最新的研究成果，本论文的主要观点为Radio transmitter identification is an emerging technology that uses RF fingerprints to identify different radiation sources, and can find important applications in the field of wireless security。 The choice of classifier is one of the key factors affecting recognition performance。 Aiming at the shortcomings of a single classifier model, the author proposes a radio transmitter identification method based on improved SVM-KNN。 First, the square integral bispectrum (SIB) algorithm is used to extract the signal fingerprint features from the steady-state signal of the mobile phone。 Then, instead of the traditional Euclidean distance calculation method, the manifold geodesic distance calculation method is used to calculate the distance between the test sample and each support vector。 Experimental studies using the same model of mobile phone show that the improved SVM-KNN algorithm is less affected by the choice of kernel function parameters, and has higher recognition accuracy without increasing the computational complexity。 We also proved the robustness of our method in a wide range of signal-to-noise ratio。不知是否符合录用要求，望您批评与指正。