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Source Direction Finding and Direct Localization Exploiting UAV Array With Unknown Gain-Phase Errors

delete2022-11-01
delete15
PRE
AI
李
李建峰 (Jianfeng Li) *
Q
Qiting Zhang
W
Weiming Deng
Y
Yawei Tang
张
张小飞 (Xiaofei Zhang)
吴
吴启晖 (Qihui Wu)
DOI:10.1109/JIOT.2022.3181450delete
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摘要

摘要

En 中文
An unmanned aerial vehicle (UAV) array is composed of multiple UAVs carrying array elements, which can sense signals synchronously through a global positioning system (GPS) trigger. However, gain-phase errors caused by synchronization errors and the inconsistent complex gains of receiving array channels result in array manifold perturbation, which makes the performance of traditional localization methods degrade or even fail. In this article, two efficient algorithms for source direction finding and direct localization using a UAV array are, respectively, presented. First, the array manifold changes with the movement of UAVs, which contributes to multiposition fusion, thus avoiding the infinite solutions of underdetermined equations. Then, the quadratic optimization problem can be constructed by using the orthogonal relation between noise subspaces and contaminated steering vectors obtained from multiple observations. Thereafter, we can construct the cost function and obtain the spectral function, from which the source directions and positions can be all estimated by grid search. Meanwhile, the gain-phase error values can be solved successively. Moreover, in order to avoid the influence of heteroscedasticity of different observation positions in direct localization, we carry out a blind weighting operation. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.
Keyword:
Location awareness
Antenna arrays
Autonomous aerial vehicles
Sensor arrays
Internet of Things
Direction-of-arrival estimation
Signal processing algorithms
Direct localization
gain-phase errors
multiposition fusion
source direction finding
unmanned aerial vehicle (UAV) array

期刊

IEEE Internet of Things Journal 封面图
IEEE Internet of Things Journal
IF:
8.9
论文数:
1.4W
被引数:
7.8W

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