Persistently active block sparsity with application to direction-of-arrival estimation of moving sources

J. Zheng, M. Kaveh

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

Abstract

In this paper, the problem of recovering inconsistent sparse models from multiple observations is considered. A new method is developed by introducing a novel objective function, which exploits both block-level and element-level sparsities and promotes persistence in activity within a block. Then, we use a SVD-based method to reduce its computational complexity. Application of the method to the Direction-Of-Arrival (DOA) estimation of moving sources using a sensor array is presented and a simulation example is shown as a demonstration of the promising performance of the method in a moving DOA setting, particularly when sources are very close to each other.

Original languageEnglish (US)
Title of host publication2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages393-396
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico
Duration: Dec 13 2011Dec 16 2011

Publication series

Name2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Other

Other2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Country/TerritoryPuerto Rico
CitySan Juan
Period12/13/1112/16/11

Keywords

  • Block Sparsity
  • DOA
  • Persistent Activity

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