Fuzzy c-means clustering based robust and blind noncoherent receivers for underwater sensor networks

Authors:
Bin Li;Zheng Zhou;Weixia Zou;Shubin Wang
Affiliations:
Wireless Network Lab, Beijing University of Posts and Telecommunications, Key Lab of Universal Wireless Communications, MOE, Beijing, China;Wireless Network Lab, Beijing University of Posts and Telecommunications, Key Lab of Universal Wireless Communications, MOE, Beijing, China;Wireless Network Lab, Beijing University of Posts and Telecommunications, Key Lab of Universal Wireless Communications, MOE, Beijing, China;Wireless Network Lab, Beijing University of Posts and Telecommunications, Key Lab of Universal Wireless Communications, MOE and Inner Mongolia University, Beijing, China
Venue:
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
Year:
2010

Citing 3
Cited 2

Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Underwater acoustic sensor networks: Target size detection and performance analysis

Ad Hoc Networks
Full length article: Efficient processing of acoustic signals for high-rate information transmission over sparse underwater channels

Physical Communication

A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Journal of Network and Computer Applications
Ant intelligence inspired blind data detection for ultra-wideband radar sensors

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Attributed to complex and dynamic propagations in underwater acoustic sensors network, the multipath signals has posed great challenges in the receiver designing in past decades of years. In this paper, we adopt the UWB channel model in underwater networks and suggest a blind non-coherent receiver. Some differentiated features have been developed to represent the multipath signals. Then, we formulate the underwater signal detection as a data mining process. Fuzzy c-means clustering (FCM) algorithm is finally adopted to perform blind signal detection. Numerical simulations show that our scheme outperforms the traditional noncoherent techniques.