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.