Name
Title | ||
|---|---|---|
A tensor-based method for large-scale blind system identification using segmentation. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | European Signal Processing Conference | Signal processing,Tensor,Segmentation,Matrix decomposition,Algorithm,Theoretical computer science,Finite impulse response,System identification,Mathematics,Independence (probability theory),Computation |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martijn Bousse | 1 | 3 | 1.06 |
| Otto Debals | 2 | 50 | 6.55 |
| Lieven De Lathauwer | 3 | 3002 | 226.72 |