Venkata Pathuri Bhuvana, Mario Huemer,
"Integrated Camera and Radar Tracking using Multi-Model Cubature Kalman Filter"
: Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP 2019), IEEE, 12-2019, ISBN: 978-1-7281-2723-1
Integrated Camera and Radar Tracking using Multi-Model Cubature Kalman Filter
Sprache des Titels:
Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP 2019)
In this paper, a multiple camera and radar based tracking method is proposed using an interactive multi-model (IMM) based cubature Kalman filter (CKF). In real world scenarios, object movement cannot be characterized with a
single motion model. Furthermore, measurements from a single camera/radar may not contain relevant information to achieve desired tracking accuracy for critical applications. The approach proposed in this paper utilizes multiple motion models and the measurements from multiple camera and radar sensors to perform tracking. The proposed method operates a CKF with information weighted sensor fusion for each motion model. Furthermore, the likelihood of each motion model at a given time is calculated using the measurement residuals from all the camera and radar sensors. In the considered simulation set up, the proposed method achieves better tracking accuracy than an IMM based on the extended Kalman filter method and better switching capability among different motion models compared to the single sensor scenario.