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An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network.

Jing Liu1, Kaiyu Huang2, Guoxian Zhang3

  • 1School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China. elelj20080730@gmail.com.

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|April 21, 2017
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Summary
This summary is machine-generated.

A new distributed compact sensing matrix pursuit (DCSMP) algorithm enables decentralized sensor networks to efficiently recover signals. This method accurately identifies common and innovation components, even with unknown sparsity levels.

Keywords:
JSM-1distributed compact sensing matrix pursuit (DCSMP) algorithmdistributed compressed sensing

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Area of Science:

  • Signal Processing
  • Distributed Systems
  • Networked Sensing

Background:

  • Decentralized sensor networks lack a central fusion center, posing challenges for signal recovery.
  • Existing distributed compressed sensing algorithms often rely on random sensing matrices, limiting applicability in real systems.

Purpose of the Study:

  • To propose a novel algorithm, distributed compact sensing matrix pursuit (DCSMP), for decentralized signal recovery.
  • To leverage deterministic sensing matrices tailored for real acquisition systems.
  • To address the challenge of unknown sparsity in decentralized scenarios.

Main Methods:

  • The DCSMP algorithm employs a two-part strategy: common and innovation support set estimation.
  • Common support set estimation involves fusing information from neighboring sensor nodes.
  • Innovation support set estimation projects the measurement vector to isolate unknown components, followed by orthogonal matching pursuit (OMP).

Main Results:

  • The DCSMP algorithm decouples the estimation of common and innovation components, preventing errors in one from affecting the other.
  • It is proven that accurate estimation of the common support set leads to correct identification of the innovation set.
  • The algorithm successfully handles unknown sparsity levels due to the independence of the two estimation processes.

Conclusions:

  • DCSMP offers an effective solution for signal recovery in decentralized sensor networks using deterministic sensing matrices.
  • The algorithm's robustness to unknown sparsity and its decoupled estimation process enhance its practical applicability.
  • This work advances distributed compressed sensing by providing a computationally and communicatively efficient method for sensor nodes.