Sparse System Identification in Pairs of FIR and TM Bases

TL;DR

This paper introduces a method for reconstructing sparse transfer functions using combined FIR and TM bases, demonstrating improved sparsity and reconstruction efficiency through theoretical proofs and simulations.

Contribution

It proposes a novel concatenated FIR and TM basis approach for sparse system identification, with proven uniqueness and conditions for effective l_1 minimization.

Findings

Sparse representation is unique with FIR and TM bases.

l_1 minimization accurately reconstructs the coefficient vector.

Concatenated bases yield sparser models with lower order.

Abstract

This paper considers the reconstruction of a sparse coefficient vector {\theta} for a rational transfer function, under a pair of FIR and Takenaka-Malmquist (TM) bases and from a limited number of linear frequency-domain measurements. We propose to concatenate a limited number of FIR and TM basis functions in the representation of the transfer function, and prove the uniqueness of the sparse representation defined in the infinite dimensional function space with pairs of FIR and TM bases. The sufficient condition is given for replacing the l_0 optimal solution by the l_1 optimal solution using FIR and TM bases with random samples on the upper unit circle, as the foundation of reconstruction. The simulations verify that l_1 minimization can reconstruct the coefficient vector {\theta} with high probability. It is shown that the concatenated FIR and TM bases give a much sparser representation, with much lower reconstruction order than using only FIR basis functions and less dependency on the knowledge of the true system poles than using only TM basis functions.