Blind decorrelation and deconvolution algorithm for multiple-input multiple-output system: I. Theorem derivation

Details Blind decorrelation and deconvolution algorithm for multiple-input multiple-output system: I. Theorem derivation Blind decorrelation and deconvolution algorithm for multiple-input multiple-output system: I. Theorem derivation

Nov 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999spie.3807..200y&link_type=abstract

Proc. SPIE Vol. 3807, p. 200-209, Advanced Signal Processing Algorithms, Architectures, and Implementations IX, Franklin T. Luk;

Statistics

Scientific paper

The problems of blind decorrelation and blind deconvolution have attracted considerable interest recently. These two problems traditionally have been studied as two different subjects, and a variety of algorithms have been proposed to solve them. In this paper, we consider these two problems jointly in the application of a multi-sensor network and propose a new algorithm for them. In our model, the system is a MIMO system (multiple-input multiple-output) which consists of linearly independent FIR channels. The unknown inputs are assumed to be uncorrelated and persistently excited. Furthermore, inputs can be colored sources and their distributions can be unknown. The new algorithm is capable of separating multiple input sources passing through some dispersive channels. Our algorithm is a generalization of Moulines' algorithm from single input to multiple inputs. The new algorithm is based on second order statistics which require shorter data length than the higher order statistics algorithms for the same estimation accuracy.

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