Consensus-based Hybrid Kalman Filtering Algorithms for Systems with Multiplicative Noises

被引：0

作者：

Xu, Long ^{[1
]}

Ma, Kemao ^{[1
]}

Fan, Hongxia ^{[1
,2
]}

机构：

[1] Harbin Inst Technol, Control & Simulat Ctr, Harbin 150001, Heilongjiang, Peoples R China

[2] Harbin Univ Commerce, Sch Basic Sci, Harbin 150028, Heilongjiang, Peoples R China

来源：

2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年

关键词：

Hybrid discrete stochastic systems; Multiplicative noises; State estimation; Unscented transformation; Consensus on information;

D O I：

暂无

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 consensus-based hybrid Kalman filtering problem is investigated for a class of hybrid systems with multiplicative noises. Here, we consider the system with linear state equation and nonlinear observation equation. A connected undirect graph is employed to represent the information exchange among the sensor nodes. Based on the hybrid approach (linear and nonlinear), a new hybrid Kalman filter is constructed. Then, by employing the consensus on information approach, the consensus algorithm based on hybrid Kalman filtering is designed. We finally provide a numerical example to show the performance of the proposed approach.

引用

页码：5477 / 5482

页数：6

共 24 条

[1] Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems [J].

Al-Hussein, Abdullah ;

Haldar, Achintya .

STRUCTURAL CONTROL & HEALTH MONITORING, 2016, 23 (01) :156-175

[2] An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles [J].

Allotta, B. ;

Caiti, A. ;

Chisci, L. ;

Costanzi, R. ;

Di Corato, F. ;

Fantacci, C. ;

Fenucci, D. ;

Meli, E. ;

Ridolfi, A. .

MECHATRONICS, 2016, 39 :185-195

[3] Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation [J].

Awale, Vaibhav ;

Hablani, Hari B. .

DEFENCE SCIENCE JOURNAL, 2015, 65 (06) :425-430

[4] Stability of consensus extended Kalman filter for distributed state estimation [J].

Battistelli, Giorgio ;

Chisci, Luigi .

AUTOMATICA, 2016, 68 :169-178

[5] Consensus-based multiple-model Bayesian filtering for distributed tracking [J].

Battistelli, Giorgio ;

Chisci, Luigi ;

Fantacci, Claudio ;

Farina, Alfonso ;

Graziano, Antonio .

IET RADAR SONAR AND NAVIGATION, 2015, 9 (04) :401-410

[6] DIGITAL SYNTHESIS OF NON-LINEAR FILTERS [J].

BUCY, RS ;

SENNE, KD .

AUTOMATICA, 1971, 7 (03) :287-&

[7] Distributed event-triggered H∞ consensus filtering in sensor networks [J].

Ding, Lei ;

Guo, Ge .

SIGNAL PROCESSING, 2015, 108 :365-375

[8] Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach [J].

Gonzalez-Jaime, Luis ;

Vegas-Sanchez-Ferrero, Gonzalo ;

Kerre, Etienne E. ;

Aja-Fernandez, Santiago .

KNOWLEDGE-BASED SYSTEMS, 2016, 106 :264-273

[9] Cooperative Space Object Tracking Using Space-Based Optical Sensors via Consensus-Based Filters [J].

Jia, Bin ;

Pham, Khanh D. ;

Blasch, Erik ;

Shen, Dan ;

Wang, Zhonghai ;

Chen, Genshe .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2016, 52 (04) :1908-1936

[10] A new method for the nonlinear transformation of means and covariances in filters and estimators [J].

Julier, S ;

Uhlmann, J ;

Durrant-Whyte, HF .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2000, 45 (03) :477-482

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