This paper presents a Bayesian framework for inferring the posterior of the augmented state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or the final destination. Thus, it is for joint object tracking and intent recognition. Several 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：本文提出一个贝叶斯框架，用于推断目标增广状态的后验概率，该状态包含其潜在目的或意图（如中间航路点和/或最终目的地），从而实现联合目标跟踪与意图识别。在虚拟领航者假设下，本文提出了多种潜在意图模型，这些模型刻画了目标隐藏目标对其瞬时行为的影响。在此框架中，还考虑了多种运动模型（包括高度机动目标的模型）。先验未知的目标意图（如目的地）可随时间动态变化，并在状态空间内取任意值（如位置或空间区域）。本文引入序贯蒙特卡洛（粒子滤波）方法，用于同时估计目标（运动学）状态及其意图，并采用拉奥-布莱克韦尔化技术提升推理过程的统计性能。通过仿真数据与真实雷达测量数据验证了所提方法的有效性。