海量电磁数据中雷达信号的高效分选方法 |
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引用本文: | 张强,王红卫,陈游,徐源. 海量电磁数据中雷达信号的高效分选方法[J]. 火力与指挥控制, 2016, 0(10): 150-154. DOI: 10.3969/j.issn.1002-0640.2016.10.036 |
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作者姓名: | 张强 王红卫 陈游 徐源 |
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作者单位: | 空军工程大学航空航天工程学院,西安,710038 |
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基金项目: | 陕西省自然科学基金(2012JQ8019);航空科学基金(20145596025);航空科学基金资助项目(20152096019) |
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摘 要: | 针对海量电磁数据中雷达信号难以进行快速准确分选的问题,提出一种新的聚类分选方法,即改进k-means算法的Map Reduce并行化实现方法。通过引入初始聚类中心个数k1、最大聚类中心个数kmax和距离门限rt3个参数,克服了k-means算法需要事先确定k值和易受孤立点影响的局限;基于Hadoop平台实现了对改进k-means算法的Map Reduce并行化,克服了k-means算法串行实现时间复杂度高的局限。最后,实验表明改进k-means算法取得了更高的分选准确率,Map Reduce并行化后具有良好的加速比和扩展性,能够很好地对海量电磁数据中雷达信号进行高效分选。
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关 键 词: | 海量电磁数据 雷达信号 分选 k-means算法 MapReduce |
An Efficient Sorting Method of Radar Signals in the Massive Electromagnetism Data |
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Abstract: | Aimed at the problem that it’s hard to sort the radar signals in the massive electromagnetism data quickly and accurately,a new clustering algorithm is proposed,that is improved k-means algorithm using MapReduce programming mode. The initial clustering centers number k1,the maximum clustering centers number kmax and the distance threshold rt are introduced by the improved k-means clustering algorithm,to overcome the confines of the k-means clustering algorithm that it needs the pre-determined k value and is prone to be effected by isolated individual data point. Based on the Hadoop platform,Parallel implementation of the improved k-means clustering algorithm using MapReduce programming mode is realized,to overcome the confines of the k-means algorithm that serial implementation has a high time complexity. Finally, the experimental result validates the improved k -means clustering algorithm has high sorting precision, and shows the parallel implementation of the improved k-means clustering algorithm using MapReduce programming mode owns good speedup ratio and scalability. It also can sort the radar signals in the massive electromagnetism data efficiently. |
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Keywords: | massive electromagnetism data radar signals sorting k-means algorithm MapReduce |
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