基于二階統(tǒng)計(jì)量盲源分離算法的無源雷達(dá)同頻干擾抑制研究

呂曉德; 孫正豪; 劉忠勝; 張漢良; 劉平羽

doi:10.11999/JEIT190178

基于二階統(tǒng)計(jì)量盲源分離算法的無源雷達(dá)同頻干擾抑制研究

doi: 10.11999/JEIT190178

呂曉德^{1, 2},
孫正豪^{1, 2, 3, ,},
劉忠勝^{1, 2},
張漢良^{1, 2, 3},
劉平羽^{1, 2, 3}

1.
中國科學(xué)院空天信息創(chuàng)新研究院北京 100094
2.
微波成像技術(shù)國家級(jí)重點(diǎn)實(shí)驗(yàn)室北京 100190
3.
中國科學(xué)院大學(xué) 北京 100049

詳細(xì)信息

作者簡介:
呂曉德：男，1969年生，研究員，研究方向?yàn)榛陉嚵屑夹g(shù)的的新體制雷達(dá)系統(tǒng)及其應(yīng)用

孫正豪：男，1995年生，碩士生，研究方向?yàn)闊o源雷達(dá)信號(hào)處理

劉忠勝：男，1977年生，副研究員，研究方向?yàn)楦缮鍿AR信號(hào)處理

張漢良：男，1993年生，碩士生，研究方向?yàn)榛贚TE信號(hào)的無源雷達(dá)信號(hào)處理

劉平羽：男，1994年生，碩士生，研究方向?yàn)闊o源雷達(dá)信號(hào)處理

通訊作者:
孫正豪　sunzhenghao17@mails.ucas.ac.cn

中圖分類號(hào): TN958.97
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出版歷程
- 收稿日期: 2019-03-25
- 修回日期: 2019-11-25
- 網(wǎng)絡(luò)出版日期: 2019-12-14
- 刊出日期: 2020-06-04

Research on Suppressing Co-channel Interference of Passive Radar Based on Blind Source Separation Using Second Order Statistics

Xiaode Lü^{1, 2},
Zhenghao SUN^{1, 2, 3
, ,},
Zhongsheng LIU^{1, 2},
Hanliang ZHANG^{1, 2, 3},
Pingyu LIU^{1, 2, 3}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
National Key Lab of Microwave Imaging Technology, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要:
針對基于長期演進(jìn)(LTE)信號(hào)的無源雷達(dá)存在同頻基站干擾的問題，該文提出一種基于2階統(tǒng)計(jì)量的盲源分離算法，該算法是在卷積混合模型下，通過多通道最小均方(LMS)算法實(shí)現(xiàn)分離信號(hào)之間的相關(guān)性最小。由于各發(fā)射基站信號(hào)之間統(tǒng)計(jì)不相關(guān)，當(dāng)分離信號(hào)之間的相關(guān)性達(dá)到最小時(shí)，完成觀測信號(hào)的分離。在此基礎(chǔ)上，改進(jìn)了傳統(tǒng)無源雷達(dá)信號(hào)處理的流程，增加了分離同頻干擾基站直達(dá)波和多徑雜波的步驟，實(shí)現(xiàn)了對同頻干擾基站雜波的抑制。通過仿真分析，驗(yàn)證了算法的有效性，為基于LTE信號(hào)的無源雷達(dá)數(shù)據(jù)處理提供了參考。
- 無源雷達(dá) /
- 長期演進(jìn)信號(hào) /
- 同頻干擾抑制 /
- 盲源分離 /
- 2階統(tǒng)計(jì)量
Abstract:
For the problem of co-frequency base station interference in passive radar based on Long Term Evolution (LTE) signal, an algorithm based on blind source separation using second order statistics is proposed. The presented algorithm is based on convolution mixed model, and achieves the minimum correlation among separated signals through multi-channel Least-Mean-Square (LMS) algorithm. Without statistical correlation among the signals of each transmitting base station, the separation of the observed signals is completed when the separated signals achieve the minimum correlation. On this basis, the traditional signal processing for passive radar is improved. The steps of separating co-frequency interference clutter consisting of both direct-path and multipath clutter are added, which can suppress the clutter interference of co-channel base station. Simulation and analysis verify the effectiveness of the algorithm. The algorithm provides a reference for data processing of passive radar based on LTE signal.
- Passive radar /
- Long Term Evolution (LTE) signal /
- Co-channel interference suppression /
- Blind source separation /
- Second order statistics

HTML全文

圖 1 盲源分離模型結(jié)構(gòu)圖

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圖 2 信號(hào)卷積混合濾波器模型

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圖 3 信號(hào)后向分離濾波器模型

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圖 4 第i個(gè)通道的分離系統(tǒng)示意圖

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圖 5 無源雷達(dá)信號(hào)處理流程圖

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圖 6 傳統(tǒng)無源雷達(dá)雜波對消能量對比圖

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圖 7 觀測信號(hào)與源信號(hào)的散點(diǎn)圖

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圖 8 傳統(tǒng)處理流程后的互模糊距離剖面圖

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圖 9 分離信號(hào)與源信號(hào)散點(diǎn)圖

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圖 10 對消能量比較圖

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圖 11 改進(jìn)的處理流程后的互模糊距離剖面圖

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圖 12 噪聲對雜波對消比的影響

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表 1 多通道LMS算法總結(jié)

參數(shù)：$N$= 源數(shù)目，$M$= 觀測信號(hào)數(shù)目　　　一般假設(shè)$N = M$ 　　　 ${K_i}$= 分離濾波器階數(shù)，$i = 1,2, ··· ,N$ 　　　 ${\mu _{ij}}$= 步長，$i,j = 1,2, ··· ,N$，且$i \ne j$
輸入：${x_i}\left( t \right) = $觀測信號(hào)，$i = 1,2, ··· ,N$
初始化：${ {{W} }_{ {{ij} } } }\left( 0 \right) = { {{{\textit{0}}} }_{ {{Kj} } } }$, $i,j = 1,2, ··· ,N$，且$i \ne j$ 　　　　 ${ {{Y} }_i}\left( t \right) = { {{{\textit{0}}} }_{Ki} }$, $i = 1,2, ··· ,N$ 　　　　 ${K_i}$選取$K \ge \max \left\{ {{K_i},i = 1,2, ··· ,N} \right\}$ 　　　　 ${\mu _{ij}}$根據(jù)輸入數(shù)據(jù)進(jìn)行調(diào)整
計(jì)算：對$t = 1,2, ··· $，迭代計(jì)算：　　　 ${y_i}\left( t \right) = {x_i}\left( t \right) - \displaystyle\sum\nolimits_{j \ne i,j = 1}^N { {{W} }_{ij}^{\rm{T} }\left( {t - 1} \right){ {{Y} }_j}\left( t \right)} $ 　　　 ${{{W}}_{ij}}\left( t \right) = {{{W}}_{ij}}\left( {t - 1} \right) + {\mu _{ij}}{y_i}\left( t \right){{{Y}}_j}\left( t \right)$ 　　　 ${{{Y}}_i}\left( t \right) = {\left[ {{y_i}\left( {t - 1} \right),{y_i}\left( {t - 2} \right), ··· ,{y_i}\left( {t - {K_i}} \right)} \right]^{\rm{T}}}$ 　　　 $i,j = 1,2, ··· ,N,i \ne j$
終止條件：前后兩次分離信號(hào)之間相關(guān)性系數(shù)的變化量小于所設(shè) 　　　　　的誤差門限
輸出：${y_i}\left( t \right) = $分離信號(hào)，$i = 1,2, ··· ,N$

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表 2 回波通道1仿真參數(shù)

	主基站信號(hào)		同頻干擾基站信號(hào)
	時(shí)延(μs)	衰減(dB)	時(shí)延(μs)	衰減(dB)
直達(dá)波	0	0	0.07	–1
多徑1	0.13	–8	0.20	–9
多徑2	0.29	–11	0.36	–12
多徑3	0.42	–15	0.59	–15
弱多徑	0.16～1.95	–20～>–30	0.16～1.95	–20～>–30
目標(biāo)1	11.39	–30
目標(biāo)2	17.15	–37

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表 3 回波通道2仿真參數(shù)

	主基站信號(hào)		同頻干擾基站信號(hào)
	時(shí)延(μs)	衰減(dB)	時(shí)延(μs)	衰減(dB)
直達(dá)波	0.03	–2	0	–1
多徑1	0.19	–8	0.16	–9
多徑2	0.35	–14	0.36	–13
多徑3	0.46	–19	0.49	–14
弱多徑	0.16～1.95	–20～–30	0.16～1.95	–20～–30

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