Joint kinematic and feature tracking using probabilistic argumentation
Deepak Khosla, Yang Chen · 2003
This paper describes a multi-target tracking approach based on merging traditional Kalman recursive filtering with evidential reasoning methodology. The approach fuses all the available information about targets to be tracked – both kinematic (position, velocity, etc.) and identity (attributes, features, type, class, etc.) information and uses the fused measure for data association and tracking. The proposed tracking system departs from other feature-aided tracking systems in that (1) it allows more flexible domain knowledge representation such as in the form of uncertain rules, (2) it is able to accommodate imprecise as well as partial knowledge (e.g., partial probability distribution), and (3) it uses a probabilistic argumentation system as the evidential reasoning methodology for attribute/feature fusion. The method described here is general purpose, will work on multiple-target tracking problems with both single and multi-sensor systems and is not sensor type dependent. We demonstrate the feasibility and utility of the proposed method through a multi-target tracking simulation example.