Strong interference suppression for subspace judgment analysis
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摘要:
针对海洋中存在的强干扰和环境噪声导致水下目标方位估计算法性能剧烈下降的问题, 提出了一种子空间判决分析的强干扰抑制方法(SSJ), 可实现多个强干扰下的目标方位估计。根据常规波束形成粗估的目标角度区间, 利用目标−干扰−噪声子空间与导向矢量的相关性, 设置判决项和估计合适的判决阈值来分离和抑制样本协方差矩阵中的非目标信息, 降低干扰和噪声的输出功率, 同时提高输出信干噪比, 为增强阵列的目标方位分辨能力提供方法支撑。仿真和海试数据处理结果显示, SSJ方法可抑制目标角度区间外的强干扰和噪声, 明显降低了干扰的输出功率和目标主瓣附近的旁瓣级, 提高了目标方位角度的分辨力。相比于现有的子空间干扰抑制方法, 所提方法具有更加稳健的干扰抑制能力。
Abstract:In the presence of strong underwater interferences, the performance of existing target of interest (TOI) bearing estimation algorithms significantly degrades. In this paper, a subspace judgment (SSJ)-based interference suppression method is proposed, which aims to enhance the ability of TOI-bearing estimation under multiple strong interferences. Specifically, with prior knowledge of the TOI bearing interval, the proposed method builds a judgment item exploiting the correlation between the TOI-interference-noise subspace and steering vector. Then the eigenvectors not dominated by the TOI can be accurately identified with a comparison of the aforementioned threshold. Finally, the identified ones will be subtracted from the sample covariance matrix (SCM). As a result, a residual SCM that primarily contains TOI is obtained, which provides methodical support for improving the capability of TOI-bearing resolution. Simulation and experimental results demonstrate that the method effectively suppresses strong interferences outside TOI-bearing intervals. It also reduces the output power of interference and sidelobe levels while improving the capability of TOI-bearing resolution. The proposed method outperforms other state-of-the-art subspace-based interference suppression methods.
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Key words:
- Subspace judgment /
- Judgment item /
- Judgment threshold /
- Interference suppression
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图 1
${\| {{\boldsymbol\xi} _m^{\rm H}{{\boldsymbol{e}}_m}} \|^2}$ 值的仿真结果(信噪比: −15~5 dB, 快拍数: 10~50)
图 4 目标运动时, 经CBF、EAAIS和SSJ干扰抑制后的时间方位历程图和归一化功率谱 (a) 经CBF处理后的时间方位历程图; (b) 经EAAIS干扰抑制后的时间方位历程图; (c) 经SSJ干扰抑制后的时间方位历程图; (d) (a)中黑色虚线时间点处归一化功率谱
图 5 强干扰运动时, 经CBF、EAAIS和SSJ干扰抑制后的时间方位历程图和归一化功率谱 (a) 经CBF处理后的时间方位历程图; (b) 经EAAIS干扰抑制后的时间方位历程图; (c) 经SSJ干扰抑制后的时间方位历程图; (d) (a)中黑色虚线时间点处归一化功率谱
图 6 目标和干扰均运动时, 经CBF、EAAIS和SSJ干扰抑制后时间方位历程图和归一化功率谱 (a) 经CBF处理后的时间方位历程图; (b) 经EAAIS干扰抑制后的时间方位历程图; (c) 经SSJ干扰抑制后的时间方位历程图; (d) (a)中黑色虚线时间点处归一化功率谱图
图 10 第1部分历程, 经CBF、EAAIS和SSJ干扰抑制后的时间方位历程图和归一化功率谱 (a) 经CBF处理后的时间方位历程图; (b) 经EAAIS干扰抑制后的时间方位历程图; (c) 经SSJ干扰抑制后的时间方位历程图; (d) (a)中黑色虚线时间点处归一化功率谱
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[1] Vaccaro R J, Chhetri A, Harrison B F. Matrix filter design for passive sonar interference suppression. J. Acoust. Soc. Am., 2004; 115(6): 3010—3020 doi: 10.1121/1.1736653 [2] 鄢社锋, 侯朝焕, 马晓川. 矩阵空域预滤波目标方位估计. 声学学报, 2007; 32(2): 151—157 doi: 10.3321/j.issn:0371-0025.2007.02.009 [3] 韩东, 章新华, 孙瑜. 宽带最优空域矩阵滤波器设计. 声学学报, 2011; 36(4): 405—411 doi: 10.15949/j.cnki.0371-0025.2011.04.005 [4] Fan Z, Liang G, Wang Y, et al. Efficient Sub-Regional multiple-source detection based on subspace matrix filtering. IEEE Signal Process. Lett., 2014; 22(7): 943—947 doi: 10.1109/LSP.2014.2379619 [5] 范展. 水下无人平台自主被动探测技术研究. 博士学位论文, 哈尔滨: 哈尔滨工程大学, 2015 [6] 梁国龙, 赵文彬, 付进. 一种降维空域滤波矩阵的设计方法. 电子学报, 2017; 45(2): 417—423 doi: 10.3969/j.issn.0372-2112.2017.02.021 [7] Kirsteins I P, Tufts D W. Adaptive detection using low rank approximation to a data matrix. IEEE Trans. Aerosp. Electron. Syst., 1994; 30(1): 55—67 doi: 10.1109/7.250406 [8] Harrison B F. The Eigen component association method for adaptive interference suppression. J. Acoust. Soc. Am., 2004; 115(5): 2122—2128 doi: 10.1121/1.1699395 [9] 任岁玲, 葛凤翔, 郭良浩. 基于特征分析的自适应干扰抑制. 声学学报, 2013; 38(3): 272—280 doi: 10.15949/j.cnki.0371-0025.2013.03.004 [10] Ren S, Ge F X, Guo X, et al. Eigenanalysis-Based adaptive interference suppression and its application in acoustic source range estimation. IEEE J. Oceanic Eng., 2015; 40(4): 903—916 doi: 10.1109/JOE.2014.2359378 [11] Chen W, Zhang W, Wu Y, et al. Joint algorithm based on interference suppression and Kalman filter for bearing-only weak target robust tracking. IEEE Access, 2019; 7: 131653—131662 doi: 10.1109/ACCESS.2019.2940956 [12] Sun D, Ma C, Yang T C, et al. Improving the performance of a vector sensor line array by deconvolution. IEEE J. Oceanic Eng., 2020; 45(3): 1063—1077 doi: 10.1109/JOE.2019.2912586 [13] Park Y, Gerstoft P. Gridless sparse covariance-based beamforming via alternating projections including co-prime arrays. J. Acoust. Soc. Am., 2022; 151(6): 3828—3837 doi: 10.1121/10.0011617 [14] 孙大军, 张珂, 梅继丹, 等. 稀疏矢量阵信号栅瓣及对称伪峰干扰抑制. 声学学报, 2021; 46(1): 23—34 doi: 10.15949/j.cnki.0371-0025.2021.01.003 [15] 汪洋, 刘清宇, 段睿, 等. 波束形成后多干扰抵消方法. 声学学报, 2019; 44(4): 687—697 doi: 10.15949/j.cnki.0371-0025.2019.04.029 [16] Nadakuditi R, Edelman A. Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples. IEEE Trans. Signal Process., 2008; 56(7): 2625—2638 doi: 10.1109/TSP.2008.917356 [17] Ollila E, Raninen E. Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions. IEEE Trans. Signal Process., 2019; 67(10): 2707—2719 doi: 10.1109/TSP.2019.2908144 [18] Ollila E, Breloy A. Regularized tapered sample covariance matrix. IEEE Trans. Signal Process., 2022; 70: 2306—2320 doi: 10.1109/TSP.2022.3169269 [19] Nadler B. Finite sample approximation results for principal component analysis: A matrix perturbation approach. Ann. Stat., 2008; 36(6): 2791—2817 [20] Johnstone L M, Lu A Y. On Consistency and sparsity for principal components analysis in high dimensions. J. Am. Stat. Assoc., 2009; 104(486): 682—693 doi: 10.1198/jasa.2009.0121 [21] Wage K E, Buck J R. Snapshot performance of the dominant mode rejection beamformer. IEEE J. Oceanic Eng., 2014; 39(2): 212—225 doi: 10.1109/JOE.2013.2251538 [22] Cox H. Resolving power and sensitivity to mismatch of optimum array processors. J. Acoust. Soc. Am., 1973; 54(3): 771—785 doi: 10.1121/1.1913659 -
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