Abstract:
Radar Systems continuously emits electromagnetic pulses to illuminate object of interests in the environment and receive back-scattered signal. These back-scattered signals
called echoes are collected by the Radar receiver and are integrated in a two-dimensional
grid to formulate a raw 2D-map. This data is further processed using FFT based MTD
techniques and coherent integration is performed. Afterwords, other signal processing
and detection algorithms are applied to extract moving targets in the presence of clutter
and interference. Convectional target detection generally used in radar include Constant
False Alarm Radar (CFAR). CFAR detector is based on statistical models and may not
provide optimal results in dynamic environment scenarios which includes strong clutter,jamming ,interference, weather & atmospheric affects. Furthermore, CFAR detector
is sub-optimal for targets with rotors due to micro-doppler effects.
AI based technique have potential to be used in Radar for target detection/ classification. Due to unavailability of actual radar data, simulated datasets are widely used
in literature for the training and testing of AI based models which results in biased
trained networks. In this thesis, research is done for usage of AI algorithms for Radar
target detection with special focus on training datasets. For the generation of simulated dataset, a software simulator is developed to emulate radar transmitter, antenna,
receiver, targets, and clutter scenario using MATLAB. Two deep learning based models namely Faster-RCNN and YOLO-v4 are trained and tested on the simulated radar
dataset. Actual radar data files are utilized to produce a large dataset and both deep
learning models are evaluated on it. Subsequently, training on actual radar data is performed and results are compared with conventional target detection technique as well
as with the networks trained on simulated dataset.
