论文标题
使用消息传递的OTHR网络的测量级融合
Measurement-Level Fusion for OTHR Network Using Message Passing
论文作者
论文摘要
基于基于统计电离层模型的超高雷达（OTHR）网络提供的多径测量值的未知数目标是复杂的，这需要解决四个子问题：目标检测，目标跟踪，多位数据数据关联和电离层高度识别。由于四个子问题高度相关，但要遇到高维潜在变量的棘手的推理问题，因此需要关节溶液。在本文中，开发了一种统一的消息传递方法，结合了信仰传播（BP）和平均场（MF）近似，以简化棘手的推断。 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.关于包含一对一框架（硬）约束的离散多路径数据关联，其PDF通过Loopopy BP近似。最后，近似后PDF以闭环方式进行迭代更新，这对于在目标检测，目标跟踪，多径数据关联和电离层高度识别之间有效处理耦合问题。同时，由于直接处理OTHR网络的原始多径测量值，因此提出的方法具有测量级融合体系结构，这对于改善目标跟踪性能是有益的。它的性能在模拟的OTHR网络多坐标跟踪方案上进行了证明。
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.
