基于相参累积预处理的空间谱估计方法

doi:10.16183/j.cnki.jsjtu.2019.332

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (11): 1209-1217.doi: 10.16183/j.cnki.jsjtu.2019.332

基于相参累积预处理的空间谱估计方法

余华兵¹，郑恩明²，陈新华²

1. 北京神州普惠科技股份有限公司, 北京100085; 2. 中国科学院声学研究所, 北京100190

收稿日期:2019-11-18 出版日期:2020-12-04 发布日期:2020-12-04
通讯作者: 郑恩明，男，副研究员，电话(Tel.): 010-82547953；E-mail: zhengembj@163.com.
作者简介:余华兵（1975-），男，湖北省襄阳市人，研究员，副总工程师，主要研究方向为水声工程及阵列信号处理.
基金资助:
国防科技创新TQ项目(2018)，海军装备预先研究项目(2019)

A Spatial Spectrum Estimation Method Based on Coherent Cumulative Preprocessing

YU Huabing¹,ZHENG Enming²,CHEN Xinhua²

1. Appsoft Technology Co., Ltd., Beijing 100085, China; 2. Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

Received:2019-11-18 Online:2020-12-04 Published:2020-12-04

摘要/Abstract

摘要： 针对最小方差无畸变响应空间谱估计(MVDR)方法稳定性问题，提出一种基于相参累积预处理的空间谱估计方法.该方法采用复解析小波变换将接收阵拾取数据转换为一定频带的复解析数据，并按空间平滑处理思想对复解析数据进行分组处理；充分利用传感器数据相位信息，对各组复解析数据进行相移补偿和累积处理，获得一组信噪比含有量较高的数据，并在时域采用多点累积处理方式对新数据构造协方差矩阵；依据协方差矩阵自身的正交特性实现空间谱估计.数值仿真和实测数据处理结果表明：相比MVDR方法和对角加载MVDR方法，该方法通过对接收阵拾取数据进行时域复解析变换和相参累积预处理，改变了构建协方差矩阵的数据来源，通过多个采样点累积实现满秩协方差矩阵的稳定获取.该方法依据空间方位与各传感器数据之间的相位差关系，通过两次指数函数等列式求和运算处理，有效提高了空间谱估计的稳定性.

关键词: 空间谱估计；复解析变换；相参累积；多点累积

Abstract: For the stability problem of spatial spectrum estimation based on the minimum variance distortionless response (MVDR) method, a kind of spatial spectrum estimation method based on coherent cumulative preprocessing is proposed. First, complex analytic data with a certain frequency band is transformed by complex analytic wavelet transform from the collected data of receiving array, and the sub-group data is obtained according to the idea of spatial smoothing from the complex analytic data. Next, making full use of the phase information of sensor data, a group of data with high signal-to-noise ratio is obtained by accumulating the complex analytic data of each sub-group after time delay compensation. Then, the covariance matrix of the new data is constructed via multi-point cumulative processing in time domain. Finally, spatial spectrum estimation is realized according to the orthogonal property of the covariance matrix. The processing results of numerical simulation and measured data show that, compared with the MVDR method and the diagonal loading MVDR method, the data source of constructing the covariance matrix is changed in this method through time domain complex analytic transform and coherent accumulation pretreatment. The full rank covariance matrix is stably obtained by multiple sampling points accumulation in this method. According to the relation of spatial bearing and the phase difference of the sensor data, this method can effectively improve the stability of spatial spectrum estimation via double exponential function addition.

Key words: spatial spectrum estimation; complex analytic transformation; coherent accumulation; multipoint accumulation

中图分类号:

TB 566

余华兵，郑恩明，陈新华. 基于相参累积预处理的空间谱估计方法[J]. 上海交通大学学报, 2020, 54(11): 1209-1217.

YU Huabing,ZHENG Enming,CHEN Xinhua. A Spatial Spectrum Estimation Method Based on Coherent Cumulative Preprocessing[J]. Journal of Shanghai Jiaotong University, 2020, 54(11): 1209-1217.

参考文献［16］

［1］	ZHENG E M, CHEN X H, YU H B, et al. Robust high-resolution beam-forming based on high order cross sensor processing method［J］. Journal of Systems Engineering and Electronics, 2015, 26(5): 932-940.
［2］	CHEN X H, LIU C, YU H B, et al. Robust broadband beam-forming based on the feature of underwater target radiated noise［J］. China Ocean Engineering, 2016, 30(6): 1004-1011.
［3］	VOROBYOV S A, GERSHMAN A B, LUO Z Q. Robust adaptive beamforming using worst-case performance optimization: A solution to the signal mismatch problem［J］. IEEE Transactions on Signal Processing, 2003, 51(2): 313-324.
［4］	YOO D S. Subspace-based DOA estimation with sliding signal-vector construction for ULA［J］. Electronics Letters, 2015, 51(17): 1361-1363.
［5］	崔琳, 李亚安, 房媛媛, 等. 一种基于支持向量机的对角加载鲁棒波束形成方法［J］. 兵工学报, 2013, 34(5): 598-604.
	CUI Lin, LI Ya’an, FANG Yuanyuan, et al. The robust diagonal loading beamforming method using support vector machines［J］. Acta Armamentarii, 2013, 34(5): 598-604.
［6］	郑恩明, 张恩宾, 孙长瑜, 等. 一种基于重置协方差矩阵的波束形成优化方法［J］. 振动与冲击, 2015, 34(17): 185-190.
	ZHENG Enming, ZHANG Enbin, SUN Changyu, et al. An optimization approach for beam-forming based on reset covariance matrix［J］. Journal of Vibration and Shock, 2015, 34(17): 185-190.
［7］	唐孝国, 张剑云, 洪振清. 一种改进的MVDR相干信源DOA估计算法［J］. 电子信息对抗技术, 2012, 27(6): 6-10.
	TANG Xiaoguo, ZHANG Jianyun, HONG Zhen-qing. DOA estimation of coherent signals via improved MVDR algorithm［J］. Electronic Information Warfare Technology, 2012, 27(6): 6-10.
［8］	周彬, 赵安邦, 龚强, 等. 基于对角减载的水声阵列SMI-MVDR空间谱估计技术［J］. 系统工程与电子技术, 2014, 36(12): 2381-2387.
	ZHOU Bin, ZHAO Anbang, GONG Qiang, et al. Underwater acoustic array SMI-MVDR spatial spectral estimation based on diagonal reduction［J］. Systems Engineering and Electronics, 2014, 36(12): 2381-2387.
［9］	LIU Y, XIE C, ZHANG Y R. Direction of arrivals estimation for correlated broadband radio signals by MVDR algorithm using wavelet［J］. China Communications, 2017, 14(3): 190-197.
［10］	郑恩明, 黎远松, 陈新华, 等. 改进的最小方差无畸变响应波束形成方法［J］. 上海交通大学学报, 2016, 50(2): 188-193.
	ZHENG Enming, LI Yuansong, CHEN Xinhua, et al. Improved bearing resolution approach for MVDR beam-forming［J］. Journal of Shanghai Jiao Tong University, 2016, 50(2): 188-193.
［11］	李智忠, 许忠良, 李海涛, 等. 基于傅里叶变换的快速TAMVDR算法［J］. 舰船科学技术, 2016, 38(1): 85-89.
	LI Zhizhong, XU Zhongliang, LI Haitao, et al. Fast TAMVDR algorithm based on fourier transform［J］. Ship Science and Technology, 2016, 38(1): 85-89.
［12］	YANG L S, MCKAY M R, COUILLET R. High-dimensional MVDR beamforming: Optimized solutions based on spiked random matrix models［J］. IEEE Transactions on Signal Processing, 2018, 66(7): 1933-1947.
［13］	李冰, 汪永明, 黄海宁. 基于时域解析估计的多重信号分类波束形成方法［J］. 上海交通大学学报, 2019, 53(8): 928-935.
	LI Bing, WANG Yongming, HUANG Haining. Multiple signal classification beam-forming method based on time domain analysis［J］. Journal of Shanghai Jiao Tong University, 2019, 53(8): 928-935.
［14］	张家凡, 易启伟, 李季. 复解析小波变换与振动信号包络解调分析［J］.振动与冲击, 2010, 29(9): 93-96.
	ZHANG Jiafan, YI Qiwei, LI Ji. Complex analytic wavelet transform and vibration signals envelope-demodulation analysis［J］. Journal of Vibration and Shock, 2010, 29(9): 93-96.
［15］	郑恩明, 陈新华, 宋春楠. 基于全相位预处理的低旁瓣波束形成方法［J］. 兵工学报, 2018, 39(10): 1971-1978.
	ZHENG Enming, CHEN Xinhua, SONG Chunnan. Low side-lobe beam-forming method based on all-phase preprocessing［J］. Acta Armamentarii, 2018, 39(10): 1971-1978.
［16］	陈新华, 郑恩明. 基于分组时延预处理的时域波束形成方法［J］. 应用声学, 2019, 38(4): 545-552.
	CHEN Xinhua, ZHENG Enming. Time domain beam-forming algorithm based on sub-group & time delay preprocessing［J］. Journal of Applied Acoustics, 2019, 38(4): 545-552.

[1]	郑恩明1，黎远松2，陈新华1，余华兵1，孙长瑜1. 改进的最小方差无畸变响应波束形成方法[J]. 上海交通大学学报（自然版）, 2016, 50(02): 188-193.
[2]	张维，杨士莪，黄益旺，宋扬. 基于微扰法的快速声速剖面反演[J]. 上海交通大学学报（自然版）, 2013, 47(08): 1286-1291.
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[4]	陈晓,李亚安,李余兴,蔚婧. 基于距离加权的概率数据关联机动目标跟踪算法[J]. 上海交通大学学报（自然版）, 2018, 52(4): 474-479.
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基于相参累积预处理的空间谱估计方法

A Spatial Spectrum Estimation Method Based on Coherent Cumulative Preprocessing

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基于相参累积预处理的空间谱估计方法

A Spatial Spectrum Estimation Method Based on Coherent Cumulative Preprocessing

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