The fingerprint characteristic of mobile terminal’s signal is unique and it can be used to identify the source of the signal. In all the characteristics, the characteristic of transient signals has been more favoured because of the greater diversity. However, the duration of transient signal is extremely short and difficult to accurately detect. Therefore, in order to successfully obtain the fingerprint characteristic, most of the research results are based on the laboratory environment. In this paper, through the methods of differential constellation trajectory and neural network, we realize the extraction and recognition of fingerprint characteristic of mobile phone’s transient signal in the real environment. Meanwhile, by controlling the distance between the terminal and the base station, we also studied the recognition of fingerprint characteristic under different noise conditions.

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