Machine Learning Based Receiver Design for Radar Communications

With increasing congestion of Radio Frequency (RF) spectrum, enabling radar and communication systems to coexist is becoming an important area of research for efficient spectrum utilization. Designing radar systems that can function amidst communication interference is a major step in this direction. The non-optimality of matched filtering-based receivers under communication interference provides the need to look for alternative approaches in radar receiver design. In this thesis we propose a machine learning based radar receiver design to tackle the problem of communication interference. Three different neural network architectures were designed and evaluated. The matched filtering based Constant False Alarm Rate (CFAR) detector was considered as a baseline for the evaluations. The performance of these detectors was evaluated on signal datasets generated from two sets of parameters each with different configurations of Signal to Noise Ratio (SNR) and interference power. The results obtained from the simulations depict that most of the evaluated neural network architectures significantly outperform the baseline CFAR detector in most configurations of SNR and interference power. This shows that the designed neural network architectures are able to learn some form of filtering better than the matched filter