Enabling and directing real-time cognitive radar transmitter optimization.

As the available wireless spectrum grows more crowded with increased usage from high bandwidth telecommunications applications, it becomes infeasible for many other users of wireless spectrum to continue operating with static, inflexible methods. Among these users are radar systems, which have historically been allocated large sections of bandwidth. In order to adapt and coexist with new technology in a dynamically managed environment, next generation radars must be able to adjust their spectral configuration in real time. The research presented in this dissertation provides a framework that can be used for determining transmission constraints over both spatial direction and signal frequency. While existing research has demonstrated how to optimize radar transmitters using adjustable amplifier matching networks, such optimizations have not been able to complete quickly enough for use in real-time adaptation. To accelerate these optimizations, this dissertation presents a faster method for evaluating the performance of transmit amplifiers using a software-defined radio (SDR) and a load-pull extrapolation method using deep learning image completion techniques. Additionally, the accelerated optimization technique has been adapted for use with the pulse-to-pulse waveform agility paradigm of cognitive radars. Finally, the impact on Doppler detection accuracy of modifying the radar transmit chain during a coherent radar processing interval is analyzed, along with techniques for correcting the resulting distortions