• Doppler
• micro-Doppler
• Radar
• Target

Within the security and defense domain, radar is more and more applied in the confined and crowded urban and littoral environments. Consequently, there is a demand for detecting and classifying a wider range of small targets such as mopeds, dismounts, animals, birds, flocks of birds, and small Unmanned Aerial Vehicles (UAVs). In the last few years, the popularity of small UAVs is increasing and is expected to increase even further. Both in the military domain and in civil domain mini-UAVs are more widely used. Therefore, the importance of security systems able to detect and classify them is increasing as well. Basically, detection of these smaller targets requires lowering the detection threshold, with respect to both target radar cross section (RCS) and radial velocity.
In these crowded environments, full situation awareness can be maintained only if target classification can be done reliably and rapidly. Rapid classification allows filtering-out objects that are irrelevant for the current mission, alleviating the tracker load. For this first rapid classification, distinction between broad target classes may be sufficient. Depending on the mission, these broad classes could be man-made object, i.e., a potential threat, and bio-life, i.e., a non-threat. In a second classification step, it is desired to provide further information by extracting features as speed, size and type of target. In this thesis, the potential of exploiting micro-Doppler properties for both classification steps will be discussed. MATLAB source used for Micro-Doppler ‘signature’ extraction makes use of Spectrogram function, implemented by Fast-Fourier transform algorithm. The proposed approach is verified on simulated dataset and feature extraction algorithm is tested on real radar measurements collected with Ku-band FMCW radar.