Abstract:
Intelligence, surveillance, and reconnaissance (ISR) systems assist the defense and military in their tactical operations by gathering movement intelligence data for trac...Show MoreMetadata
Abstract:
Intelligence, surveillance, and reconnaissance (ISR) systems assist the defense and military in their tactical operations by gathering movement intelligence data for tracking adversaries and their activities in an area-of-interest. However, there are significant spatio-temporal gaps in the collected data due to short track durations and discontinuous coverage. As a result, the ISR operators or analysts are unable to connect the incomplete set of movements to detect threats in the form of salient activities of the adversaries. Our proposed approach aims to fill this gap by developing a novel threat analytics framework that consists of an adversarial agent, powered by deep reinforcement learning, and a machine learning-based threat detector to help analysts identify salient adversarial activities in the wake of incomplete observations. The experiment results on simulated data show that the proposed framework is able to correctly identify, on an average, 99% of the threats.
Date of Conference: 02-03 November 2021
Date Added to IEEE Xplore: 30 November 2021
ISBN Information: