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  • J.K. Romberg
  • 2006
The purpose of this paper is to give a brief overview of the main results for sparse recovery via L optimization. Given a set of K linear measurements y=Ax where A is a Ktimes;N matrix, the recovery is performed by solving the convex program minparxpar<sub>1</sub> subject to Ax=y, where parxpar<sub>1</sub>:=Sigma <sub>t=0</sub> <sup>N-1</sup>|x(t)|. If x is(More)
We address the problem of encoding signals which are sparse, i.e. signals that are concentrated on a set of small support. Mathematically, such signals are modeled as elements in the /spl lscr//sub p/ ball for some p < 1. We describe a strategy for encoding elements of the /spl lscr//sub p/ ball which is universal in that 1) the encoding procedure is(More)
H 0 is chosen as in the static case and the observation noise covariance is taken as R k = 0:01 2 I 72272. The estimation performance of the CSKF-p Algorithm in the dynamic case is presented in Fig. 3(a). This figure depicts the mean square estimation error based on N = 50 Monte Carlo runs for N = 100 PM iterations. As can be seen, the best estimation(More)
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