In an inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying multiple signal classification (MUSIC) to the outputs of discrete wavelet transformation with subband selection based on the entropy threshold. Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.
SchmidtR. O.Multiple emitter location and signal parameter estimation. IEEE Trans. Anten. Propag., 1986, AP-34, (3), 276–280.
2.
KrimH.VibergM.Two decades of array signal processing research. The parametric approach. IEEE Signal Proc. Mag., 1996, 13(4), 67–94.
3.
BarabellA. J.Improving the resolution performance of eigenstructure based direction-finding algorithms. Proceeding of IEEE International Conference on Acoustics, speech and signal processing, Boston, USA, 1983, vol. 1, pp. 336–339.
4.
AsanoF.AsohH.MatsuiT.Sound source localization and separation in near field. IEICE Trans. Fund., 2000, E83-A, (11), 2286–2294.
5.
AsanoF.IkedaS.OgawaM.AsohH.KitawakiN.A combined approach of array processing and independent component analysis for blind separation of acoustic signals. Proceeding of IEEE International Conference on Acoustics, speech and signal processing, Salt Lake City, USA, 2001, vol. 5, pp. 2729–2732.
6.
SekiharaK.PoeppelD.MarantzA.KoizumiH.MiyashitaY.Noise covariance incorporated MEG-MUSIC algorithm: A method for multiple-dipole estimation tolerant of the influence of background brain activity. IEEE Trans. Biomed. Engineering, 1997, 44(9), 839–847.
7.
ErmerJ. J.MosherJ. C.HuangM.LeahyR. M.Paired MEG data set source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans. Biomed. Engineering, 2000, 47(9), 1248–1260.
8.
MosherJ. C.LeahyR. M.Source localization using recursively applied and projected (RAP) MUSIC. IEEE Trans. Signal Processing., 1999, 47(2), 332–340.
9.
OweissK. G.AndersonD. J.A new approach to array denoising. Proceedings of IEEE 34th Asilomar Conference, Asilomar, USA, 2000, vol. 2, pp. 1403–1407.
10.
SaidA.PearlmanR. O.An image multiresolution representation for loseless and lossy compression. IEEE Trans. Image Process., 1996, 5, 1303–1310.
11.
FukueT.HamadaN.DOA estimation of moving target under the clutter environment by applying MUSIC to the QMF Doppler filter bank. IEICE Trans. Commun., 2005, E88-B, (5), 2142–2151.
12.
BellA.SejnowskiT.An information maximization approach to blind separation and blind deconvolution. Neural Comput., 1995, 7, 1129–1159.
13.
DaubechiesI.Ten lectures on wavelets, 1992 (SIAM, Philadelphia).
14.
ChuiC. K.An introduction to wavelets, 1992 (Academic Press, New York).
15.
CoverT. M.ThomasJ. A.Elements of information theory, 1991 (Wiley-Interscience).
