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In this work we implement an automatic emitter identification system using three dimensional convolutional neural networks (3D-CNN). We show that it is possible to leverage the short term spatio-temporal properties of the raw I/Q signal data using 3D-CNN and apply this to the task of transmitter identification. The time series of raw I/Q signal data exhibits a “helical” structure which encodes the intrinsic properties of the emitter as short term variations. These features are exploited by our system to create unique “fingerprints” for the emitters, which are finally used for the task of identification. Our system is end-to-end, works with the raw I/Q signal data and is able to automatically discriminate between different transmitters without using a recurrent structure, thus reducing the complexity of the system. We experimented with four novel RF transmitter datasets that we curated in an uncalibrated and uncontrolled indoor environment. The first dataset consists of four identical transmitters, the second eight identical transmitters, the third four heterogeneous transmitters and the last eight heterogeneous transmitters. We use Ettus Research USRP Software Defined Radios (USRP B210, B200, X310), ADLM Pluto and BladeRF for transmission and collect the raw signal data using a RTL Software Defined Radio (RTL SDR) receiver dongle, from each of the four and eight emitters at a time. We use the data to train and test the 3D convolutional neural network for discerning intrinsic transmitter characteristics for the task of classification. Through our methods and implementation, we show that we can identify the transmitters with ~99 % accuracy. |
