Superresolution frequency estimation by alternating notch periodogram

A novel periodogram-based maximum-likelihood algorithm is proposed for a frequency estimation problem. It is called an alternating notch-periodogram algorithm (ANPA), since the original multidimensional maximum likelihood problem is decomposed into a sequence of much simpler one-dimensional problems of finding the peaks of notch periodograms. The ANPA achieves superresolution and a very low SNR threshold and can be computed and implemented in several efficient ways. First, with FFT and a concurrent Gram-Schmidt procedure using Schur's recursions, the notch periodogram can be computed without any costly eigendecomposition and matrix inversion. This approach can further lead to a mapping of the notch periodogram onto a VLSI architecture consisting mainly of a highly pipelined notch processor and two FFT processors. Second, without degrading the excellent performance of ANPA, the notch periodogram can be simplified and approximated to provide further computational reduction and implementational simplicity. >

[1]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[2]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[3]  R. Kumaresan,et al.  Singular value decomposition and improved frequency estimation using linear prediction , 1982 .

[4]  Albert H Nuttall,et al.  Spectral Analysis of a Univariate Process with Bad Data Points, via Maximum Entropy and Linear Predictive Techniques , 1976 .

[5]  Rajendra Kumar,et al.  A fast algorithm for solving a Toeplitz system of equations , 1983, IEEE Trans. Acoust. Speech Signal Process..

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

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

[8]  David R. Brillinger,et al.  Time Series: Data Analysis and Theory. , 1982 .

[9]  R. R. Boorstyn,et al.  Multiple tone parameter estimation from discrete-time observations , 1976, The Bell System Technical Journal.

[10]  L. Scharf,et al.  A Prony method for noisy data: Choosing the signal components and selecting the order in exponential signal models , 1984, Proceedings of the IEEE.

[11]  Petre Stoica,et al.  Maximum likelihood estimation of the parameters of multiple sinusoids from noisy measurements , 1989, IEEE Trans. Acoust. Speech Signal Process..

[12]  Donald G. Childers,et al.  Modern Spectrum Analysis , 1978 .

[13]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

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

[15]  S. Kung,et al.  VLSI Array processors , 1985, IEEE ASSP Magazine.

[16]  R. Kumaresan,et al.  Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood , 1982, Proceedings of the IEEE.

[17]  Z. Bai,et al.  On detection of the number of signals when the noise covariance matrix is arbitrary , 1986 .

[18]  T. Söderström,et al.  The Steiglitz-McBride identification algorithm revisited--Convergence analysis and accuracy aspects , 1981 .

[19]  T. Ulrych,et al.  Time series modeling and maximum entropy , 1976 .

[20]  Yiu-Tong Chan,et al.  Spectral estimation via the high-order Yule-Walker equations , 1982 .

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

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

[23]  V. Pisarenko The Retrieval of Harmonics from a Covariance Function , 1973 .

[24]  T. Kailath A Theorem of I. Schur and Its Impact on Modern Signal Processing , 1986 .

[25]  Sun-Yuan Kung,et al.  A highly concurrent algorithm and pipeleined architecture for solving Toeplitz systems , 1983 .

[26]  Louis L. Scharf,et al.  Fast algorithms for computing QR and Cholesky factors of Toeplitz operators , 1988, IEEE Trans. Acoust. Speech Signal Process..

[27]  Robert Boorstyn,et al.  Single tone parameter estimation from discrete-time observations , 1974, IEEE Trans. Inf. Theory.

[28]  Jeng-Kuang Hwang,et al.  A combined detection-estimation algorithm for the harmonic-retrieval problem , 1993, Signal Process..

[29]  Ramdas Kumaresan,et al.  ESTIMATING THE PARAMETERS OF EXPONENTIALLY DAMPED OR UNDAMPED SINUSOIDAL SIGNALS IN NOISE , 1982 .

[30]  J. Cadzow,et al.  Spectral estimation: An overdetermined rational model equation approach , 1982, Proceedings of the IEEE.