Sensor Registration and Tracking from Heterogeneous Sensors with Belief Propagation

Modern sensor platforms carry an increasingly diverse range of sensors onboard, in order to estimate target positions inside their common surveillance region. Off-the-shelf sensors often provide measurements at different rates, and with different and potentially varying levels of uncertainty. Heterogeneous and asynchronous sensor networks make the sensor fusion problem more challenging as multiple measurement models and a dynamic prediction model are required. Moreover, a key challenge to address in fusion systems is that of sensor bias. Any relative bias between sensors could result in measurements not correlating with one another in a common frame of reference, and therefore vastly reducing track accuracy. This work presents novel results on a joint method that estimates both external angular bias between a radar and a camera, and the states of multiple targets. The proposed technique uses a particle-based implementation of Belief Propagation (BP), and compares with Random Finite Set (RFS)-based approaches. Initial results show that the BP approach outperforms the RFS approaches in terms of accuracy by around 50% when using the Optimal Sub-Pattern Assignment (OSPA) metric.