Blind signal separation based on widely linear complex autoregressive process of order one

Abstract

Abstract In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.

Keywords

• Blind source separation
• Complex auto-regressive model
• Generalized autocorrelation
• Gradient learning