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Dynamic sparse state estimation using L1-L1 minimization: Adaptive-rate measurement bounds, algorithms and applications Host Publication: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Authors: J. Mota, N. Deligiannis, A. Sankaranarayanan, V. Cevher and M. Rodrigues Publication Year: 2015 Number of Pages: 5
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 l1-l1 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 l1-l1 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.
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