A Committee Machine Approach for Compressed Sensing Signal Reconstruction
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 7, 1705-1717 p.Article in journal (Refereed) Published
Although many sparse recovery algorithms have been proposed recently in compressed sensing (CS), it is well known that the performance of any sparse recovery algorithm depends on many parameters like dimension of the sparse signal, level of sparsity, and measurement noise power. It has been observed that a satisfactory performance of the sparse recovery algorithms requires a minimum number of measurements. This minimum number is different for different algorithms. In many applications, the number of measurements is unlikely to meet this requirement and any scheme to improve performance with fewer measurements is of significant interest in CS. Empirically, it has also been observed that the performance of the sparse recovery algorithms also depends on the underlying statistical distribution of the nonzero elements of the signal, which may not be known a priori in practice. Interestingly, it can be observed that the performance degradation of the sparse recovery algorithms in these cases does not always imply a complete failure. In this paper, we study this scenario and show that by fusing the estimates of multiple sparse recovery algorithms, which work with different principles, we can improve the sparse signal recovery. We present the theoretical analysis to derive sufficient conditions for performance improvement of the proposed schemes. We demonstrate the advantage of the proposed methods through numerical simulations for both synthetic and real signals.
Place, publisher, year, edition, pages
2014. Vol. 62, no 7, 1705-1717 p.
Committee machine, compressed sensing, signal reconstruction, sparse recovery
Signal Processing Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-144354DOI: 10.1109/TSP.2014.2303941ISI: 000333026100008ScopusID: 2-s2.0-84897008400OAI: oai:DiVA.org:kth-144354DiVA: diva2:713438
QC 201404232014-04-232014-04-222014-04-23Bibliographically approved