基于消息传递的OTHR网络测量级融合
Measurement-Level Fusion for OTHR Network Using Message Passing
摘要：
基于电离层统计模型的超视距雷达（OTHR）网络提供的多径测量数据对未知数量目标进行跟踪是一个复杂的问题，需要解决目标检测、目标跟踪、多径数据关联和电离层高度识别等4个子问题。联合解决方案是期望的，因为这四个子问题是高度相关的，但从棘手的推理问题的高维潜变量的痛苦。本文提出了一种结合置信传播（BP）和平均场（MF）近似的统一消息传递方法，用于简化复杂的推理。基于潜变量的联合概率分布函数（PDF）的因子分解对应的因子图以及将该因子分解分为BP区域和MF区域的选择，由于MF具有共轭指数模型的简单MP更新规则，因此可以用MF来近似包括目标运动状态、目标能见度状态和电离层高度在内的连续潜变量的后验PDF。对于包含一对一帧（硬）约束的离散多径数据关联，其概率密度函数用循环BP近似。最后，采用闭环方式迭代更新近似后验概率密度函数，有效地解决了目标检测、目标跟踪、多径数据关联和电离层高度识别之间的耦合问题。同时，由于直接对OTHR网络的原始多径测量进行处理，该方法具有测量级融合结构，有利于提高目标跟踪性能。它的性能表现在一个模拟的OTHR网络多目标跟踪方案。
英文摘要：
Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: target detection, target tracking, multipath data association and ionospheric height identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this paper, a unified message passing approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability distribution function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of continuous latent variables including target kinematic state, target visibility state, and ionospheric height, are approximated by MF due to its simple MP update rules for conjugate-exponential models. With regard to discrete multipath data association which contains one-to-one frame (hard) constraints, its PDF is approximated by loopy BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among target detection, target tracking, multipath data association, and ionospheric height identification. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is benefit to improving target tracking performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.
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