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 â„“1-â„“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 â„“1-â„“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.
Mota, J, Deligiannis, N, Sankaranarayanan, A, Cevher, V & Rodrigues, M 2015, Dynamic sparse state estimation using L1-L1 minimization: Adaptive-rate measurement bounds, algorithms and applications. in IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’15. pp. 1-5, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia, 19/04/15.
Mota, J., Deligiannis, N., Sankaranarayanan, A., Cevher, V., & Rodrigues, M. (2015). Dynamic sparse state estimation using L1-L1 minimization: Adaptive-rate measurement bounds, algorithms and applications. In IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’15 (pp. 1-5)
@inproceedings{c0ac82b984a44f8294454cab65233c52,
title = "Dynamic sparse state estimation using L1-L1 minimization: Adaptive-rate measurement bounds, algorithms and applications",
abstract = "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 â„“1-â„“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 â„“1-â„“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.",
keywords = "State estimation, sparsity,, background subtraction, motion estimation, online algorithms",
author = "Joao Mota and Nikolaos Deligiannis and Aswin Sankaranarayanan and Volkan Cevher and Miguel Rodrigues",
year = "2015",
language = "English",
pages = "1--5",
booktitle = "IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP{\textquoteright}15",
note = "2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 19-04-2015 Through 24-04-2015",
}