Two rapid power iterative DOA estimators for UAV emitter using massive/ultra-massive receive array

Authors

Yiwen Chen
Qijuan Jie
Yiqiao Zhang
Feng Shu
Xichao Zhan
Shihao Yan, Edith Cowan UniversityFollow
Wenlong Cai
Xuehui Wang
Zhongwen Sun
Peng Zhang
Peng Chen

Document Type

Journal Article

Publication Title

Drones

Volume

7

Issue

6

Publisher

MDPI

School

School of Science / Security Research Institute

RAS ID

60211

Funders

National Natural Science Foundation of China / Major Science and Technology plan of Hainan Province / Scientific Research Fund Project of Hainan University

Comments

Chen, Y., Jie, Q., Zhang, Y., Shu, F., Zhan, X., Yan, S., & Chen, P. (2023). Two rapid power iterative DOA estimators for UAV emitter using massive/ultra-massive receive array. Drones, 7(6), article 361. https://doi.org/10.3390/drones7060361

Abstract

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide good directional measurement results, the rapid power iterative polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with the polynomial solution method to obtain the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPI-PR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to the Cramér–Rao Lower Bound (CRLB). Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance with respect to the computational complexity.

DOI

10.3390/drones7060361

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.