Joint target tracking and identification in complex traffic scene

In practical road traffic scene, targets usually face high ground clutter, high and variable motion, high nonlinearity, which lead to targets tracking or identification challenging. What's more, tracking and identification are usually interdependent in reality, and thus it is promising to solve them jointly. In this paper, we propose a novel joint tracking and identification (JTI) scheme to handle such problems involving coupled tracking and identification, i.e., JTI problems. Specifically, we formulate the JTI problem in complex traffic scene using a hybrid system. Then, by exploiting the generalized Bayes risk for JTI, we derive analytical estimator and decider for the coupling of tracking and identification in complex road targets motion. Furthermore, an unscented Kalman filter-expected mode augmentation-based estimation strategy is creatively developed to improve both estimation and decision performance. In additions, a joint performance evaluation metric is presented to assess the performance the joint of the proposed JTI scheme. Finally, two simulation examples under different traffic scenarios demonstrate that the proposed JTI approach outperforms the traditional tracking-then-identification and identification-then-tracking methods in joint performance.