Millimeter-wave radar has been widely adopted in intelligent transportation systems. Modern millimeter-wave radars' high resolution makes a single target yield multiple measurements (point cloud measurements), turning target tracking into an extended target tracking (ETT) problem. The uncertainty in radar measurement sources coupled with the complex spatial distribution of measurements fundamentally challenges ETT algorithms. Conventional ETT algorithms with cluster-then-associate frameworks partition point cloud measurements predominantly through density, failing to exploit target shape information and resulting in suboptimal clustering efficacy. This leads to inaccurate associations between measurements and extended targets, ultimately degrading overall tracking accuracy. This paper proposes a novel approach to circumvent the performance limitations caused by clustering errors in traditional methods. First, we propose a clustering-free closed-loop ETT framework that incorporates the prior target's shape information as feedback. Subsequently, we develop a data association method that leverages the inherent correlation between point cloud measurements and target shape. The associated measurements are then probabilistically fused and integrated into a Kalman filter for state updating. In simulated and real-world datasets, compared with the traditional method, clustering before association, we have validated the effectiveness of the proposed method.

With the increasing demand for traffic situation awareness, extended traffic target (ETT) tracking is a significant yet challenging task especially in tough scenarios with dense and various ETTs. Due to the spatial proximity of ETTs and a noisy sensor, it is challenging for multi-target tracking algorithms to effectively distinguish and track ETTs. To improve the accuracy and robustness of ETT tracking in tough traffic scenarios, we analyze the interaction among ETTs and the lane constraint. Firstly, we develop an interactive motion model for collision avoidance to address trajectory confusion when ETTs are in close proximity. Additionally, we propose a lane constraint method that models lanes as pseudo measurements and constrains the motion of ETTs via pseudo update. Considering the complexity and extendibility, the extended target trajectory probability hypothesis density (ETTPHD) filter is adopted to achieve a more accurate estimation of ETT trajectories. Specifically, we realize the proposed interactive motion model and lane constraint method based on the ETTPHD (IC-ET-TPHD) filter. Performance comparisons between our proposed filter and other algorithms are conducted through both simulations and experiments.

Joint tracking and classification (JTC) of vehicles is a crucial yet challenging task in intelligent transport and automotive systems. The advent of high-resolution modern sensors necessitates treating vehicles as extended targets. Current extended target tracking (ETT) algorithms provide shape estimations for vehicles, making shape size the most intuitive and accessible feature for classification. This paper contributes two key elements to achieve the JTC of vehicles. For one thing, we introduce the rectangular constraints and customize distinguishable measurement models using modified Gaussian processes (GP). For another thing, based on the customized GP models, we strengthen the role of class in the conditional extended target probability hypothesis density (ET-PHD) filter. Subsequently, we propose a class-enhanced JTC-ET-PHD filter and its Gaussian mixture implementation, enabling simultaneous kinematic, shape, and class estimation of vehicles. Finally, numerical results validate the proposed shape estimation and JTC method, affirming their effectiveness in addressing JTC challenges.

Weak target recognition, tracking and track management with a low signal-to-noise ratio (SNR) are always tricky problems. Probability hypothesis density (PHD) filtering propagates the first-order multi-target moment to obtain the best Poisson approximation to multi-target density. The PHD filtering does not consider explicit associations between measurements and targets, which is computationally efficient. But it cannot distinguish different targets or extract the time series of track states. Based on track-before-detect (TBD) strategies, this paper proposes labeled PHD (LPHD) filtering and derives its close-form solution, which identifies targets with a unique label. It is derived based on rigorous Bayes criteria, finite set statistics and Kullback-Leibler divergence minimization approximation. The separable TBD-based observation likelihood is conjugate to the Poisson mixture prior for LPHD filtering. Under the point-target assumption, the multi-hypothesis assignments of pixel-to-target are implemented with Murty’s K-shortest path algorithm for LPHD filtering. Additionally, sequential Monte Carlo (SMC) implementations under the nonlinear non-Gaussian assumption are devised. Finally, simulations exhibit good performance in low SNR scenarios.