A method for passive sonar broadband target detection based on peak filtering in frequency-wavenumber domain
-
摘要:
在水声信号处理中, 传统的无源声呐宽带目标检测在多目标、强干扰的复杂环境中输出信噪比低, 使得检测性能急剧下降。针对此问题, 提出一种基于均匀线列阵在频域−波数域上宽带信号能量分布特性进行目标检测的方法。该方法首先将阵列信号转换到频域−波数域, 利用不同频率下波数主瓣、旁瓣宽度特征以及空间分布特征, 设计针对主瓣的判别与分配方法, 实现对同一目标不同频率下波数谱主瓣判别, 使用主瓣能量累积、主瓣数目累积的方式来形成方位谱, 从而进行目标检测。理论分析和仿真结果表明, 所提方法只利用对目标检测有突出贡献的波数主瓣, 降低了旁瓣的影响, 有效提高了无源宽带水声目标的检测能力。海上试验数据处理结果表明, 目标输出信噪比相比子带峰值能量检测算法可提高5.58 dB, 较传统能量检测可提高8.73 dB, 计算时间相比传统能量检测降低46%, 验证了所提方法的有效性与实时性。
Abstract:Traditional detection of broadband targets in passive sonars has low output signal-to-noise ratio and poor performance in a complex situation with multiple targets and strong interferences. To solve this problem, a target detection method is proposed based on the characteristics of the energy distribution of broadband signals in the frequency-wavenumber domain by using uniform linear array. The proposed method converts the array signal into the frequency-wavenumber domain and uses the characteristics of the width and the spatial distribution of the main lobes and the side lobes to discriminate the main lobes in the wavenumber domain. After discriminating the main lobes of the same target at different frequencies, the accumulation of main lobe energy and the number of main lobes are used as the azimuth spectra for target detection. The theoretical analysis and simulations show the proposed method only utilizes the main lobes which have prominent contributions to target detection, thereby reducing the influence of the side lobes dramatically and improving the detection performance significantly. The results of trial data processing show that the output signal-to-noise ratio of the proposed method can be increased by 5.58 dB compared to SPED and 8.73 dB compared to CED. In addition, the computing time is decreased by 46% compared to CED, which validates the superiority of the proposed method.
-
Key words:
- Passive sonar /
- Broadband target detection /
- Frequency-wavenumber domain /
- Peak filtering
-
表 1 干扰信号仿真参数
目标/干扰 目标 干扰1 干扰2 中心频率 (Hz) 3000 2400 3000 带宽 (Hz) 2000 600 10 方位 (°) 30 20 40 信干比 (dB) — −28 −38 表 2 算法性能比较
CED SPED E-SPAD SSED SSCD SSECD 合作目标平均输出信噪比 (dB) 1.53 4.68 — 7.51 — 10.26 合作目标“有效带宽”谱值之比 — — 1.51 — 2.99 — 计算时间 (ms) 148.8 164.2 79.6 -
[1] 李启虎, 李敏, 杨秀庭. 水下目标辐射噪声中单频信号分量的检测: 理论分析. 声学学报, 2008; 33(3): 193—196 doi: 10.3321/j.issn:0371-0025.2008.03.001 [2] 李启虎, 李敏, 杨秀庭. 水下目标辐射噪声中单频信号分量的检测: 数值仿真. 声学学报, 2008; 33(4): 289—293 doi: 10.3321/j.issn:0371-0025.2008.04.001 [3] 陈新华, 鲍习中, 李启虎, 等. 水下声信号未知频率的目标检测方法研究. 兵工学报, 2012; 33(4): 471—475 [4] Jin S L, Chi C, Li Y, et al. A supervised learning detection method with pre-processing of sparsity-based adaptive line enhancer. Chinese Journal of Acoustics, 2021; 40(4): 496—510 doi: 10.15949/j.cnki.0217-9776.2021.04.002 [5] 王逸林, 马世龙, 邹男, 等. 时空域联合的水下未知线谱目标检测方法. 电子与信息学报, 2019; 41(7): 1682—1689 doi: 10.11999/JEIT180796 [6] 马凯, 蔡昱明, 王易川, 等. 基于噪声抑制门的两级自适应线谱增强算法. 电子与信息学报, 2021; 43(3): 773—780 doi: 10.11999/JEIT200540 [7] 禚江浩, 王玲, 许可, 等. 用于被动声纳宽带目标检测的多水听器互相关方法. 信号处理, 2021; 37(9): 1691—1700 doi: 10.16798/j.issn.1003-0530.2021.09.013 [8] Bono M, Shapo B, Mccarty P, et al. Subband energy detection in passive array processing. ADA405484, 2000: 25—30 [9] Zarnich R E. A fresh look at broadband passive sonar processing. Adaptive Sensor Array Processing Workshop, Office of Naval Research, USA, 1999: 99—104 [10] 杨晨辉, 马远良, 杨益新. 峰值能量检测及其在被动声纳显示中的应用. 应用声学, 2003; 22(5): 31—35 doi: 10.3969/j.issn.1000-310X.2003.05.008 [11] 李兰瑞, 李鹏, 刘天宇, 等. 水声信号检测与识别技术研究现状. 通信技术, 2020; 53(12): 2904—2907 doi: 10.3969/j.issn.1002-0802.2020.12.004 [12] Wang Q C, Wang L J. An improved subband peak energy detection method. IEEE/OES China Ocean Acoustics, Harbin, China , 2016: 1—5 [13] Lou W X, Fu Q R, Feng K. An improved method of sub-band peak energy detection. J. Phys. Conf. Ser., 2022; 2258(1): 012066 doi: 10.1088/1742-6596/2258/1/012066 [14] Zhao A B, Wang K R, Hui J, et al. Spatial spectral enhancement of broadband signals in a towed array using deconvolved subband peak energy detection. Remote Sens., 2022; 14(13): 3008—3024 doi: 10.3390/rs14133008 [15] 楼万翔, 傅仁琦, 侯觉. 一种基于子频段的弱目标检测方法. 舰船科学技术, 2021; 43(19): 149—152 doi: 10.3404/j.issn.1672-7649.2021.10.030 [16] Hamid U, Wyne S, Ali S. On high angular resolution processing for multiple targets detection in passive underwater sensor array systems. 2021 OES China Ocean Acoustics, IEEE, Harbin, China, 2021: 873—878 [17] Jomon G, Jojish J V, Santhanakrishnan T. MVDR beamformer with subband peak energy detector for detection and tracking of fast moving underwater targets using towed array sonars. Acta Acust. united Acust., 2019; 105(1): 220—225 doi: 10.3813/AAA.919302 [18] 范文涛, 唐波, 李朋伟, 等. 一种波束谱特征加权的水下弱目标检测方法. 信号处理, 2022; 38(1): 195—201 doi: 10.16798/j.issn.1003-0530.2022.01.022 [19] 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. 声学学报, 2019; 44(4): 707—714 doi: 10.15949/j.cnki.0371-0025.2019.04.031 [20] 蒋小勇, 周胜增, 杜选民. 一种空-频联合最优滤波的被动宽带检测方法. 电子与信息学报, 2021; 43(3): 865—872 doi: 10.11999/JEIT200672 [21] Somasundaram S D, Butt N R, Jakobsson A, et al. Low-complexity uncertainty-set-based robust adaptive beamforming for passive sonar. IEEE J. Ocean. Eng., 2015(99): 1—17 doi: 10.1109/JOE.2015.2474495 [22] Zhang K Y, Wang W, Wang X L. A method to improve cross-azimuth detection of weak targets under strong interference. 2021 OES China Ocean Acoustics, IEEE, Harbin, China, 2021: 861—865 [23] Li X M, Huang H N, Li Y, et al. A broadband high resolution direction of arrival estimation algorithm based on conditional wavenumber spectral density. Chinese Journal of Acoustics, 2020; 39(4): 482—497 doi: 10.15949/j.cnki.0217-9776.2020.04.004 [24] 王聪, 刘雄厚, 孙超, 等. 基于频率着色的被动声呐宽带能量检测方法. 哈尔滨工程大学学报, 2021; 42(4): 456—462 doi: 10.11990/jheu.202007079 [25] van Trees H L. Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons, 2004 [26] Weber M, Heisler R. A frequency-domain beamforming algorithm for wideband, coherent signal processing. J. Acoust. Soc. Am, 1984; 76(4): 1132—1144 doi: 10.1121/1.391405 -
计量
- 文章访问数: 54
- 被引次数: 0