Sparse Signal Reconstruction using Weight Point Algorithm


  • Koredianto Usman School of Electrical Engineering & Informatics Bandung Institute of Technology (ITB), Jalan Ganesha 10, Bandung 40132
  • Hendra Gunawan Faculty of Mathematics and Natural Sciences Bandung Institute of Technology (ITB), Jalan Ganesha 10, Bandung 40132,
  • Andriyan B. Suksmono School of Electrical Engineering & Informatics Bandung Institute of Technology (ITB), Jalan Ganesha 10, Bandung 40132



compressive sampling, convex combination, convex polytope, sparse reconstruction, l1-norm, weight point.


In this paper we propose a new approach of the compressive sensing (CS) reconstruction problem based on a geometrical interpretation of l1-norm minimization. By taking a large l1-norm value at the initial step, the intersection of l1-norm and the constraint curves forms a convex polytope and by exploiting the fact that any convex combination of the polytope's vertexes gives a new point that has a smaller l1-norm, we are able to derive a new algorithm to solve the CS reconstruction problem. Compared to the greedy algorithm, this algorithm has better performance, especially in highly coherent environments. Compared to the convex optimization, the proposed algorithm has simpler computation requirements. We tested the capability of this algorithm in reconstructing a randomly down-sampled version of the Dow Jones Industrial Average (DJIA) index. The proposed algorithm achieved a good result but only works on real-valued signals.


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How to Cite

Usman, K., Gunawan, H., & Suksmono, A. B. (2018). Sparse Signal Reconstruction using Weight Point Algorithm. Journal of ICT Research and Applications, 12(1), 35-53.




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