M. Mishali and Y. C. Eldar, “Blind multi-band signal reconstruction: compressed sensing for analog signals,” IEEE Transactions on Signal Processing, Vol. 57, No. 30, pp. 993–1009, March 2009.

has been cited by the following article:

• TITLE: A Mobile-Agent-Based Adaptive Data Fusion Algorithm for Multiple Signal Ensembles in Wireless Sensor Networks

  AUTHORS: Tianjing WANG, Zhen YANG, Guoqing LIU

  KEYWORDS: Wireless Sensor Networks, Mobile Agent, Compressed Sensing, Distributed Compressed Sensing, Joint Sparsity, Joint Reconstruction

  JOURNAL NAME: Wireless Sensor Network, Vol.1 No.5, December 16, 2009

  ABSTRACT: Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.