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基于時差多參分選的多層感知器網(wǎng)絡(luò)脈間識別

陳濤,  王天航,  郭立民

陳濤, 王天航, 郭立民. 基于時差多參分選的多層感知器網(wǎng)絡(luò)脈間識別[J]. 電子與信息學(xué)報, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913
引用本文: 陳濤, 王天航, 郭立民. 基于時差多參分選的多層感知器網(wǎng)絡(luò)脈間識別[J]. 電子與信息學(xué)報, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913
CHEN Tao, WANG Tianhang, GUO Limin. Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913
Citation: CHEN Tao, WANG Tianhang, GUO Limin. Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913

基于時差多參分選的多層感知器網(wǎng)絡(luò)脈間識別

doi: 10.11999/JEIT170913 cstr: 32379.14.JEIT170913

  • (哈爾濱工程大學(xué)信息與通信工程學(xué)院 哈爾濱 150001)

基金項(xiàng)目: 

國家自然科學(xué)基金(61571146)，中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(HEUCFP201769)，武器裝備預(yù)研基金項(xiàng)目

詳細(xì)信息
    作者簡介:

    陳濤：陳 濤： 男，1974年生，教授，博士生導(dǎo)師，研究方向?yàn)殡娮訉古c信號檢測. 王天航： 男，1992年生，碩士生，研究方向?yàn)殡娮訉古c信號檢測. 郭立民： 男，1977年生，副教授，碩士生導(dǎo)師，研究方向?yàn)殡娮訉古c信號檢測.

  • 中圖分類號: TN957.51

Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting

  • CHEN Tao    WANG Tianhang    GUO Limin

Funds: 

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769), Weapon Equipment Pre- Research Foundation of China

  • 摘要: 現(xiàn)代戰(zhàn)爭中，雷達(dá)系統(tǒng)發(fā)展迅速。為識別復(fù)雜的雷達(dá)信號調(diào)制模式以及混合體制雷達(dá)，該文提出一種基于多站獲取脈沖時差參數(shù)聯(lián)合其他脈沖描述字分選的辦法，利用多層感知器神經(jīng)網(wǎng)絡(luò)得到脈間調(diào)制識別結(jié)果。該文通過時差參數(shù)與其他脈沖描述字去交錯解決傳統(tǒng)脈沖重復(fù)周期估計(jì)算法無法對復(fù)雜的脈間調(diào)制方式進(jìn)行估計(jì)。利用訓(xùn)練好的多層感知器，獲取完成去交錯后的脈沖序列其特征向量，獲得其脈間調(diào)制類型識別。通過實(shí)驗(yàn)仿真，在脈沖丟失率不高于20%情況下，對復(fù)雜脈間調(diào)制方式的正確識別概率在90%以上。
    Abstract: In modern warfare, the radar system is developing rapidly. To recognize complex modulation mode of radar signal and hybrid pulse repetition interval radar, this paper proposes a sorting method based on multi station acquired pulse time-difference parameter combined with other pulse description words, taking advantage of the multi-station TDOA from the same emitter is similar to sort emitter signal pulse, and finally got the recognition result with the Multi-Layer Percetron (MLP) neural network. Traditional Pulse Repetition Interval (PRI) estimation algorithms estimate complex pulse interval modulation invalidly. In this paper, to solve this problem pulse time-difference parameter and other pulse description words are used. The feature vector of de-interlace pulse sequence is acquired and the result of pulse interval modulation type recognition is obtained with the trained MLP neutral network. Through experimental simulation, the correct recognition probability of the complex pulse interval modulation method is more than 90% in the case of the pulse loss rate is not more than 20%.
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    doi: 10.3969 /j.issn.1673-5692.2016.02.008.
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    MA Shuang, WU Haibin, LIU Zheng, et al. Method for emitter TDOA sorting based on recursive extended histogram [J]. Journal of National University of Defense Technology, 2012, 34(5): 83-89. doi: 10.3969/j.issn.1001-2486.2012.05.017.
    MA Xiantong, LUO Jingqing, and MENG Xianghao. Signal sorting and positioning method for similar radiation sources based on time difference of arrival[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2363-2368. doi: 10.11999/JEIT141480.
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    [14] KAUPPI J P and MARTIKAINEN K. An efficient set of features for pulse repetition interval modulation recognition [C]. IET International Conference on Radar System, Edinburgh, UK, UK, 2007: 1-5. doi: 10.1049/CP:20070548.
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    NIE Xiaowei. Radar signal pre-sorting based on K-means algorithm[J]. Electronic Science and Technology, 2013, 26(11): 55-58. doi: 10.3969/j.issn.1007-7820.2013.11.015.
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  • 文章訪問數(shù):  1456
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2017-09-28
  • 修回日期:  2018-01-10
  • 刊出日期:  2018-07-19

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