DYNAMIC SPARSE STATE ESTIMATION USING l1-l1 MINIMIZATION: ADAPTIVE-RATE MEASUREMENT BOUNDS, ALGORITHMS AND APPLICATIONS
نویسندگان
چکیده
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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Dynamic sparse state estimation using ℓ1-ℓ1 minimization: Adaptive-rate measurement bounds, algorithms and applications
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 the...
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