Multipath Measurements Clustering of Over-The-Horizon Radar Based on Affinity Propagation
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摘要: 电离层多层结构特性使得天波雷达(OTHR)与目标之间存在多条信号传播路径，进而可能对单目标产生多路径量测。该文考虑了天波雷达多路径量测聚类问题，其需要同时对多路径量测进行电离层传播路径辨识和聚类。由于天波雷达量测模型假设1个目标通过1种电离层传播路径至多产生1个量测，因此需要考虑多路径聚类约束。该文将相似性传播聚类扩展到多路径约束模型，并提出一种新的多路径相似性传播聚类算法。该算法通过构建多路径量测聚类的概率图模型，将聚类问题转化为概率图模型隐变量的推断问题，采用最大和置信传播算法近似求解聚类变量的最大后验概率。算法优点包括可以自动识别聚类团数目，单次消息传播的时间复杂度为量测个数和传播路径个数乘积的平方。仿真实验分析表明，所提算法较多路径多假设聚类算法具有更好的聚类性能。Abstract: The multi-layer structure of the ionosphere can support several signal propagation paths between the sky-wave Over-The-Horizon Radar (OTHR) and targets, often giving rise to multipath measurements for a single target. The problem of multipath measurements clustering for OTHR is considered, which needs to solve the problems of multipath measurements recognition and measurements clustering at the same time. OTHR measurements model assumes that a target can generate at most one measurement through an ionospheric propagation path, and multipath clustering constraints need to be considered. In this paper, affinity propagation clustering is extended to multipath constraint model, and a new multipath constraint affinity propagation clustering algorithm is proposed. The algorithm transforms the clustering problem into an inference problem by constructing the probabilistic graphical model of multipath measurements clustering, and uses the max-sum belief propagation to approximate the maximum a posteriori probability of the clustering matrix. The advantages of the algorithm include that it identifies automatically the number of clusters, and the computational complexity scales quadratically in the number of measurements and the number of propagation paths. Simulation results show that, the proposed method can outperform multiple hypothesis multipath clustering algorithm.
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表 2 内存消耗(kB)随目标个数变化表
算法 目标数 1 2 3 4 5 6 7 8 AP 70.8 71.50 70.5 77.2 92.2 96.8 98.8 99.7 MH(N=200) 77.1 72.2 78.1 103.5 102.2 97.6 100.0 102.7 MH(N=400) 83.3 80.9 78.4 106.9 116.0 110.5 104.3 105.3 MH(N=600) 81.1 102.1 125.3 94.7 145.7 95.5 133.3 128.5 MH(N=800) 92.3 107.5 119.3 117.0 130.4 139.1 131.1 136.3 
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