Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-Massive Receive Array

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

doi:10.3390/drones7060361

Cited by 2 publications

(1 citation statement)

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“…In the pursuit of swift direction finding (DF) for transmitters aboard UAVs in future wireless networks, researchers have delved into developing innovative solutions. One such approach, outlined in [14], presents a streamlined DOA estimation architecture tailored for massive multiple-input multiple-output (MIMO) receiver arrays. In parallel, ref.…”

Section: Introductionmentioning

confidence: 99%

Polarization Direction of Arrival Estimation for Vector Array of Unmanned Aerial Vehicle Swarm

Lan,

Wang,

Dong

et al. 2023

Electronics

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Aiming at the problem of the excessive error of direction of arrival (DOA) estimation caused by the position disturbance of a UAV swarm during flight, a robust polarization-DOA estimation method based on sparse Bayesian learning (SBL) is proposed. First, the algorithm decomposes the covariance matrix of the received data of the UAV swarm vector array and then constructs the determination matrix of the UAV position coordinates by exploiting the orthogonality of the eigenvalues and eigenvectors. Then, the optimal solution of the semi-positive definite programming (SDP) problem is solved using the constrained global least square method, and the exact self-positioning coordinates of UAVs are obtained. Second, we construct a spatially discrete grid to model the received data of the UAV group vector array. The SBL theory is then applied to obtain the posterior probability distribution of the sparse signal matrix. The sparsity of the signal matrix is controlled with a hyperparameter, and the estimation of the DOA is conducted using a fixed-point iteration to obtain the maximum posterior estimate of the signal matrix. Finally, according to the estimated DOA, the polarization parameter is obtained from the constructed objective function of the polarization parameter estimation. The simulation results show that the proposed algorithm achieves higher accuracy and robustness than the traditional 2D DOA estimation algorithm in the direction-finding system for UAV swarm vector arrays.

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Section: Introductionmentioning

confidence: 99%