概率PCR5融合规则在多传感器目标跟踪中的应用
Application of probabilistic PCR5 Fusion Rule for Multisensor Target Tracking
摘要：
本文定义并实现了一个非贝叶斯融合规则相结合的概率密度估计的本地（非线性）过滤器跟踪移动目标的被动传感器。该规则是对最近在DSmT框架中开发的用于融合基本信念分配的有效的比例冲突再分配规则5（PCR 5）的严格概率范例的限制。定义了概率PCR 5（p-PCR 5）的采样方法。结果表明，p-PCR 5是一个错误的建模更强大，并允许保持局部密度的模式，并保留尽可能多的整体信息固有的每个密度相结合。特别地，当假设关于其偏差太远时，p-PCR 5能够在融合后维持多个假设/模式。这个新的p-PCR 5规则已经在分布式非线性滤波应用的一个简单示例上进行了测试，以显示这种方法对未来发展的兴趣。非线性分布式滤波器是通过基本的粒子滤波技术实现的。在我们的模拟中获得的结果表明，即使模型在初始化和真实电影方面不太一致，这种基于p-PCR 5的滤波器也能够跟踪目标。
英文摘要：
This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient Proportional Conflict Redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is defined. It is shown that p-PCR5 is more robust to an erroneous modeling and allows to keep the modes of local densities and preserve as much as possible the whole information inherent to each densities to combine. In particular, p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the hypotheses are too distant in regards to their deviations. This new p-PCR5 rule has been tested on a simple example of distributed non-linear filtering application to show the interest of such approach for future developments. The non-linear distributed filter is implemented through a basic particles filtering technique. The results obtained in our simulations show the ability of this p-PCR5-based filter to track the target even when the models are not well consistent in regards to the initialization and real cinematic.
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