Extended Rank Reduction Estimator for Noncircular Sources
- 10.2991/cnci-19.2019.18How to use a DOI?
- DOA Estimation, auto-calibration, extended RARE, noncircular Source, identifiability.
Researchers have placed much attention on direction-of-arrival (DOA) estimation algorithms for noncircular sources in recent years. However, inevitable sensor errors will cause considerable deterioration of estimation performance of these algorithms. This paper proposes an extended rank reduction estimator (RARE, a kind of auto-calibration methods) for noncircular sources upon a condition of factorization of the steering vectors in the presence of sensor errors. The proposed method can realize the “decoupling” estimation of DOAs and sensor errors without any iterative process. The simulation results of two different applications of the proposed method are presented, which demonstrate that the proposed method outperforms the conventional RARE in terms of estimation accuracy and the number of sources that can be distinguished. Moreover, its performance advantage in estimation accuracy is more significant when signal-to-noise ratio is low, or the number of snapshots is small.
- © 2019, 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/).
Cite this article
TY - CONF AU - Chong Li AU - Xiaowen Li AU - Xiaobao Fu PY - 2019/05 DA - 2019/05 TI - Extended Rank Reduction Estimator for Noncircular Sources BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 129 EP - 136 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.18 DO - 10.2991/cnci-19.2019.18 ID - Li2019/05 ER -