Convolutional Neural Networks for Radio Source Detection

This paper introduces a Convolutional Neural Network (CNN) architecture for radio event and source detection. The upper triangle of the auto-correlation matrix is extracted as the feature input to an uni-dimensional (1D) CNN and trained to detect the presence of a source in the sampled signal and estimate the number of signals. Since, the number of source signals present in the sampled waveform can vary between 0 and $L-1$ , where $L$ is the number of sensors in the array, the network is modeled as a multi-class, multi-label classification problem. The proposed method is robust to a varying number of incoming signals to resolve and Signal to Interference Plus Noise Ratio (SINR). The CNN architecture is trained and tested with the number of sources varying between 0 and 4. The source detection architecture is introduced and its efficacy is validated using simulations closely replication real-time RF events.