Parametric dictionary learning for TWRI using distributed particle swarm optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers a distributed network of through-The-wall radars for accurate indoor scene reconstruction in the presence of multipath propagation. A sparsity based method is proposed for eliminating ghost targets under imperfect knowledge of interior wall locations. Instead of aggregating and processing the observations at a central fusion station, joint scene reconstruction and estimation of interior wall locations is carried out in a distributed manner across the network. More specifically, an alternating minimization approach is utilized to solve the associated non-convex optimization problem, wherein the sparse scene is reconstructed using the recently proposed modified distributed orthogonal matching pursuit algorithm while the wall location estimates are obtained with a novel distributed particle swarm optimization algorithm (D-PSO) proposed in this paper. Existing literature on averaging consensus is leveraged to derive the D-PSO algorithm. The efficacy of proposed approach is demonstrated using numerical simulation.

Original languageEnglish (US)
Title of host publication2016 IEEE Radar Conference, RadarConf 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008636
DOIs
StatePublished - Jun 3 2016
Event2016 IEEE Radar Conference, RadarConf 2016 - Philadelphia, United States
Duration: May 2 2016May 6 2016

Publication series

Name2016 IEEE Radar Conference, RadarConf 2016

Other

Other2016 IEEE Radar Conference, RadarConf 2016
Country/TerritoryUnited States
CityPhiladelphia
Period5/2/165/6/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications
  • Instrumentation

Fingerprint

Dive into the research topics of 'Parametric dictionary learning for TWRI using distributed particle swarm optimization'. Together they form a unique fingerprint.

Cite this