正交匹配追踪反卷积声源识别方法
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摘要
OMP-DAMAS波束形成声源识别方法能够显著缩减主瓣宽度,降低旁瓣水平,获得极高的分辨率和定位精度。基于数值仿真多个声源激励下的识别成像图和偏差值探究其结果随声源频率、迭代次数和信噪比等参数的变化规律。结果表明:OMP-DAMAS能够有效提高分辨率和定位精度,适用于中高频声源的识别并且对噪声具有较好的适应性。当声源频率大于2 300Hz,信噪比高于0dB时,OMP-DAMAS均能准确识别声源,获得清晰的成像结果。其重构的声源个数取决于迭代次数,在信噪比较高时可以通过设置合适的动态范围以避免旁瓣污染。上述结论对反卷积OMP-DAMAS波束形成技术的运用具有指导意义。进一步,基于多个扬声器的声源识别试验验证了该方法的有效性。
Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem(OMP-DAMAS)is characterized with high resolution and high location accuracy by reducing mainlobe and eliminating sidelobes.The change rules of the acoustic source identification results with signal frequency,iteration number and signal to noiseratio(SNR)were explored based on numerical simulated incoherent sources identification imaging maps and deviation value.The results are listed as following.OMP-DAMAS can improve resolution and location accuracy effectively.Excellent performance and robustness are guaranteed for medium and high frequency source.Acoustic sources can be identified with clear map when the algorithm is applied to sources with frequency higher than 2 300 Hz and SNR higher than 0 dB.The number of reconstructed sources depends on iteration number,while sidelobes can be eliminated by setting dynamic range properly.These conclusions have guiding significance for the application of the OMP-DAMAS.Furthermore,the feasibility of the method has been validated through a physical experiment on acoustic source identification of several loudspeakers.
Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem(OMP-DAMAS)is characterized with high resolution and high location accuracy by reducing mainlobe and eliminating sidelobes.The change rules of the acoustic source identification results with signal frequency,iteration number and signal to noiseratio(SNR)were explored based on numerical simulated incoherent sources identification imaging maps and deviation value.The results are listed as following.OMP-DAMAS can improve resolution and location accuracy effectively.Excellent performance and robustness are guaranteed for medium and high frequency source.Acoustic sources can be identified with clear map when the algorithm is applied to sources with frequency higher than 2 300 Hz and SNR higher than 0 dB.The number of reconstructed sources depends on iteration number,while sidelobes can be eliminated by setting dynamic range properly.These conclusions have guiding significance for the application of the OMP-DAMAS.Furthermore,the feasibility of the method has been validated through a physical experiment on acoustic source identification of several loudspeakers.
引文
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[15]Pati Y C,Rezaifa R,Krishnaprasad P S.Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C].Proceeding of 27th Annual Asilomar Conference on Signals,Systems and Computers.Pacific Grove,CA,USA,1-3November 1993:40-44.
[2]褚志刚,杨洋,倪计民,等.波束形成声源识别技术研究进展[J].声学技术,2013,32(5):430-435.CHU Zhigang,YANG Yang,NI Jimin,et al.Review of beamforming based sound source identification techniques[J].Technical Acoustics,2013,32(5):430-435.(in Chinese)
[3]Brooks T F,Humphreys W M.A deconvolution approach for the mapping of acoustic sources(DAMAS)determined from phased microphone arrays[J].Journal of Sound and Vibration,2006,294(4/5):856-879.
[4]杨洋,褚志刚,江洪,等.反卷积DAMAS2波束形成声源识别研究[J].仪器仪表学报,2013,34(8):1779-1786.YANG Yang,CHU Zhigang,JIANG Hong,et al.Research on DAMAS2 beamforming sound source identification[J].Chinese Journal of Scientific Instrument,2013,34(8):1779-1786.(in Chinese)
[5]William M H.Extension of DAMAS phased array processing for spatial coherence determination(DAMAS-C)[C].12th AIAA/CEAS Aeroacoustics Conference(27th AIAA Aeroacoustics Conference),Cambridge,MA,8-10May 2006.
[6]杨洋,褚志刚.四种典型波束形成声源识别清晰化方法[J].数据采集与处理,2014,29(2):316-326.YANG Yang,CHU Zhigang.Four typical clearness methods for beamforming acoustic source identification[J].Journal of Data Acquisition&Processing,2014,29(2):316-326.(in Chinese)
[7]Yardibi T,Li J,Stoica P,et al.Sparsity constrained deconvolution approaches for acoustic source mapping[J].Journal of the Acoustical Society of America,2008,123(5):2631-2642.
[8]Padois T,Berry A.Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem[J].Journal of the Acoustical Society of America,2015,138(6):3678-3685.
[9]Tropp J A.Greed is good:Algorithmic results for sparse approximation[J].IEEE Transactions on Information Theory,2004,50(10):2231-2242.
[10]杨真真,杨震,孙林慧,等.信号压缩重构的正交匹配追踪类算法综述[J].信号处理,2013,29(4):486-496.YANG Zhenzhen,YANG Zhen,SUN Linhui,et al.A survey on orthogonal matching pursuit type algorithms for signal compression and reconstruction[J].Signal Processing,2013,29(4):486-496.(in Chinese)
[11]Peillot A,Ollivier F,Chardon G,et al.Localization and identification of sound sources using“compressive sampling”techniques[C].18th International Congress on Sound and Vibration,Rio de Janeiro,Brazil,10-14July 2011,1-8.
[12]宁方立,卫金刚,刘勇,等.压缩感知声源定位方法研究[J].机械工程学报,2016,52(19):42-52.NING Fangli,WEI Jingang,LIU Yong,et al.Study on sound sources localization using compressive sensing[J].Journal of Mechanical Engineering,2016,52(19):42-52.(in Chinese)
[13]Ning F L,Wei J G,Qiu L F,et al.Three-dimensional acoustic imaging with planar microphone arrays and compressive sensing[J].Journal of Sound and Vibration,2016,380:112-128.
[14]Davis G M,Mallat S,Zhang Z.Adaptive time-frequency decompositions with matching pursuits[J],Optical Engineering,1994,33(7):2183-2191.
[15]Pati Y C,Rezaifa R,Krishnaprasad P S.Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C].Proceeding of 27th Annual Asilomar Conference on Signals,Systems and Computers.Pacific Grove,CA,USA,1-3November 1993:40-44.