检测前跟踪中的Riemann-Langevin粒子滤波
Riemann-Langevin Particle Filtering in Track-Before-Detect
摘要：
检测前跟踪（TBD）是一种功能强大的方法，它直接为跟踪器提供传感器测量值，而无需预先检测。由于测量模型的非线性，TBD中的在线状态估计最常通过粒子滤波来解决。现有的用于TBD的粒子滤波器不将测量信息并入其建议分布中。朗之万蒙特卡罗（LMC）是一种采样方法，其建议是能够利用所有可用的知识后（即，先验和测量信息）。这封信综合了最新的进展，基于LMC滤波描述黎曼-朗之万粒子滤波器，并介绍了其新的应用TBD。我们的方法的好处是在一个具有挑战性的低噪音的情况下说明。
英文摘要：
Track-before-detect (TBD) is a powerful approach that consists in providing the tracker with sensor measurements directly without pre-detection. Due to the measurement model non-linearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in LMC-based filtering to describe the Riemann-Langevin particle filter and introduces its novel application to TBD. The benefits of our approach are illustrated in a challenging low-noise scenario.
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