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To provide important prior knowledge for the DOA estimation of UAV emittersin future wireless networks, we present a complete DOA preprocessing system forinferring the number of emitters via massive MIMO receive array. Firstly, inorder to eliminate the noise signals, two high-precision signal detectors,square root of maximum eigenvalue times minimum eigenvalue (SR-MME) andgeometric mean (GM), are proposed. Compared to other detectors, SR-MME and GMcan achieve a high detection probability while maintaining extremely low falsealarm probability. Secondly, if the existence of emitters is determined bydetectors, we need to further confirm their number. Therefore, we performfeature extraction on the the eigenvalue sequence of sample covariance matrixto construct feature vector and innovatively propose a multi-layer neuralnetwork (ML-NN). Additionally, the support vector machine (SVM), and naiveBayesian classifier (NBC) are also designed. The simulation results show thatthe machine learning-based methods can achieve good results in signalclassification, especially neural networks, which can always maintain theclassification accuracy above 70\% with massive MIMO receive array. Finally, weanalyze the classical signal classification methods, Akaike (AIC) and Minimumdescription length (MDL). It is concluded that the two methods are not suitablefor scenarios with massive MIMO arrays, and they also have much worseperformance than machine learning-based classifiers.

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array