Sparsity regularized recursive total least-squares
Digital Signal Processing: A Review Journal, cilt.40, sa.1, ss.176-180, 2015 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 40 Sayı: 1
- Basım Tarihi: 2015
- Doi Numarası: 10.1016/j.dsp.2015.02.018
- Dergi Adı: Digital Signal Processing: A Review Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.176-180
- Anahtar Kelimeler: Adaptive filtering, System identification, Sparse representation, Total least-squares
- Kırklareli Üniversitesi Adresli: Evet
Özet
This paper introduces a new family of recursive total least-squares (RTLS) algorithms for identification of sparse systems with noisy input vector. We regularize the RTLS cost function by adding a sparsifying term and utilize subgradient analysis. We present ℓ1 norm and approximate ℓ0 norm regularized RTLS algorithms, and we elaborate on the selection of algorithm parameters. Simulation results show that the presented algorithms outperform the existing RLS and RTLS algorithms significantly in terms of mean square deviation (MSD). Furthermore, we demonstrate the virtues of our automatic selection for regularization parameter when ℓ1 norm regularization is applied.