Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

Yulei Liu; Liping Yan; Bo Xiao; Yuanqing Xia; Mengyin Fu

doi:10.1007/978-3-642-37832-4_3

Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

Yulei Liu^*, Liping Yan, Bo Xiao, Yuanqing Xia, Mengyin Fu

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

Abstract

This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

Original language	English
Pages (from-to)	19-29
Number of pages	11
Journal	Advances in Intelligent Systems and Computing
Volume	214
DOIs	https://doi.org/10.1007/978-3-642-37832-4_3
Publication status	Published - 2014
Event	7th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2012 - Beijing, China Duration: 15 Dec 2012 → 17 Dec 2012

Keywords

Asynchronous multirate multisensor
Cross-correlated noises
Data fusion
State estimation

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10.1007/978-3-642-37832-4_3

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title = "Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises",

abstract = "This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.",

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author = "Yulei Liu and Liping Yan and Bo Xiao and Yuanqing Xia and Mengyin Fu",

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T1 - Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

AU - Liu, Yulei

AU - Yan, Liping

AU - Xiao, Bo

AU - Xia, Yuanqing

AU - Fu, Mengyin

PY - 2014

Y1 - 2014

N2 - This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

AB - This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

KW - Asynchronous multirate multisensor

KW - Cross-correlated noises

KW - Data fusion

KW - State estimation

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U2 - 10.1007/978-3-642-37832-4_3

DO - 10.1007/978-3-642-37832-4_3

M3 - Conference article

AN - SCOPUS:84888425468

SN - 2194-5357

VL - 214

SP - 19

EP - 29

JO - Advances in Intelligent Systems and Computing

JF - Advances in Intelligent Systems and Computing

T2 - 7th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2012

Y2 - 15 December 2012 through 17 December 2012

ER -

Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

Abstract

Keywords

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