  To provide important prior knowledge for the DOA estimation of UAV emitters
in future wireless networks, we present a complete DOA preprocessing system for
inferring the number of emitters via massive MIMO receive array. Firstly, in
order to eliminate the noise signals, two high-precision signal detectors,
square root of maximum eigenvalue times minimum eigenvalue (SR-MME) and
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 sample covariance matrix
to construct 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 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.
