Target Detection using Autoencoders in a Radar Surveillance System

Autoencoders are a type of neural network that reproduce in the ideal case the input signal at the output. In an intermediate step of these networks, a code, which should contain all relevant information of the input signal, is created. In this work, an average code for the background of the observed area is created and deviations from this background code are used to detect targets in the observed region. The Autoencoder is trained with available background data and additionally with profiles created by noise in the dynamic range of the radar. Results are shown for range profiles measured with a 94GHz radar.