Integrated Camera and Radar Tracking using MultiModel Cubature Kalman Filter In this paper, a multiple camera and radar based tracking method is proposed using an interactive multimodel (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 cameraradar 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.

Interactive Multimodel Tracking of a Highly Maneuvering Target using MSPDAF with Least Squares Virtual Fusion For target cognition and data fusion, the sensors are usually considered as synchronous sampling. This assumption is often no longer valid as the data rate of infrared sensors is significantly higher than that of radar. In this paper, a novel algorithm named LS IMMMSPDAF is proposed for the least squares (LS) virtual fusion method by involving time calibration due to multisensors asynchronous sampling. As for the interactive multimodel (IMM) part, it is designed for fusing multimodal observations in order to track a highly maneuvering target with different dimensions. Experimental results of two case studies have demonstrated that our algorithm can conquer the defect of asynchronous sampling, and achieve improvement in the accuracy of track estimation than MSPDAF.