用电离层参数的GMRF模型进行OTHR多目标跟踪
OTHR multitarget tracking with a GMRF model of ionospheric parameters
摘要：
电离层是由超视距雷达（OTHR）发射的无线电波的传播介质。电离层参数，通常，虚拟电离层高度（VIH），需要进行坐标配准OTHR多目标跟踪和定位。电离层参数的不准确性对超视距雷达的目标定位有很大的不利影响。因此，研究精确的电离层参数模型和估计方法以及相应的目标跟踪算法，对提高超视距雷达的定位精度具有重要意义。本文在OTHR目标跟踪中考虑了电离层随位置的变化以及电离层的空间相关性。我们使用高斯马尔可夫随机场（GMRF）模型的VIH，提供了一个更准确的表示的VIH OTHR目标跟踪。基于期望条件最大化和GMRF模型的VIH，我们提出了一种新的联合优化解决方案，称为ECM-GMRF，同时进行目标状态估计，多径数据关联和VIH估计。在ECM-GMRF中，利用电离层探测器和OTHR的测量值来估计VIH，从而更好地估计VIH，从而提高数据关联和目标状态估计的精度，反之亦然。仿真结果表明了该算法的有效性。
英文摘要：
The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.
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