非相干噪声减载的空域加窗解卷波束形成

doi:10.3969/j.issn.1000-1093.2021.07.013

摘要/Abstract

摘要： 针对传统波束形成器在强干扰和非相干噪声影响下多目标分辨能力以及弱目标检测能力下降问题，提出一种非相干噪声减载的空域加窗解卷波束形成算法。基于快拍数分情况估计噪声减载量，通过求解半定规划问题或特征值估计法分别得到不同快拍数条件下的噪声减载量。利用减载后的协方差矩阵进行空域加窗的波束形成得到空间能量谱，根据空域加窗波束形成器的点扩展函数对空间谱解卷得到空间谱估计。仿真及海试数据处理表明，该算法能够有效抑制强干扰旁瓣泄漏、非相干噪声对期望信号的影响，获得优于传统解卷算法的检测能力。

关键词: 声纳, 波束形成, 解卷, 噪声减载, 空域加窗

Abstract: A denconvolved beamforming with spatial windowing and incoherent noise reduction is proposed for multi-target resolution and weak target detection under the influence of strong interference and incoherent noise. The noise reduction capacity is estimated based on the number of snapshots.The noise reduction capacity under different conditions of snapshots is obtained by solving the semi-definite programming problem or estimating the eigenvalue of covariance matrix. Then the covariance matrix after reduction is used to perform spatial windowed beamforming to obtain the spatial energy spectrum，and the point spread function of spatial windowed beamformer is proposed to deconvolve the spatial spectrum for the spatial spectrum estimate in the proposed algorithm. Simulation and sea trial data processing show that the proposed algorithm can effectively suppress the effects of strong interference and incoherent noise on the desired signal，and obtain a detection capability that is superior to those of traditional deconvolution algorithms.

Key words: sonar, beamforming, deconvolution, noisereduction, spatialwindowing

中图分类号:

TN911.7

王昊，马启明. 非相干噪声减载的空域加窗解卷波束形成[J]. 兵工学报, 2021, 42(7): 1457-1462.

WANG Hao, MA Qiming. Deconvolved Beamforming with Spatial Windowing and Incoherent Noise Reduction[J]. Acta Armamentarii, 2021, 42(7): 1457-1462.

参考文献

［1］ CAPON J.High-resolution frequency-wavenumber spectrum analysis［J］.Proceedings of the IEEE，1969，57(8):1408-1418.
［2］ LI J，STOICA P，WANG Z S.On robust Capon beamforming and diagonal loading［J］.IEEE Transactions on Signal Processing，2003，51(7):1702-1715.
［3］王昊，马启明.宽带子阵域特征空间稳健对角减载波束形成［J］.电子学报，2019，47(3):74-80.
WANG H，MA Q M. Broadband robust diagonal reducing adaptive beamforming based on subarray subspace［J］.Acta Electronic Sinica，2019，47(3):74-80.(in Chinese)
［4］ SCHMIDT R.Multiple emitter location and signal parameter estimation［J］.IEEE Transactions on Antennas and Propagation，1986，34(3):276-280.
［5］ KUNDU D.Modified MUSIC algorithm for estimating DOA of signals［J］.Signal Processing，1996，48(1):85-90.
［6］ XENAKI A，GERSTOFT P，MOSEGAARD K.Compressive beamforming［J］.The Journal of the Acoustical Society of America，2014，136(1):260-271.
［7］ EDELMANN G F，GAUMOND C F.Beamforming using compressive sensing［J］.The Journal of the Acoustical Society of America，2011，130(4): EL232-7.
［8］ ZHENG J，KAVEH M.Sparse spatial spectral estimation: a covariance fitting algorithm，performance and regularization［J］.IEEE Transactions on Signal Processing，2013，61(11):2767-2777.
［9］ YANG Y X，ZHANG Y H，YANG L.Wideband sparse spatial spectrum estimation using matrix filter with nulling in a strong interference environment［J］.The Journal of the Acoustical Society of America，2018，143(6):3891-3898.
［10］ YANG T C.Deconvolved converntional beamforming for a horizontal line array［J］.IEEE Journal of Oceanic Engineering，2018，43(1):160-172.
［11］ YANG T C.Superdirective beamforming applied to SWellEx96 horizontal arrays data for source localization［J］. The Journal of

the Acoustical Society of America，2019，145(3):EL179-EL184.
［12］ HALD J.Removal of incoherent noise from an averaged cross-spectral matrix［J］.The Journal of the Acoustical Society of America，2017，142(2):846-854.
［13］蒋光禹，孙超，刘雄厚，等.非均匀对角减载最小方差无失真响应多目标分辨［J］.声学学报，2019，44(4):555-565.
JIANG G Y，SUN C，LIU X H，et al.Minimum variance distortionless response multi-source resolving using inhomogeneous diagonal unloading［J］.Acta Acustica，2019，44(4): 555-565.(in Chinese)
［14］ RICHARDSON W H.Bayesian-based iterative method of image restoration［J］.The Journal of Optical Society of America，1972，62(1):55-59.
［15］ LUCY L B.An iterative technique for the rectification of observed distributions［J］.The Astronomical Journal，1974，79(6):745-754.
［16］ STOOL M.A Krylov-Schur approach to the truncated SVD［J］.Linear Algebra and Its Applition，2012，436(8):2795-2806.

[1]	范超, 王鼎, 杨宾, 尹洁昕. 信号传播速度未知下水下多基地声纳定位算法[J]. 兵工学报, 2022, 43(3): 637-652.
[2]	武鹤龙，邱政，张维全. 侧扫声纳图像非下采样轮廓波变换域分区增强方法[J]. 兵工学报, 2021, 42(7): 1463-1470.
[3]	张羽，王朋，刘纪元，钟荣兴，韦琳哲，迟骋. 合成孔径声纳重叠相位中心与惯性导航系统联合估计运动误差算法[J]. 兵工学报, 2021, 42(3): 588-597.
[4]	邵鹏飞，王蕾，王方勇. 基于序贯蒙特卡洛与概率假设密度滤波的主动分布式声纳多目标跟踪[J]. 兵工学报, 2020, 41(5): 941-949.
[5]	张东俊，黎潇，米杨. 基于交战进程的潜艇声感知行为机理方程[J]. 兵工学报, 2020, 41(5): 958-966.
[6]	曾腾，张春华，王朋. 基于局部异常因子算法的三维声纳单帧重建研究[J]. 兵工学报, 2020, 41(3): 552-558.
[7]	贾基东，李淑秋，高善国. 一种基于Costas序列的多输入多输出声纳正交发射信号集设计方法[J]. 兵工学报, 2019, 40(8): 1680-1687.
[8]	王乐宁，喻敏，姚直象，张晓亮. Hermite分数时延滤波器在声纳信号源仿真逆波束形成中的应用[J]. 兵工学报, 2019, 40(7): 1460-1467.
[9]	张鸿伟，汤建龙，冯宗盛，柯小梅. 基于2阶锥规划的雷达干扰宽带数字波束形成优化设计[J]. 兵工学报, 2018, 39(11): 2185-2191.
[10]	毕杨, 王英民, 王奇. 双重优化的宽带聚焦波束形成算法研究[J]. 兵工学报, 2017, 38(8): 1563-1571.
[11]	夏平, 任强, 吴涛, 雷帮军. 融合多尺度统计信息模糊C均值聚类与Markov随机场的小波域声纳图像分割[J]. 兵工学报, 2017, 38(5): 940-948.
[12]	于涤非，张春华，黄勇. 改进型圆面阵的快速波束形成算法设计[J]. 兵工学报, 2017, 38(5): 959-967.
[13]	汲夏，丛卫华，杜栓平. 基于C6678多核数字信号处理器的声纳信号并行处理设计[J]. 兵工学报, 2016, 37(8): 1476-1481.
[14]	谭克，邹丽娜. 正态分布转换算法在声纳图像处理中的应用[J]. 兵工学报, 2016, 37(6): 1052-1057.
[15]	殷敬伟，刘强，陈阳，朱广平，生雪莉. 基于海豚whistle信号的仿生主动声纳隐蔽探测技术研究[J]. 兵工学报, 2016, 37(5): 769-777.