Optimised projections for generalised distributed compressed sensing

Optimised projections for generalised distributed compressed sensing

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Different signals from the various sensors of the same scene form an ensemble. Distributed compressed sensing (DCS) rests on a new concept called the joint sparsity of the ensemble. JSM-1 is a model that describes the joint sparsity by one dictionary. Previously, the generalisation of JSM-1 was proposed where the signal ensemble depends on two dictionaries. Its compressed sensing (CS) version is considered: generalised DCS (GDCS). Instead of using random projections (random Gaussian (rGauss)), a gradient method with Barzilai–Borwein stepsize (GBB) is developed to optimise the projections in the GDCS. It enhances the reconstruction performance of the GDCS. It is verified by some experiments on the synthesised signals.


    1. 1)
      • 1. Baron, D., Wakin, M.B., Duarte, M.F., Sarvotham, S., Baraniuk, R.G.: ‘Distributed compressed sensing’,, accessed September 2013.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • 8. Nocedal, J., Wright, S.J.: ‘Numerical optimization’ (Springer, New York, USA, 2006, 2nd edn.).
    9. 9)

Related content

This is a required field
Please enter a valid email address