Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 15, Issue 2, Pages 1282-1292Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2015.2487989
Keywords
Distributed compressive sensing (DCS); Bayesian inference; signal reconstruction
Funding
- EPSRC [EP/K033700/1]
- Natural Science Foundation of China [61401018, U1334202]
- Fundamental Research Funds for the Central Universities [2014JBM149]
- State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University [RCS2014ZT08]
- Chinese Ministry of Education [313006]
- Engineering and Physical Sciences Research Council [EP/K033700/1] Funding Source: researchfish
- EPSRC [EP/K033700/1] Funding Source: UKRI
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Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intrasignal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra-and intersignal correlations. The proposed approach is able to address-networked sensing system applications with privacy concerns and/or for a fusion-center-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quickly.
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