Paper Info
Title
Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection.
Abstract
Acoustic resonance-based level measurement principle needs to extract a sequence of resonance frequencies (RFs) from the synthesis wave and then calculate level height via this RF sequence. However, in practice, the uncertain disturbances in the measurement environment usually lead to the signal distortion of the collected synthesis wave. In this case, some RF points in the sequence are inevitably missed which causes the nonnegligible calculation error. Hence, based on the Dempster-Shafer evidence theory (DST), this paper presents a recursive evidence fusion method to combine multiple RF sequences in a row. It provides a natural way to supplement the missed RF points and also significantly improve the measurement accuracy even if the observed RF sequences are all intact. That is to say, regardless of the missing case or intact case, the proposed fusion method always has high performance. Finally, the comparative experiments of level detection show the level gauge using this method is robust for the sequence with the missing RF points and can further provide higher measurement accuracy than the single RF sequence-based and digital filtering-based level detection methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2917724
IEEE ACCESS
Keywords
Field
DocType
Level detection,acoustic resonance,DS evidence theory (DST),random-fuzzy variable (RFV),alarm monitoring
Computer science,Algorithm,Fusion,Recursion,Distributed computing,Acoustic resonance
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Xiaobin Xu114522.74
Danfeng Fang200.34
Guo Li321.05
Peng Chen400.68
Xiaojian Xu510724.29
Jian-Ning Li6444.74