Recursive Observation Evidence Fusion Method for Acoustic Resonance-Based Level Detection

• Published in: IEEE access Vol. 7; pp. 65060 - 65074
• Main Authors: Xu, Xiaobin, Fang, Danfeng, Li, Guo, Chen, Peng, Xu, Xiaojian, Li, Jianning
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic measurements; Acoustic resonance; Acoustics; alarm monitoring; DS evidence theory (DST); Filtering theory; Level detection; Liquids; Mathematical analysis; Measurement uncertainty; Radio frequency; random-fuzzy variable (RFV); Resonant frequency; Signal distortion; Synthesis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2917724

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.