Maneuvering target tracking using an unbiased nearly constant heading model

This paper addresses the problem of modeling maneuvering target motion in tracking applications. Moving targets typically follow deterministic straight-line or curved trajectories, with minor deviations due to random disturbances. As a result, modeling target motion typically involves the derivation of state transition functions based on the laws of kinematics, with the addition of uncertainty terms in the form of random noise to compensate for model mismatch. Although it is possible to construct quite accurate models, there is a trade-off between model simplicity (and, thus, ease of implementation) and model accuracy. In this paper, we present a model for target motion that is based on a Brownian description of the target's speed and heading, which allows the derivation of closed form expressions for the exact first two moments of the propagated probability density function of the target's state vector. We outline the design of tracking algorithms based on this model, and demonstrate its effectiveness in dealing with maneuvering targets based on simulations.