Abstract
Higher-order statistics based inverse filter criteria (HOS-IFC) proposed by Tugnait (1997) and Chi et al. (2002) have been widely used for blind identification and deconvolution of multiple-input multiple-output (MIMO) linear time-invariant systems with a set of nonGaussian measurements. Based on a relationship, that holds true for finite signal-to-noise ratio, between the optimum inverse filter associated with the HOS-IFC and the unknown MIMO system, an iterative FFT-based blind system identification (BSI) algorithm for MIMO systems is proposed in this paper, for which common subchannel zeros are allowed and the system order information is never needed, and meanwhile its performance is superior to the performance of Tugnait's HOS-IFC approach. Some simulation results are presented to support the efficacy of the proposed BSI algorithm.
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