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Enhanced OTHR detection using Bayesian fusion of multipath target returns

机译:使用多路径目标返回的贝叶斯融合增强的OTHR检测

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In this paper, a target detection technique based on the fusion of multipath signals in a Bayesian framework is proposed with application to the HF Over-The-Horizon-Radar (OTHR). The detection consists of a generalized likelihood ratio test and a data fusion process, where target measurement likelihood is derived based on a set of multi-path models which are characterized by a known Doppler transition with additive Gaussian noise. The latter describes the relationship between the Doppler frequencies propagated along each of the multiple paths. A centralized Bayesian fusion is applied to compute an overall amplitude of the detection iteratively, where the posterior probability density of Doppler frequency for a specific propagation path from the underlying target is estimated via a sequential Bayesian estimator. Compared to the conventional detection method used in OTHR, the proposed detection technique is able to increase detection SNR by fusing target detections over all paths. Results from our numerical simulations are presented, which show the effectiveness of the proposed algorithm.
机译:本文提出了一种基于贝叶斯框架中多径信号融合的目标检测技术,并将其应用于HF Over-The-Horizo​​n-Radar(OTHR)。该检测由广义似然比测试和数据融合过程组成,其中目标测量似然是基于一组多路径模型得出的,该多路径模型的特征是已知的多普勒跃迁和加性高斯噪声。后者描述了沿多条路径中的每条路径传播的多普勒频率之间的关系。应用集中式贝叶斯融合来迭代地计算检测的总振幅,其中,通过顺序贝叶斯估计器来估计针对特定潜在路径从基础目标出发的多普勒频率的后验概率密度。与OTHR中使用的常规检测方法相比,所提出的检测技术能够通过融合所有路径上的目标检测来提高检测SNR。我们的数值模拟结果被提出,表明了该算法的有效性。

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