采用条件波数谱密度函数的宽带高分辨方位谱估计算法
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摘要
针对宽带高分辨方位估计存在方位估计偏差大、算法复杂度高等问题,提出了一种基于条件波数谱密度(Conditional Wavenumber Spectral Density based,CWSD-based)的宽带高分辨方位谱估计算法.该算法利用条件波数谱密度将阵列信号转换到频率-波数空间,宽带信号能量在该空间的坐标呈现与入射角相关的线性分布,通过借鉴直线检测原理,实现邻近目标的高分辨方位估计,且无需预估角度和信源数等信息。仿真结果表明,该算法理论分辨率与处理最高频率成反比,估计均方误差约为0.1°,对阵形畸变鲁棒,运算效率高。海上试验数据表明,本文方法在方位分辨率、弱目标检测、非目标向噪声抑制、稳健性等方面都优于宽带常规波束形成和最小方差无畸变算法,在实际海洋中可实现超低旁瓣高分辨波达方向估计。
A broad-band high-resolution Direction-of-Arrival(DOA)estimation algorithm named the Conditional Wavenumber Spectral Density based(CWSD-based)is proposed in this paper aimed at the problem of large error and high complexity of wide-band.The super-resolution DOA estimation,minimal main-lobe width,ultra-low side-lobe and high precision target resolution performance can be obtained after transforming the array signals to f-k space and using the characteristic of wideband signal power distributed as a line on the Conditional Wavenumber Spectral Density.Moreover,there are no DOA pre-estimates of the incident signals and the number of sources to be required in this algorithm.Computer simulations indicate that the theoretical resolution of the algorithm is inversely proportional to the highest processing frequency,the estimated mean square error is about 0.1 degree,and also is robust to array elements distortion.Meanwhile,it achieves lower time complexity.The proposed algorithm,whose performance is superior to CBF and MVDR in the aspects of resolution,weak target detection,non-target direction noise suppression and robustness etc.,can achieve ultra-low sidelobe high-resolution DOA in real marine environment.
A broad-band high-resolution Direction-of-Arrival(DOA)estimation algorithm named the Conditional Wavenumber Spectral Density based(CWSD-based)is proposed in this paper aimed at the problem of large error and high complexity of wide-band.The super-resolution DOA estimation,minimal main-lobe width,ultra-low side-lobe and high precision target resolution performance can be obtained after transforming the array signals to f-k space and using the characteristic of wideband signal power distributed as a line on the Conditional Wavenumber Spectral Density.Moreover,there are no DOA pre-estimates of the incident signals and the number of sources to be required in this algorithm.Computer simulations indicate that the theoretical resolution of the algorithm is inversely proportional to the highest processing frequency,the estimated mean square error is about 0.1 degree,and also is robust to array elements distortion.Meanwhile,it achieves lower time complexity.The proposed algorithm,whose performance is superior to CBF and MVDR in the aspects of resolution,weak target detection,non-target direction noise suppression and robustness etc.,can achieve ultra-low sidelobe high-resolution DOA in real marine environment.
引文
1李启虎.数字式声呐设计原理.合肥:安徽教育出版社,2003
2 Chen X,Lin H Q.Research on simulation algorithm of vessels noise signal based on time-varying modulating.J.Syst.Simul.,2012;24(2):297-300
3陶笃纯.舰船噪声节奏的研究(I)——数学模型及功率谱密度.声学学报,1983; 8(2):65-76
4 Takashi S.Focus position and depth of two-dimensional patterning by Talbot effect lithography.Microelectron.Eng.,2014; 123:80-83
5 Somasundaram S D.Wideband robust capon beamforming for passive sonar.IEEE J.Oceanic Eng.,2013; 38(2):308-322
6 Zhou H,Hu G,Shi J,et al.Orthogonal projection method for DOA estimation in low-altitude environment based on signal subspace.AEU-Int.J.Electron.Commun.,2018;83:317-321
7鄢社锋,马远良,侯朝焕.宽带波束域相干信号子空间高分辨方位估计.声学学报,2006; 31(5):418-424
8 Lin J C,Ma X C,Yan S F et al. Time-frequency multiinvariance ESPRIT for DOA estimation.IEEE Antennas Wirel. Propag.Lett.,2016; 15(1):770—773
9 Capon J.High-resolution frequency-wavenumber spectrum analysis.Proc.IEEE,1969; 57(8):1408—1418
10 Cheng F,Xia J H,Xu Z B et al.Frequency—wavenumber(FK)-based data selection in high-frequency passive surface wave survey.Surv.Geophys.,2018; 39(4):661—682
11王芳,王宝善.频率-波数域方法的发展及其在台阵数据分析中的应用·中国地震,2017; 33(2):191—200
12 Tian Z,Yu L.Lamb wave frequency—wavenumber analysis and decomposition.J.Intell.Mater.Syst. Struct., 2014;25(9):1—17
13 Poggi V.Time-frequency—wavenumber analysis of surface waves using the continuous wavelet transform.Pure Appl.Geophys.,2013; 170(3):319—335
14 Beerens S P,van Ijsselmuide S P,Volwerk C et al.Flow noise analysis of towed sonar arrays.UDT Europe,1999
15吴国清.舰船噪声识别(I)——总体框架、线谱分析和提取.声学学报,1998; 23(5):394—400
16蒋国健,林建恒,孙军平,高鑫,衣雪娟,江鹏飞,蒋东阁.海洋信道随机性引起的船舶噪声线谱衰减.声学学报,2015; 40(2):170—177
17 Wales S C,Heitmeyer R M.An ensemble source spectra model for merchant ship-radiated noise.J.Acoust.Soc.Am.,2002; 111(3):1211-1231
18 Beall J M,Kim Y C,Powers E J.Estimation of wavenumber and frequency spectra using fixed probe pairs.J.Appl.Phys.,1982; 53(6):3933-3940
19张振杰,郝向阳,刘松等.基于Hough一维变换的直线检测算法.光学学报,2016; 36(4):0412005
20鄢社锋,马远良.传感器阵列波束优化设计及应用.北京:科学出版社,2009
21 Simanapalli S,Kaveh M.Broadband focusing for partially adaptive beamforming.IEEE Trans.Aerosp.Electron.Syst.,1994; 30(1):68-80
2 Chen X,Lin H Q.Research on simulation algorithm of vessels noise signal based on time-varying modulating.J.Syst.Simul.,2012;24(2):297-300
3陶笃纯.舰船噪声节奏的研究(I)——数学模型及功率谱密度.声学学报,1983; 8(2):65-76
4 Takashi S.Focus position and depth of two-dimensional patterning by Talbot effect lithography.Microelectron.Eng.,2014; 123:80-83
5 Somasundaram S D.Wideband robust capon beamforming for passive sonar.IEEE J.Oceanic Eng.,2013; 38(2):308-322
6 Zhou H,Hu G,Shi J,et al.Orthogonal projection method for DOA estimation in low-altitude environment based on signal subspace.AEU-Int.J.Electron.Commun.,2018;83:317-321
7鄢社锋,马远良,侯朝焕.宽带波束域相干信号子空间高分辨方位估计.声学学报,2006; 31(5):418-424
8 Lin J C,Ma X C,Yan S F et al. Time-frequency multiinvariance ESPRIT for DOA estimation.IEEE Antennas Wirel. Propag.Lett.,2016; 15(1):770—773
9 Capon J.High-resolution frequency-wavenumber spectrum analysis.Proc.IEEE,1969; 57(8):1408—1418
10 Cheng F,Xia J H,Xu Z B et al.Frequency—wavenumber(FK)-based data selection in high-frequency passive surface wave survey.Surv.Geophys.,2018; 39(4):661—682
11王芳,王宝善.频率-波数域方法的发展及其在台阵数据分析中的应用·中国地震,2017; 33(2):191—200
12 Tian Z,Yu L.Lamb wave frequency—wavenumber analysis and decomposition.J.Intell.Mater.Syst. Struct., 2014;25(9):1—17
13 Poggi V.Time-frequency—wavenumber analysis of surface waves using the continuous wavelet transform.Pure Appl.Geophys.,2013; 170(3):319—335
14 Beerens S P,van Ijsselmuide S P,Volwerk C et al.Flow noise analysis of towed sonar arrays.UDT Europe,1999
15吴国清.舰船噪声识别(I)——总体框架、线谱分析和提取.声学学报,1998; 23(5):394—400
16蒋国健,林建恒,孙军平,高鑫,衣雪娟,江鹏飞,蒋东阁.海洋信道随机性引起的船舶噪声线谱衰减.声学学报,2015; 40(2):170—177
17 Wales S C,Heitmeyer R M.An ensemble source spectra model for merchant ship-radiated noise.J.Acoust.Soc.Am.,2002; 111(3):1211-1231
18 Beall J M,Kim Y C,Powers E J.Estimation of wavenumber and frequency spectra using fixed probe pairs.J.Appl.Phys.,1982; 53(6):3933-3940
19张振杰,郝向阳,刘松等.基于Hough一维变换的直线检测算法.光学学报,2016; 36(4):0412005
20鄢社锋,马远良.传感器阵列波束优化设计及应用.北京:科学出版社,2009
21 Simanapalli S,Kaveh M.Broadband focusing for partially adaptive beamforming.IEEE Trans.Aerosp.Electron.Syst.,1994; 30(1):68-80