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基于r-西格蒙德函数和高阶累积量的智能水产养殖工程中生物声学信号的自适应识别

Adaptive Recognition of Bioacoustic Signals in Smart Aquaculture Engineering Based on r-Sigmoid and Higher-Order Cumulants.

作者信息

Cao Tianyu, Zhao Xiaoqun, Yang Yichen, Zhu Caiyun, Xu Zhongwei

机构信息

Department of Information and Communication Engineering, College of Electronic and Information Engineering, Tongji University, Jiading District, Shanghai 201804, China.

出版信息

Sensors (Basel). 2022 Mar 15;22(6):2277. doi: 10.3390/s22062277.

DOI:10.3390/s22062277
PMID:35336448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8953075/
Abstract

In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.

摘要

近年来，随着精准农业技术的发展，人们对水产养殖声学信号的兴趣日益浓厚。水下声学信号被认为具有噪声，特别是因为它们不可避免地与大量环境背景噪声混合，对信号特征提取和内在规律揭示造成严重干扰。此外，干扰给数据的传输、存储和处理带来了相当大的负担。提出了一种基于高阶累积量（HOC）和识别Sigmoid函数的信号识别曲线（SRC）算法，用于目标信号的特征提取。利用SRC可以准确识别感兴趣的信号数据。本研究对该算法进行了分析和验证。结果表明，当信噪比大于7dB时，SRC算法有效，当信噪比为11dB时性能提升最大。此外，SRC算法在应用中表现出了更好的灵活性和鲁棒性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13b8/8953075/66b1c6fa512a/sensors-22-02277-g014.jpg
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