Unified tracking and fusion for airborne collision avoidance using log-polar coordinates

Collision avoidance applications require state estimators that are able to deliver estimates of relevant quantities with sufficient quality under hard real-time constraints. In this paper, we will present a unified approach to tracking and data fusion in this context. The proposed approach is easy to implement and allows for a kinematics integration of data stemming from a variety of sensor types. Initialization, prediction, and update in a common state space extended Kalman filter are elaborated. Simulation results show strengths and weaknesses of the proposed approach.