An adaptive data detection algorithm based on intermittent chaos with strong noise background

• Published in: Neural computing & applications Vol. 32; no. 22; pp. 16755 - 16762
• Main Authors: Biao, W., Yu, Fujiang, Yang, Wenzhong, He, Cheng
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2020; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive systems; Algorithms; Artificial Intelligence; Background noise; Chaos theory; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Excitation; Hilbert transformation; Image Processing and Computer Vision; Mathematical analysis; Probability and Statistics in Computer Science; Signal detection; Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns; Adaptive step size; Line spectrum detection; Low SNR; Intermittent chaos; dimension spectrum
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3839-9

In order to realize the signal detection under the condition of lower SNR, this paper introduced the adaptive phase length based on the Duffing chaotic system and verified the measured signal at the optimal excitation frequency. The existence of the target signal was judged by observing whether there are two consecutive intermittent chaos in the time domain. Then the envelope of the intermittent chaos was obtained by Hilbert transform. Finally, the exact value of envelope spectrum was obtained by using the one-and-half-dimension spectrum, which can calculate the precise value of the frequency of the signal to be measured. The experimental results showed that the proposed algorithm can achieve a lower SNR than the conventional detection. Compared with the general chaotic detection, this algorithm can realize smart self-adaptation. It is unnecessary to specify different excitation frequencies and chaotic thresholds for different frequencies to be measured. In addition to the existence of the target signal judgment, the algorithm can also achieve accurate calculation of the frequency of the signal to be measured.