ICASSP 2022accepted0 citations

Joint Source Localization and Association Through Overcomplete Representation Under Multipath Propagation Environment

Yuan Liu, Zhi-Wei Tan, Andy W. H. Khong, Hongwei Liu

Abstract

This work addresses the source localization and association problem in a multipath propagation environment. By focusing on the limitation of the prior information in practical applications, we propose a target localization and association method based on iterative optimization with semi-unitary constraint and eigen-decomposition techniques. In contrast to the previous works, the proposed method can localize spatial sources and associate the incident paths to each source without prior knowledge pertaining to the propagation environment. Moreover, the proposed approach can be applied to an arbitrary array geometry without reducing the effective array aperture. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

BibTeX
@inproceedings{icassp2022_jointsourcelocal,
  title = {Joint Source Localization and Association Through Overcomplete Representation Under Multipath Propagation Environment},
  author = {Yuan Liu and Zhi-Wei Tan and Andy W. H. Khong and Hongwei Liu},
  booktitle = {ICASSP 2022},
  year = {2022}
}
Joint Source Localization and Association Through Overcomplete Representation Under Multipath Propagation Environment · ICASSP 2022