Singular-spectrum analysis: a toolkit for short, noisy chaotic signals
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Singular spectrum analysis of traffic workload in a large-scale wireless lan
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Comparing time series using wavelet-based semblance analysis
Computers & Geosciences
Statistical models of reconstructed phase spaces for signal classification
IEEE Transactions on Signal Processing - Part I
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The blind separation of single-channel signal is one of the most important aspects in many fields. Our research is carried out to develop a blind separation method of single-channel signal, in which the singular spectrum analysis (SSA) and blind source separation (BSS) techniques are jointly used, i.e. the single-channel signal is firstly changed into pseudo-MIMO (multi-input and multi-output) mode, and then each source signal is separated via a fast BSS algorithm. A signal preprocessing procedure, which is mainly focused on testing the nonstationarity of single-channel signal, is conducted before the operations of mixed signal transform and separation. In this research, the approach of heuristic segmentation of a nonstationary time-series is proposed. Throughout the experiment, the effectiveness of the proposed method is validated with a data set taken from a digital wideband receiver in an outdoor test. Then, a comparison is made between the proposed method and the Hilbert-Huang transform (HHT)-based signal separation method. The advantage of the proposed method is exhibited.