Maximum likelihood methods for direction-of-arrival estimation

Five methods of direction-of-arrival (DOA) estimation which can be derived from the maximum-likelihood (ML) principle are considered. The ML method (MLM) results from the application of the ML principle to the statistics of the observed raw data. The standard multiple signal classification (MUSIC) procedure, called MUSIC-1, is obtained as a brute-force approximation of the MLM. An improved MUSIC procedure, named MUSIC-2, is obtained by applying the ML principle to the statistics of certain linear combinations of the sample noise space eigenvectors. A procedure which compromises between the good performance of the MLM and the computational simplicity of MUSIC is a method of direction estimation (MODE-1) which is derived as a large sample realization of the MLM. A fifth method, called MODE-2, is obtained by using the ML principle on the statistics of certain linear combinations of the sample eigenvectors. MODE-2 is computationally less demanding than the MLM (it is of the same complexity as MODE-1) and statistically more efficient. A numerical comparison of these five DOA estimation methods is presented. It confirms the analytic results on their theoretical performance levels. >

[1]  P. Stoica,et al.  Novel eigenanalysis method for direction estimation , 1990 .

[2]  Petre Stoica,et al.  MUSIC, maximum likelihood and Cramer-Rao bound: further results and comparisons , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[3]  Thomas Kailath,et al.  Asymptotic performance of eigenstructure spectral analysis methods , 1984, ICASSP.

[4]  Ken Sharman,et al.  Adaptive algorithms for estimating the complete covariance eigenstructure , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Mostafa Kaveh,et al.  The statistical performance of the MUSIC and the minimum-norm algorithms in resolving plane waves in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[6]  Donald W. Tufts,et al.  Simple, effective computation of principal eigenvectors and their eigenvalues and application to high-resolution estimation of frequencies , 1986, IEEE Trans. Acoust. Speech Signal Process..

[7]  Petre Stoica,et al.  Performance study of conditional and unconditional direction-of-arrival estimation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[8]  D. R. Farrier,et al.  Asymptotic results for eigenvector methods , 1985 .

[9]  Georges Bienvenu,et al.  Adaptivity to background noise spatial coherence for high resolution passive methods , 1980, ICASSP.

[10]  Petre Stoica,et al.  MUSIC, maximum likelihood, and Cramer-Rao bound , 1989, IEEE Transactions on Acoustics, Speech, and Signal Processing.

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

[12]  Paruchuri R. Krishnaiah,et al.  On some nonparametric methods for detection of the number of signals , 1987, IEEE Trans. Acoust. Speech Signal Process..

[13]  Arye Nehorai,et al.  Maximum likelihood estimation of exponential signals in noise using a Newton algorithm , 1988, Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling.

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

[15]  Ken Sharman,et al.  Maximum likelihood parameter estimation by simulated annealing , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[16]  Tariq S. Durrani,et al.  Genetic algorithms for spatial spectral estimation , 1988, Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling.

[17]  D. Goodman,et al.  A note on minimizing the prediction error when the zeros are restricted to the unit circle , 1982 .

[18]  Yoram Bresler,et al.  Exact maximum likelihood parameter estimation of superimposed exponential signals in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[19]  Ramdas Kumaresan,et al.  An algorithm for pole-zero modeling and spectral analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..

[20]  Hong Wang,et al.  On the performance of signal-subspace processing- Part I: Narrow-band systems , 1986, IEEE Trans. Acoust. Speech Signal Process..

[21]  T. Durrani,et al.  A comparative study of modern eigenstructure methods for bearing estimation-A new high performance approach , 1986, 1986 25th IEEE Conference on Decision and Control.

[22]  Thomas Kailath,et al.  Determining the number of signals by information theoretic criteria , 1983, ICASSP.

[23]  Ilan Ziskind,et al.  Maximum likelihood localization of multiple sources by alternating projection , 1988, IEEE Trans. Acoust. Speech Signal Process..

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