276395

M3 - Paper

SP - 1

EP - 6

T2 - 2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings

Y2 - 30 January 2018

ER -

On the minimax robust Kalman Filter: A bounded estimation resources approach

Abstract

Conference

Product types of Minciencias

Access to Document

Fingerprint

Cite this