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 final destination. The methodology is thus for joint 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：本文提出了一种贝叶斯框架，用于推断包含目标潜在目标或意图（如中间航路点和/或最终目的地）的增强状态后验概率。该方法因此适用于联合跟踪与意图识别。本文在虚拟领航者框架内提出了若干潜在意图模型，这些模型能够捕捉目标隐藏意图对其瞬时行为的影响。在此背景下，本文还考虑了包括高机动目标在内的多种运动模型。目标先验未知的意图（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。本文引入序贯蒙特卡洛（粒子滤波）方法，用于同步估计目标的（运动学）状态及其意图。通过采用Rao-Blackwellisation技术以提升推断过程的统计性能。仿真数据与真实雷达测量结果验证了所提方法的有效性。