Destination and time-series inference of moving objects via conditionally Markov process

Abstract This paper presents a destination and time-series inference algorithm for tracking moving targets. The destination of the object is considered the intent, and inference and state estimation are performed in the Bayesian framework. To describe the destination-aware target motion, we construct the state transition model using a conditionally Markov process. We introduce a multiple model to achieve simultaneous intent and time-series inferences. Given finite destination candidates, the maximum a posteriori hypothesis is chosen as the destination. For time-series inference, local estimates obtained from Kalman filters are fused to yield target state estimates. To address unspecified terminal conditions, the proposed algorithm incorporates parameter correction techniques based on relative geometry. Numerical simulations are performed to validate the proposed inference algorithm.