ℓ1-regularized recursive total least squares based sparse system identification for the error-in-variables - Lim, Jun-seok; Pang, Hee-Suk (SpringerPlus, 2016)
In this paper an ℓ 1 -regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed ℓ 1 -RTLS algorithm is an RLS like iteration using the ℓ 1 regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed ℓ 1 -regularized RTLS for the sparse system identification setting.