Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing
2014 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 97, 146-151 p.Article in journal (Refereed) Published
Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data.
Place, publisher, year, edition, pages
2014. Vol. 97, 146-151 p.
Compressed sensing, Sparse recovery, Fusion, Signal reconstruction, Progressive reconstruction, Low latency
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-143423DOI: 10.1016/j.sigpro.2013.10.019ISI: 000331506000013ScopusID: 2-s2.0-84887790177OAI: oai:DiVA.org:kth-143423DiVA: diva2:706611
QC 201403212014-03-212014-03-212014-03-21Bibliographically approved