Scalable and effective artificial intelligence for multivariate radar environment

Ground based radar is a key system for aerial defence of any country. Current generations of radar use Moving Target Indicator (MTI) filter, Constant False Alarm Rate (CFAR) and Peak Detection (PD) algorithms for detection of target and rejection of non-target (static clutter, dynamic clutter, interference and noise) objects. These conventional Radar Signal Processing (RSP) techniques find their limitation when either targets are not detected or false alarms are generated due to lack of built-in intelligence. In order to eliminate both the issues, a realistic radar environment has been analyzed using Artificial Intelligence (AI). Special emphasis has been made on invalid detections caused by weather clutter, which was never worked upon previously using AI. A novel deep learning based object detection approach has been devised keeping radar environment, high accuracy requirement and real-time constraint of radar in view. The proposed approach has been validated for its efficacy through standalone testing as well as Poof of Concept (PoC) implementation on a real radar. Integration of AI model in radar platform also involves a novel implementation scheme, whereby results of AI model are augmented by conventional RSP. Inference time of AI models is a bottleneck for their use in radar systems, in this context, new strategies have also been analyzed to achieve a real-time performance for radar application. As the objective of this research is to devise scalable and effective solutions for upcoming generations of radar; therefore, the work is of practical value for realistic deployments.