Audio Generation from Radar signals, for target classification

Sammanfattning: Classification in radar application are often of great interest, since one does not only want to know where a target is, but also what type of target it is. This thesis focus on transforming the radar return from a target into a audio signal. So that the classification can be done by human perception, in this case human hearing. The aim of these classification methods is to be able to distinguish between two types of targets of roughly the same size, namely birds and smaller Unmanned Aerial Vehicles (UAV). It is possible with the radar to measure the targets velocity by using the Doppler effect. To be able to distinguish in which direction the target is moving are a so called I/Q representation of the radar return used, which is a complex representation of the signal. Using signal processing techniques, we extract radar signals generated from the target. By spectral transforms it is possible to generate real valued signals from the extracted target signals. It is required to extend these signals to be able to use them as audio signals, this is done with an extrapolation technique based on Autoregressive (AR) processes. The extrapolated signals are the signals used as the audio output, it is possible to perform the audio classification in most of the cases. This project is done in collaboration with Sebastian Edman [7], where different perspectives of radar classification has been investigated. As mentioned this thesis focus on transforming the radar return into an audio signal. While Edman in his thesis [7] making use of a machine learning approach to classify the targets from the generated audio signal.