Document Type
Journal Article
Publication Title
Drones
Volume
7
Issue
4
Publisher
MDPI
School
School of Science / Security Research Institute
RAS ID
60218
Funders
National Natural Science Foundation of China
Abstract
To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.
DOI
10.3390/drones7040256
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Li, Y., Shu, F., Hu, J., Yan, S., Song, H., Zhu, W., ... & Wang, J. (2023). Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array. Drones, 7(4), 256. https://doi.org/10.3390/drones7040256