Efficient multi-sensor extended target tracking using GM-PHD filter

This work deals with the efficient fusion of multiple disparate sensors, namely THz radar, stereo camera and lidar for autonomous vehicles. In particular, we develop a target tracking algorithm that is object agnostic i.e. we seek to detect any potential object in the scene and track it while also preserving extended target characteristics such as length and width. To this end, we first use conventional clustering and labelling methods in order to generate consistent features from each sensor independently. The features from each sensor are then transformed into a set of bounding boxes located in both range and cross range. The bounding box parameters are then fed into the proposed efficient multi-sensor target tracking algorithm. This is achieved by modifying the Gaussian mixture-PHD filter (GM-PHD) by incorporating a set of class labels that associate a state to a set of sensors. The performance of the proposed method target tracking method is verified using both synthetic and real world data.