Context and intent enhanced target tracking

Exploitation of contextual knowledge has recently emerged as a promising approach to increase the performance of Information Fusion systems. Despite pioneering efforts in context assisted target tracking, the realm is still in its infancy, as the frameworks combining context reasoning with target tracking are not abundant. We here postulate that, in addition to physical constraints, such as the road network, knowledge of common patterns that targets pursue can significantly improve tracking accuracy and continuity. In the presented approach, we address the problem of tracking ground targets in complex urban environments, which generally poses a challenge to modern airborne surveillance systems. A target’s actions are modeled as a Markov chain with relevant context defining transition and emission probabilities. Target’s kinematics are estimated by the Interacting Multiple Models (IMM) filter that estimates the mode transition probability matrix (TPM) at each recursion step. The TPM posterior is computed by a Quasi-Bayesian estimator conditioned on the prior and the likelihood originating from target’s measurements and the context. Through extensive simulations, we demonstrate that incorporating contextual information into TPM estimation significantly improves the filtering performance compared to both the IMM filter with a fixed TPM and adaptive TPM estimation without considering contextual information.