A Passive Broadband Detection Method Based on Space-frequency Joint Optimal Filtering
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摘要: 常规宽带能量检测在多目标、强干扰环境下输出信噪比(SNR)降低,检测性能大幅度下降。针对此问题,该文提出一种将子阵导向最小方差(STMV)宽带空域自适应波束形成与频域Eckart滤波结合的空-频联合最优滤波宽带检测方法。该方法首先通过子阵导向最小方差波束形成进行空间自适应处理,利用自适应波束形成的干扰抑制能力在空域实现最优滤波;然后通过最大似然估计实时估计信号和噪声的功率谱,构造Eckart滤波对自适应波束形成的输出分配不同权重进行加权滤波,从而实现频域信噪比最大化。所提方法通过空-频联合最优滤波,降低空域旁瓣干扰和频带内噪声的影响,使得输出信噪比最大,从而有效地改善目标宽带检测能力,提高被动声呐的宽带检测性能。仿真和试验数据处理结果验证了该方法的有效性。
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关键词:
- 被动声呐 /
- 常规宽带能量检测 /
- 子阵导向最小方差波束形成 /
- Eckart滤波
Abstract: The output Signal-to-Noise Ratio(SNR) of conventional broadband energy detection is reduced in multi-target and strong interference environment, and the detection performance is great reduced. In order to solve the problem, a broadband detection method based on space-frequency joint optimal filtering which combines subarray STeered Minimum Variance(STMV) broadband adaptive beamforming with Eckart filtering is proposed. Firstly, the spatial adaptive processing is carried out by subarray STMV beamforming, and the optimal filtering is realized in spatial domain by using the interference suppression ability of adaptive beamforming. Then, the power spectrum of signal and noise is estimated by maximum likelihood estimation, and Eckart filter is constructed to assign different weights to the output of adaptive beamforming to maximize the output SNR in frequency domain. The influence of spatial sidelobe interference and noise in the frequency band are reduced to make the output SNR maximum by the proposed method. The broadband detection ability of the target can be effectively improved and the broadband detection performance of passive sonar is also improved. The simulation and experimental data processing results verifiy the effectiveness of the method. -
表 1 4种算法性能比较
CBF STMV SASTMV 本文算法 合作目标平均输出信噪比(dB) –6.5 –4.8 –5.2 5.8 计算时间(s) 0.54 2.02 0.85 0.91 -
SUOJOKI T, TABUS I, and MERTSALMI P. A novel target detection method for passive broadband system[C]. The Seventh European Conference on Underwater Acoustics, Delft, Netherland, 2004: 999–1006. ZARNICH B E. A fresh look at broadband passive sonar processing[C]. The Adaptive Sensor Array Processing Workshop, Lexington, USA, 1999: 99–104. 杨晨辉, 马远良, 杨益新. 峰值能量检测及其在被动声呐显示中的应用[J]. 应用声学, 2003, 22(5): 31–35. doi: 10.3969/j.issn.1000-310X.2003.05.008YANG Chenhui, MA Yuanliang, and YANG Yixin. Peak energy detection with application to passive sonar display[J]. Journal of Applied Acoustics, 2003, 22(5): 31–35. doi: 10.3969/j.issn.1000-310X.2003.05.008 权恒恒, 徐晓男, 杜栓平. 基于隐马尔科夫模型的SPED弱目标检测算法[J]. 声学与电子工程, 2016(1): 1–4, 9.QUAN Hengheng, XU Xiaonan, and DU Shuanping. Weak target detection algorithm for SPED based on Hidden Markov model[J]. Acoustics and Electronics Engineering, 2016(1): 1–4, 9. 郑兆宁, 向大威. 水声信号被动检测与参数估计理论[M]. 北京: 科学出版社, 1983.ZHENG Zhaoning and XIANG Dawei. Parametric Estimation Theory and Passive Detection of Underwater Acoustic Signal[M]. Beijing: Science Press, 1983. MEHTA S K, FAY J, and MACIEJEWSKI P. A modified Eckart post-beamformer filter for improved detection using broadband features[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, Atlanta, USA, 1996, 6: 3045–3048. doi: 10.1109/ICASSP.1996.550518. 王聪, 刘雄厚, 范文涛, 等. 基于着色处理的被动声纳宽带能量检测方法[C]. 中国声学学会水声学分会2019年学术会议论文集, 南京, 中国, 2019: 516–518.WANG Cong, LIU Xionghou, FAN Wentao, et al. Passive sonar broadband energy detection based on coloring[C]. Underwater Acoustics Society of China Academic Conference 2019, Nanjing, China, 2019: 516–518. KROLIK J and SWINGLER D. Multiple broad-band source location using steered covariance matrices[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(10): 1481–1494. doi: 10.1109/29.35386 KIM J S and LEE J H. MVDR method using subband decomposition for high frequency resolution in passive sonar system[C]. Undersea Defence Technology, Cheju, Korea, 2002. LI Jian, STOICA P, and WANG Zhisong. Doubly constrained robust capon beamformer[J]. IEEE Transactions on Signal Processing, 2004, 52(9): 2407–2423. doi: 10.1109/TSP.2004.831998 SOMASUNDARAM S D. Wideband robust capon beamforming for passive sonar[J]. IEEE Journal of Oceanic Engineering, 2013, 38(2): 308–322. doi: 10.1109/JOE.2012.2223560 毛卫宁, 钱进. 一种低复杂度的稳健自适应波束形成[J]. 应用声学, 2019, 38(4): 540–544. doi: 10.11684/j.issn.1000-310X.2019.04.010MAO Weining and QIAN Jin. A low-complexity robust adaptive beamformer[J]. Journal of Applied Acoustics, 2019, 38(4): 540–544. doi: 10.11684/j.issn.1000-310X.2019.04.010 周胜增, 杜选民. 稳健的子带子阵级导向最小方差波束形成算法[J]. 声学学报, 2019, 44(4): 707–714. doi: 10.15949/j.cnki.0371-0025.2019.04.031ZHOU Shengzeng and DU Xuanmin. A robust subband subarray steered minimum variance beamforming algorithm[J]. Acta Acustica, 2019, 44(4): 707–714. doi: 10.15949/j.cnki.0371-0025.2019.04.031 王昊, 徐晓男, 马启明. 一种利用少快拍数据的宽带干扰鲁棒性抑制算法[J]. 电子与信息学报, 2019, 41(4): 851–857. doi: 10.11999/JEIT180505WANG Hao, XU Xiaonan, and MA Qiming. A robust broadband interference suppression algorithm based on few snapshots[J]. Journal of Electronics &Information Technology, 2019, 41(4): 851–857. doi: 10.11999/JEIT180505 李涛, 蒋小勇, 周胜增. 基于宽带稳健STMV波束形成的相关检测方法[J]. 声学技术, 2019, 38(5): 600–603. doi: 10.16300/j.cnki.1000-3630.2019.05.020LI Tao, JIANG Xiaoyong, and ZHOU Shengzeng. Correlation detection based on wideband robust STMV beamforming[J]. Technical Acoustics, 2019, 38(5): 600–603. doi: 10.16300/j.cnki.1000-3630.2019.05.020 刘倩, 朱安珏. 基于二阶锥规划的稳健低旁瓣自适应波束形成[J]. 声学技术, 2020, 39(3): 379–384. doi: 10.16300/j.cnki.1000-3630.2020.03.021LIU Qian and ZHU Anjue. Second-order cone programming based robust low sidelobe adaptive beamforming[J]. Technical Acoustics, 2020, 39(3): 379–384. doi: 10.16300/j.cnki.1000-3630.2020.03.021 KNABE D S. Time-domain beam signals for adaptive beamforming[C]. Undersea Defence Technology, Stockholm, Sweden, 2019.