| 一种改进的DNN算法在雷达信号分选中的应用 |
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| 引用本文： | 陈春利,金炜东.一种改进的DNN算法在雷达信号分选中的应用[J].计算机应用研究,2019,36(4). |
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| 作者姓名： | 陈春利 金炜东 |
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| 作者单位： | 西南交通大学电气工程学院,成都,610031;西南交通大学电气工程学院,成都,610031 |
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| 基金项目： | 国家自然科学基金资助项目(61461051);国家科技支撑计划课题(2015BAG14B01-05) |
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| 摘 要： | 针对深度神经网络能自动学习数据深层特征的优点进行了研究，提出一种基于深度信念网络的信号分选方法，来解决传统雷达信号分选中人工提取特征的耗时、特征冗余等问题。通过堆叠多层的深度模型对原算法进行改进，克服单一模型学习力的不足，对不同信号的本质特征进行深入学习，融合各个深度模型的后验概率进行分类决策，从而进一步提高了信号的识别率。采用改进方法对七种不同类型的雷达信号进行分选识别，并与其他信号分选方法进行对比，实验结果表明，该方法取得了更好的分类效果，展现出较强的学习数据本质特征的能力，从而验证了算法的有效性和优越性。
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| 关 键 词： | 信号分选 深度信念网络 堆叠多层模型 后验概率 |
| 收稿时间： | 2017/9/15 0:00:00 |
| 修稿时间： | 2018/7/16 0:00:00 |
Application of improved DNN algorithm in radar signal sorting |
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| Chen Chunli,Jin Weidong and zhangwenqiang.Application of improved DNN algorithm in radar signal sorting[J].Application Research of Computers,2019,36(4). |
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| Authors: | Chen Chunli Jin Weidong and zhangwenqiang |
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| Affiliation: | School of Electrical Engineering,Southwest Jiao tong University,, |
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| Abstract: | The advantage of deep neural network to automatically learn the deep characteristics of data was studied. This paper proposed a signal sorting method based on multilayer deep belief networks, in order to solve the problems of time consuming in traditional radar signal selection, feature redundancy and so on. Based on the improved algorithm of depth of stacked multilayer model, it overcame the problem of insufficient to the single model of learning ability, and deeply studied the essential features of the different signal, and fused the posterior probability of the model to make a classification decision, so as to further improve the signal recognition rate. By using this method to sort 7 different types of radar emitter signal sorting. Compared with other performance signal sorting method, the experimental results show that this method obtains better classification results, and exhibits strong learning ability to nature features, thus it verifies the effectiveness and superiority of this algorithm. |
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| Keywords: | signal sorting deep belief network stacked multilayer model the posterior probability |
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