Hard-Decision Fusion With Arbitrary Numbers of Bits for Different Samples

Yunfei Chen; Kezhi Wang; Jiming Chen

doi:10.1109/TVT.2012.2224894

Hard-Decision Fusion With Arbitrary Numbers of Bits for Different Samples

Yunfei Chen, Kezhi Wang, Jiming Chen

School of Computer Science

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

A new hard-decision fusion rule that combines arbitrary numbers of bits for different samples taken at different sensors is proposed. The best thresholds for the fusion rules using 2, 3, and 4 bits are obtained. The bit error rate for a hard-decision fusion rule with 1 bit is also derived. Numerical results show that the new scheme can achieve better performance with higher energy efficiency.

Original language	English
Pages (from-to)	879-884
Number of pages	6
Journal	IEEE Transactions on Vehicular Technology
Volume	62
Issue number	2
Early online date	15 Oct 2012
DOIs	https://doi.org/10.1109/TVT.2012.2224894
Publication status	Published - Feb 2013

Keywords

Decision fusion
sample quantization
threshold

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TVT.2012.2224894

Cite this

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abstract = "A new hard-decision fusion rule that combines arbitrary numbers of bits for different samples taken at different sensors is proposed. The best thresholds for the fusion rules using 2, 3, and 4 bits are obtained. The bit error rate for a hard-decision fusion rule with 1 bit is also derived. Numerical results show that the new scheme can achieve better performance with higher energy efficiency.",

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AB - A new hard-decision fusion rule that combines arbitrary numbers of bits for different samples taken at different sensors is proposed. The best thresholds for the fusion rules using 2, 3, and 4 bits are obtained. The bit error rate for a hard-decision fusion rule with 1 bit is also derived. Numerical results show that the new scheme can achieve better performance with higher energy efficiency.

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KW - threshold

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Hard-Decision Fusion With Arbitrary Numbers of Bits for Different Samples

Abstract

Keywords

UN SDGs

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