A tensor-based method for large-scale blind system identification using segmentation

摘要

A new method for the blind identification of large-scale finite impulse response (FIR) systems is presented. It exploits the fact that the system coefficients in large-scale problems often depend on much fewer parameters than the total number of entries in the coefficient vectors. We use low-rank models to compactly represent matricized versions of these compressible system coefficients. We show that blind system identification (BSI) then reduces to the computation of a structured tensor decomposition by using a deterministic tensorization technique called segmentation on the observed outputs. This careful exploitation of the low-rank structure enables the unique identification of both the system coefficients and the inputs. The approach does not require the input signals to be statistically independent.