Signal Processing and Novel Technologies in Radar Systems

Radar and communication technologies operating across the same frequencies leave each system susceptible to
interference from the other, resulting in performance degradation. Current approaches to mitigating interference
place restrictions on the characteristics of the radar system, which may limit their suitability to operate in a desired environment [1].
Over the past decade a novel approach has being examined in which radar and communications are incorporated
into the same platform, creating a dual-function radar communication system (DFRC). The radar architecture
and modulation of radar pulses can be exploited by the DFRC system in order to communicate without impacting
radar performance [2]. By integrating the functionality of traditionally separate systems, cost and power consumption can be reduced and the systems more efficiently managed.
However, there are many questions concerning this novel technology that must be addressed. A theoretical framework must be developed for optimising the dual-function system to facilitate fast data transfer without compromise of radar functionality. Existing transmission waveforms are optimised to the single-function of the system
emitting them, so the waveform of a dual-function system requires a novel design. Current research does not
examine the potential for radar constructive interference to be exploited in communication systems. Machine
learning offers an alternative approach to the development of complex mathematical models for spectrum sharing of radar and communications systems. For the deployment of this technology to be realised the research must
be underpinned by experimental verification using real hardware.
The PhD project outlined by Professor Ratnarajah offers the opportunity to research and provide a meaningful
contribution to overcoming these challenges.