Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches
Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches
复制标题
联合目标跟踪和识别 - 第一部分：基于顺序蒙特卡罗模型的方法
DOI：
10.1109/icif.2005.1591863
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
A. Doucet
中科院分区：
文献类型：
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作者：
P. Minvielle;A. Marrs;S. Maskell;A. Doucet
研究了基于模型的联合目标跟踪与识别方法。在贝叶斯框架中，引入了参数状态空间模型类，作为广泛存在的状态空间模型的推广。除了动态之外，它们还包括一个超级参数，该参数将目标特征或行为考虑在内。对于这样的模型类，序贯蒙特卡罗方法，也称为粒子滤波，提供了一种强大的工具来进行序贯在线估计和模型选择。本文主要研究固定超参数估计和模型选择的遍历性问题。事实上，这样一个系统的无限记忆可能会导致粒子滤波的退化或发散。它回顾了解决这一问题的各种方法，从添加人工噪声的常见和基本技巧到更复杂的方法，如引入可逆跳跃马尔可夫链蒙特卡罗移动。
This paper deals with model-based approaches for joint target tracking and identification. In a Bayesian framework, parametric state-space model classes are introduced as a generalization of the widespread state-space models. In addition to the dynamic state, they include a hyper-parameter, which takes into account target features or behaviors. For such model classes, sequential Monte Carlo approaches, also known as particle filtering, provide a powerful tool to perform sequentially on-line estimation and model selection. The paper focuses on the ergodicity concern of fixed hyper-parameter estimation and model selection. Indeed, the infinite memory of such a system may lead to the particle filter degeneracy or divergence. It reviews various methods to solve this problem, from the common and basic trick of adding an artificial noise to more complex methods, such as the introduction of reversible jump Markov chain Monte Carlo moves.