Abstract
We propose and investigate a robust fusion optimal unbiased finite impulse response (FOUFIR) algorithm, which is composed of robust UFIR filters implemented in smart sensors and a bias-constrained optimal fusion filter (FF) implemented in a local node of a decentralized wireless sensor network (WSN). The algorithm is designed in discrete time-invariant state space for correlated sensor and node noise sources. To convert the model with correlated noise sources to that without the correlation, the method of indefinite coefficient is used. Assuming that the estimates are delivered to a node with no delay and missing data, a bias-constrained optimal FF is designed employing the linear unbiased minimum variance approach. Simulations are provided for a node operating with 12 smart sensors. Experimental verification is given for a WSN composed with 8 sensors of temperature and humidity. It is shown that the FOUFIR algorithm has better robustness against errors in the noise statistics and model uncertainties than the existing fusion and decentralized Kalman filtering algorithms.
| Original language | English |
|---|---|
| Article number | 8323216 |
| Pages (from-to) | 727-735 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Volume | 4 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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Keywords
- correlated noise
- Decentralized WSN
- fusion filtering
- Kalman filter
- robustness
- unbiased FIR filter
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Bias-Constrained Optimal Fusion Filtering for Decentralized WSN with Correlated Noise Sources. / Zhao, Shunyi; Shmaliy, Yuriy S.; Ahn, Choon Ki.
In: IEEE Transactions on Signal and Information Processing over Networks, Vol. 4, No. 4, 8323216, 01.12.2018, p. 727-735.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bias-Constrained Optimal Fusion Filtering for Decentralized WSN with Correlated Noise Sources
AU - Zhao, Shunyi
AU - Shmaliy, Yuriy S.
AU - Ahn, Choon Ki
PY - 2018/12/1
Y1 - 2018/12/1
N2 - We propose and investigate a robust fusion optimal unbiased finite impulse response (FOUFIR) algorithm, which is composed of robust UFIR filters implemented in smart sensors and a bias-constrained optimal fusion filter (FF) implemented in a local node of a decentralized wireless sensor network (WSN). The algorithm is designed in discrete time-invariant state space for correlated sensor and node noise sources. To convert the model with correlated noise sources to that without the correlation, the method of indefinite coefficient is used. Assuming that the estimates are delivered to a node with no delay and missing data, a bias-constrained optimal FF is designed employing the linear unbiased minimum variance approach. Simulations are provided for a node operating with 12 smart sensors. Experimental verification is given for a WSN composed with 8 sensors of temperature and humidity. It is shown that the FOUFIR algorithm has better robustness against errors in the noise statistics and model uncertainties than the existing fusion and decentralized Kalman filtering algorithms.
AB - We propose and investigate a robust fusion optimal unbiased finite impulse response (FOUFIR) algorithm, which is composed of robust UFIR filters implemented in smart sensors and a bias-constrained optimal fusion filter (FF) implemented in a local node of a decentralized wireless sensor network (WSN). The algorithm is designed in discrete time-invariant state space for correlated sensor and node noise sources. To convert the model with correlated noise sources to that without the correlation, the method of indefinite coefficient is used. Assuming that the estimates are delivered to a node with no delay and missing data, a bias-constrained optimal FF is designed employing the linear unbiased minimum variance approach. Simulations are provided for a node operating with 12 smart sensors. Experimental verification is given for a WSN composed with 8 sensors of temperature and humidity. It is shown that the FOUFIR algorithm has better robustness against errors in the noise statistics and model uncertainties than the existing fusion and decentralized Kalman filtering algorithms.
KW - correlated noise
KW - Decentralized WSN
KW - fusion filtering
KW - Kalman filter
KW - robustness
KW - unbiased FIR filter
UR - http://www.scopus.com/inward/record.url?scp=85053740474&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053740474&partnerID=8YFLogxK
U2 - 10.1109/TSIPN.2018.2819079
DO - 10.1109/TSIPN.2018.2819079
M3 - Article
AN - SCOPUS:85053740474
VL - 4
SP - 727
EP - 735
JO - IEEE Transactions on Signal and Information Processing over Networks
JF - IEEE Transactions on Signal and Information Processing over Networks
SN - 2373-776X
IS - 4
M1 - 8323216
ER -