TY - GEN
T1 - Characteristics Analysis of Globally Cascaded Stochastic Resonance
AU - Lian, Wei
AU - Shen, Xiaohong
AU - Suo, Jian
AU - Wang, Haiyan
AU - He, Ke
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The utilization of noise energy for signal enhancement through random resonance has shown promise in improving the accuracy of underwater passive sonar for detection and localization. However, single-layer stochastic resonance(SR) systems exhibit limited filtering effects, and the issue of cascading failure arises in traditional locally cascaded stochastic resonance(LCSR) systems due to individual optimization of system parameters. To address these challenges, this paper investigates the globally cascaded stochastic resonance(GCSR) system, which leverages the synergy between sub-systems and employs a holistic approach by using the signal-to-noise ratio (SNR) at the last stage as a measure to further enhance the signal enhancement performance of the stochastic resonance system. The collaborative and distribution characteristics among GCSR subsystems are analyzed, and a comparative study of the frequency response, filtering performance, and noise resistance capability is conducted between SR, LCSR, and GCSR systems. Multiple validations demonstrate significant improvements in the signal enhancement performance of the GCSR system, particularly in low SNR conditions, compared to SR and LCSR systems.
AB - The utilization of noise energy for signal enhancement through random resonance has shown promise in improving the accuracy of underwater passive sonar for detection and localization. However, single-layer stochastic resonance(SR) systems exhibit limited filtering effects, and the issue of cascading failure arises in traditional locally cascaded stochastic resonance(LCSR) systems due to individual optimization of system parameters. To address these challenges, this paper investigates the globally cascaded stochastic resonance(GCSR) system, which leverages the synergy between sub-systems and employs a holistic approach by using the signal-to-noise ratio (SNR) at the last stage as a measure to further enhance the signal enhancement performance of the stochastic resonance system. The collaborative and distribution characteristics among GCSR subsystems are analyzed, and a comparative study of the frequency response, filtering performance, and noise resistance capability is conducted between SR, LCSR, and GCSR systems. Multiple validations demonstrate significant improvements in the signal enhancement performance of the GCSR system, particularly in low SNR conditions, compared to SR and LCSR systems.
KW - characteristic analysis
KW - parameter optimization
KW - signal enhancement
KW - stochastic resonance
UR - http://www.scopus.com/inward/record.url?scp=85184848442&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC59353.2023.10400276
DO - 10.1109/ICSPCC59353.2023.10400276
M3 - 会议稿件
AN - SCOPUS:85184848442
T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Y2 - 14 November 2023 through 17 November 2023
ER -