Parallel optimization of frequency density distribution based on non-parametric method
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摘要: 跳频序列预测是信息对抗的关键问题之一。基于跳频序列具有的伪随机特性和数据之间连续分布的相似性,采用非参数密度估计方法计算并预测出频率的大概率分布区间,进而用于引导通信的梳状灵巧干扰。针对实际应用对实时性的要求,在多结点多核平台上实现了基于消息传递机制的可扩展计算。测试表明,并行程序计算结果正确,并且具有较好的可扩展性。Abstract: Hopping-frequency forecasting is one of the key problems in information countermeasure. Based on pseudo-randomness of hopping-frequency and similarity of continuous distribution between these frequency data blocks, a novel method is presented. It applies non-parametric density estimation to predict those sections of frequency with bigger probabilities, which can guide smart comb-like jam of communication. To improve the performance of the method, a scalable computation based on message passing is implemented on multi-node computer system and is verified by the test. Key words: complex electromagnetic environment; hopping-frequency forecasting; on-parametric density estimation; R; message passing; parallel optimization
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