This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.

翻译：本文提出了一种贝叶斯框架，用于推断目标扩展状态的后验概率，该状态包含其潜在目标或意图（例如中间航路点及/或最终目的地）。因此，该方法适用于联合跟踪与意图识别。本文基于虚拟领航者公式，提出了多种新型潜在意图模型，用于刻画目标隐藏目标对其瞬时行为的影响。在此背景下，还考虑了包括高机动目标在内的多种运动模型。目标的先验未知意图（如目的地）可随时间动态变化，并在状态空间（如位置或空间区域）内取任意值。本文引入了一种序贯蒙特卡洛（粒子滤波）方法，用于同时估计目标的（运动学）状态及其意图。采用Rao-Blackwell化技术以提升推断过程的统计性能。通过仿真数据与实测雷达数据验证了所提技术的有效性。