Abstract
Signal of interest (SOI) extraction is a vital issue in communication signal processing. In this paper, we propose two novel iterative algorithms for extracting SOIs from instantaneous mixtures, which explores the spatial constraint corresponding to the Directions of Arrival (DOAs) of the SOIs as a priori information into the constrained Independent Component Analysis (cICA) framework. The first algorithm utilizes the spatial constraint to form a new constrained optimization problem under the previous cICA framework which requires various user parameters, i.e., Lagrange parameter and threshold measuring the accuracy degree of the spatial constraint, while the second algorithm incorporates the spatial constraints to select specific initialization of extracting vectors. The major difference between the two novel algorithms is that the former incorporates the prior information into the learning process of the iterative algorithm and the latter utilizes the prior information to select the specific initialization vector. Therefore, no extra parameters are necessary in the learning process, which makes the algorithm simpler and more reliable and helps to improve the speed of extraction. Meanwhile, the convergence condition for the spatial constraints is analyzed. Compared with the conventional techniques, i.e., MVDR, numerical simulation results demonstrate the effectiveness, robustness and higher performance of the proposed algorithms.
Highlights
The problem of blind source separation arises in a wide range of application fields, such as speech processing [1], image analysis [2], medical diagnosis [3] and wireless communication [4], etc
Where bik is the attenuation coefficients, and τik denotes the propagation time delays associated with the path from the kth source signal to the ith sensor which can be represented by c−1disinθk where di, c and θk denote the position of the ith sensor, the propagation velocity and the Direction of Arrivals (DOAs) of the kth source signal, respectively
As independent component analysis (ICA) is a building-block in the constrained Independent Component Analysis (cICA) algorithm, we start with a short description
Summary
The problem of blind source separation arises in a wide range of application fields, such as speech processing [1], image analysis [2], medical diagnosis [3] and wireless communication [4], etc. In [9], the geometric source separation (GSS) algorithm, which combines the optimization criteria of source separation, while constraining the responses of multiple beams based on readily available geometric information, can be used to extract the SOIs while reducing undesired interferences It is a symmetric algorithm which recovers the source signals simultaneously whose number is same as that of the observed mixtures, though often in the. The previous cICA methods generally view the a priori information as inequality constraints and transform the BSE problem into a constrained optimization problem It means that the a priori information has been incorporated into the learning process to guarantee the algorithms converge to the desired solution.
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