Robust, multi-mode coherent track-before-detect for radar detection in complex sensing environments

This project will address algorithmic challenges modern multi-mode radars face in the case of hard-to-detect objects in complex background environments. It will propose coherent long-time integration (CLTI) algorithms inspired by Bayesian filtering (e.g. coherent track-before-detect) and machine learning approaches to leverage data for learning complex backgrounds.   

CLTI combines evidence of object existence across measurements collected over time, thereby improving the signal-to-noise-plus-background clutter ratio (SNCR) and consequently the probability of detection. Recent works have demonstrated the benefits of coherent track-before-detect in performing CLTI and neural network models in self-supervised learning of background clutter statistics in complex environments to characterise the “null hypothesis” of the detection problem. This project aims to build upon these two strands of work and develop CLTI algorithms for different radar modes (e.g. range-ambiguous and Doppler-ambiguous modes). A key focus will be investigating statistical detection tests to use with the CLTI outputs and related performance bounds that account for errors in the background clutter statistics. These results will be used to develop computationally efficient approximations of the proposed algorithms for implementation on low size-weight-and-power (SWAP) radar systems.