A Cooperative Localization Method Using AOA Measurements Based on Deep Neural Network

Multi-station cooperative localization system using the intersection of angle of arrival (AOA) has gained widespread application due to its low cost and strong concealment. However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of...

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Bibliographic Details
Main Authors: Han, Kuoye, Xie, Wupeng, Pei, Ruilin, Xu, Tianlin, Li, Zhuang, Pei, Xiaoshuai
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
AOA-based localization
Base stations
cooperative localization
Coordinate measuring machines
deep neural network
localization accuracy
Location awareness
Measurement errors
Simulation
Training
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Summary:Multi-station cooperative localization system using the intersection of angle of arrival (AOA) has gained widespread application due to its low cost and strong concealment. However, traditional methods based on linear least squares (LLS) are prone to low localization accuracy due to the influence of measurement errors. To address this issue, this paper proposes an AOA localization method based on deep neural network (DNN). This method is applicable to both mobile and fixed base stations. The DNN model takes measurement values and their corresponding measurement errors as network inputs and the target coordinates as network outputs. The network is trained using a large number of samples generated through simulation. Simulation results demonstrate that the proposed method outperforms traditional approaches in terms of localization accuracy and robustness.
ISSN:2768-6515
DOI:10.1109/ICET61945.2024.10672630