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Blind Source Separation by Nuclear Norm Minimization and Local Recoverability Analysis

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4 Author(s)
Tanaka, T. ; Dept. of Aerosp. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Langbort, C. ; Mestha, L.K. ; Gil, A.E.

We propose a new blind source separation (BSS) algorithm that is effective when Hankel matrices constructed from individual source signals are near low-rank and satisfy a certain near-orthogonality condition. Source separation is achieved by finding a nonsingular reverse-mixing operation that minimizes nuclear norms of Hankel matrices constructed from estimated source signals. The new formulation results in a non-convex optimization problem involving a reverse-mixing matrix. Preliminary analysis of local recoverability of source signals as well as few numerical simulations are presented in this letter.

Published in:

Signal Processing Letters, IEEE  (Volume:20 ,  Issue: 8 )