DOA Estimation Using Log Penalty under Large Arrays

DOI

10.2991/cnct-16.2017.26How to use a DOI?

Keywords

Direction-of-arrival (DOA), Log penalty, Sparse recovery, Large arrays

Abstract

This paper proposes a new direction-of-arrival (DOA) estimation algorithm, which is suitable for the scenario that the number of sensors is large, and is comparable with the number of samples in magnitude. Instead of utilizing classical subspace technique, sparse-recovery-based approach with log penalty is exploited. In detailed implementation, we use DC (Difference of Convex function) decomposition to solve the non-convex optimization problem, and weighted L1-norm penalty to provide the initial estimation, where the weights are constructed via the orthogonality between the noise subspace and signal subspace in large-scale random matrix theory framework. As a result, an improved DOA estimation performance is achieved. Simulation results validate the effectiveness of the proposed algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Ye TIAN
AU  - He XU
PY  - 2016/12
DA  - 2016/12
TI  - DOA Estimation Using Log Penalty under Large Arrays
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 196
EP  - 201
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.26
DO  - 10.2991/cnct-16.2017.26
ID  - TIAN2016/12
ER  -