Publication

Dynamic Sparse State Estimation Using ℓ1-ℓ1 Minimization: Adaptive-rate Measurement Bounds, Algorithms and Applications

2015
Conference Papers

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. Index Terms— State estimation, sparsity, background subtraction, motion estimation, online algorithms

Official source

https://infoscience.epfl.ch/entities/publication/20ae4bf5-5841-490e-90cd-89ffc82f6b1f

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2015
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/20ae4bf5-5841-490e-90cd-89ffc82f6b1f

About this result

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Dynamic Sparse State Estimation Using ℓ1-ℓ1 Minimization: Adaptive-rate Measurement Bounds, Algorithms and Applications

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