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Mota J., Deligiannis N., Sankaranarayanan A.C., Cevher V., Rodrigues M.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

pp 3332



We propose a recursive algorithm for estimating time-varying signals from a few linear measurements. The signals are assumed sparse, with unknown support, and are described by a dynamical model. In each iteration, the algorithm solves an ℓ 1 -ℓ 1 minimization problem and estimates the number of measurements that it has to take at the next iteration. These estimates are computed based on recent theoretical results for ℓ 1 -ℓ 1 minimization. We also provide sufficient conditions for perfect signal reconstruction at each time instant as a function of an algorithm parameter. The algorithm exhibits high performance in compressive tracking on a real video sequence, as shown in our experimental results