Computer Science ›› 2019, Vol. 46 ›› Issue (4): 118-122.doi: 10.11896/j.issn.1002-137X.2019.04.019

• Network & Communication • Previous Articles     Next Articles

Dictionary Refinement-based Localization Method Using Compressive Sensing inWireless Sensor Networks

WU Jian1, SUN Bao-ming2   

  1. Suzhou Institute of Trade & Commerce,Suzhou,Jiangsu 215009,China1
    No.91977 of PLA,Beijing 102249,China2
  • Received:2018-03-19 Online:2019-04-15 Published:2019-04-23

Abstract: Traditional Compressive Sensing (CS)-based localization methods divide physical space into a fixed grid and assume that all targets fall on the grid precisely,therefore formulating the localization problem into a sparse reconstruction problem.In fact,it is very difficult to find such a fix grid because of the randomness of targets.As a result,there always exists mismatch between the assumed and actual sparse dictionaries,deteriorating localization performance significantly.This paper addressed this problem and proposed a novel dictionary refinement-based localization method using CS.In this method,the true sparse dictionary is modeled as a parameterized dictionary which views grids as adjustable parameters.Based on the model,the sparse dictionary is gradually refined by dynamically adjusting the grid.Consequently,the localization problem is formulated into a joint parameter estimation and sparse reconstruction problem,and this problem is solved under variational Bayesian inference framework.Simulation results show thatthe proposed localization method is more efficient and robust compared with traditional CS-based methods.

Key words: Wireless sensor networks, Compressive sensing, Dictionary refinement, Variational bayesian inference

CLC Number: 

  • TN911.7
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