Research on Operator-based Robust Adaptive Signal Separation Aglorithm and Its Applications
Abstract：This project will first research on the theory of operator-based adaptive signal separation algorithm from three aspects comprehensively and thoroughly. Then, the applications of the improved operator-based signal separation approach in the physiological signal analysis will be studied. The major research contents are listed as follows. (1) This project will focus on the in-depth discussion of definition of mono-component signal, which reflects the signal’s priority model. Then, we will try to derive appropriate form of operators for decomposing nonlinear and chaotic signals. (2) This project will research on incorporating signal separation model, such as orthogonal model and sparse model, etc., into the operator-based signal separation algorithm to improve its robustness and separation ability. (3) When the operators’ continuous form is complicated, i.e., it contains nonlinear and excitation term, or the nonlinear signal model is unknown, it is difficult to apply the operator-based approach into practical use because of numerical instability. Therefore, this project will research on the numerical computation methods for this kind of operators and nonlinear signals to improve its numerical stability and precision. (4) Based on the above theoretic research results, this project will research on the new operator-based signal separation approaches for analysis and diagnosis of some physiological signals with typical nonlinearity and chaos. More specifically, we will try to firstly decompose those signals into several subcomponents; and then compute the dynamic measurements of each subcomponents and correlations between them to explore intrinsic model of the original input signal. This project will not only enrich and improve the theories and methods of operator-based adaptive signal separation, but provide a new tool for some complex physiological signals analysis and diagnosis as well.
Keywords: Hilbert-Huang Transform; Wavelet Transform; time-frequency analysis; operator-based
Contact:
HU Xiyuan
E-mail: xiyuan.hu@ia.ac.cn