A study on the accurate estimation of the number of weak coherent signals

Estimation of the number of incident signals (NIS) is an important problem for array signal processing, such as direction-of-arrival (DOA) estimation. A method for estimating the number of signals without eigendecomposition (MENSE) is superior to algorithms based on the spatial smoothing preprocessing (SSP) and the Akaike information criterion (AIC). The advantages of the MENSE can be achieved by employing the Hankel correlation matrices which can suppress the correlation between the incident coherent signals and the influence of noise. In this paper, we propose a new metric based on the multiplicity criterion to improve the accuracy of the MENSE. Computer simulation results show that the proposed method is superior to the MENSE and the SSP-AIC methods in an environment arriving closely spaced coherent signals.

[1]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[2]  Surendra Prasad,et al.  Direction-of-arrival estimation using rank revealing QR factorization , 1991, IEEE Trans. Signal Process..

[3]  Thomas Kailath,et al.  Detection of number of sources via exploitation of centro-symmetry property , 1994, IEEE Trans. Signal Process..

[4]  Hsien-Tsai Wu,et al.  Source number estimators using transformed Gerschgorin radii , 1995, IEEE Trans. Signal Process..

[5]  J.P. Reilly,et al.  A real-time high-resolution technique for angle-of-arrival estimation , 1987, Proceedings of the IEEE.

[6]  Lei Huang,et al.  Source Enumeration for High-Resolution Array Processing Using Improved Gerschgorin Radii Without Eigendecomposition , 2008, IEEE Transactions on Signal Processing.

[7]  Nanning Zheng,et al.  Simple and Efficient Nonparametric Method for Estimating the Number of Signals Without Eigendecomposition , 2007, IEEE Transactions on Signal Processing.

[8]  Alfred O. Hero,et al.  Detection of the Number of Signals Using the Benjamini-Hochberg Procedure , 2007, IEEE Transactions on Signal Processing.

[9]  M. Viberg,et al.  Two decades of array signal processing research: the parametric approach , 1996, IEEE Signal Process. Mag..

[10]  S. Unnikrishna Pillai,et al.  Forward/backward spatial smoothing techniques for coherent signal identification , 1989, IEEE Trans. Acoust. Speech Signal Process..

[11]  Heinz Teutsch,et al.  Modal Array Signal Processing: Principles and Applications of Acoustic Wavefield Decomposition , 2007 .

[12]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[13]  Steven Kay,et al.  Source Enumeration via the EEF Criterion , 2008, IEEE Signal Processing Letters.

[14]  Surendra Prasad,et al.  An improved spatial smoothing technique for bearing estimation in a multipath environment , 1988, IEEE Trans. Acoust. Speech Signal Process..

[15]  Thomas Kailath,et al.  ESPRIT-estimation of signal parameters via rotational invariance techniques , 1989, IEEE Trans. Acoust. Speech Signal Process..

[16]  T. Chan.  Rank revealing QR factorizations , 1987 .

[17]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[18]  Naoyuki Ichimura.  A robust and efficient motion segmentation based on orthogonal projection matrix of shape space , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[19]  Alan Edelman,et al.  Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples , 2007, IEEE Transactions on Signal Processing.

[20]  Koichi Ichige,et al.  Accurate Source Number Detection Using Pre-Estimated Signal Subspace , 2006, IEICE Trans. Commun..

[21]  H. Akaike.  A new look at the statistical model identification , 1974 .

[22]  Kenichi Kanatani,et al.  Motion Segmentation by Subspace Separation: Model Selection and Reliability Evaluation , 2002, Int. J. Image Graph..

[23]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[24]  Pascal Larzabal,et al.  Estimation of the number of signals from features of the covariance matrix: a supervised approach , 1999, IEEE Trans. Signal Process..

[25]  Fu Li,et al.  An eigenvector technique for detecting the number of emitters in a cluster , 1994, IEEE Trans. Signal Process..

[26]  Thomas Kailath,et al.  On spatial smoothing for direction-of-arrival estimation of coherent signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[27]  Jingmin Xin,et al.  Computationally efficient subspace-based method for direction-of-arrival estimation without eigendecomposition , 2004, IEEE Transactions on Signal Processing.

[28]  Abdelhak M. Zoubir,et al.  Detection of sources using bootstrap techniques , 2002, IEEE Trans. Signal Process..

[29]  E. Radoi,et al.  Some radar imagery results using superresolution techniques , 2004, IEEE Transactions on Antennas and Propagation.

[30]  K. J. Ray Liu,et al.  Estimation of multiple sinusoidal frequencies using truncated least squares methods , 1993, IEEE Trans. Signal Process..