Abstract
In the case of multiple nonstationary independent source signals and linear instantaneous time-varying mixing systems, it is difficult to adaptively separate the multiple source signals. Therefore, the adaptive blind source separation (BSS) problem is firstly formally expressed and compared with tradition BSS problem. Then, we propose an adaptive blind identification and separation method based on the variable learning rate equivariant adaptive source separation via independence (EASI) algorithm. Furthermore, we analyze the scope and conditions of variable-learning rate EASI algorithm. The adaptive BSS simulation results also show that the variable learning rate EASI algorithm provides better separation effect than the fixed learning rate EASI and recursive least-squares algorithms.
Highlights
There is an increasing demand for dynamic systems to be safer and more reliable [1]
The extraction of fault characteristic information is indispensable [2], the complexity of mechanical devices means that mechanical vibration signals usually have mixed and multipath effects
blind source separation (BSS) can be conducted through two popular approaches, namely second-order blind identification [13] and independent component analysis (ICA) [8]
Summary
There is an increasing demand for dynamic systems to be safer and more reliable [1]. In mechanical fault diagnosis, the extraction of fault characteristic information is indispensable [2], the complexity of mechanical devices means that mechanical vibration signals usually have mixed and multipath effects. DeYoung et al [25] applied BSS techniques to mixtures of digital communication signals in which the sources are mobile or the environment is changing, and the mixing matrix will vary with time Their results indicate that the main difficulty in the separation phase is the ill-conditioned nature of the channel matrix. We formally describe the adaptive BSS problem and propose the use of a variable learning rate EASI-based method to solve the problem of nonstationary source signals in a different slowly time-varying environment. 2) This paper applies a variable learning rate EASI algorithm for the problem of adaptive BSS for a mixing matrix that varies slowly with time and a nonstationary environment. Adaptive BSS model for linear instantaneous mixing systems and nonstationary source signals
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