Sparse FIR estimation of low-order systems
Document Type
Conference Proceeding
Publication Date
11-6-2012
Abstract
This paper discusses estimation of the finite impulse response (FIR) for a linear time-invariant (LTI) system. Specifically, we focus on the case where the FIR sequence is sparse and the system model is low-order; the latter is equivalent to that the Hankel matrix constructed from the FIR sequence is low-rank. These two properties motivate us to propose a unified system identification framework, which minimizes weighted sum of three norms: the ℓ 2 norm of measurement errors for data fidelity, the ℓ 1 norm of FIR sequence for its sparsity, and the nuclear norm of Hankel matrix for its low-rankness. We further develop an optimal algorithm based on the alternating direction method (ADM) for this convex program. Numerical experiments verify the effectiveness of the proposed identification framework and the developed algorithm. © 2012 IEEE.
Publication Title
2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Recommended Citation
Ling, Q.,
Shi, W.,
Wu, G.,
&
Tian, Z.
(2012).
Sparse FIR estimation of low-order systems.
2012 IEEE Statistical Signal Processing Workshop, SSP 2012, 321-324.
http://doi.org/10.1109/SSP.2012.6319693
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10937