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The Pisarenko spectral estimation method: Extension to AR vector processes
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The present monograph studies the asymptotic behaviour of
eigenvalues, products and functions of block Toeplitz matrices
generated by the Fourier coefficients of a continuous matrix-valued
function. This study is based on the concept of asymptotically equivalent
sequences of non-square matrices. The asymptotic results on
block Toeplitz matrices obtained are applied to vector asymptotically
wide sense stationary processes. Therefore, this monograph is a generalization
to block Toeplitz matrices of the Gray monograph entitled
“Toeplitz and circulant matrices: A review”, which was published in
the second volume of Foundations and Trends in Communications
and Information Theory, and which is the simplest and most famous
introduction to the asymptotic theory on Toeplitz matrices.
For the engineering community, Gray's tutorial monograph on Toeplitz and circulant matrices has been, and remains, the best elementary introduction to the Szego theory on large Toeplitz matrices. In this paper, the most important results of the cited monograph are generalized to block Toeplitz (BT) matrices by maintaining the same mathematical tools used by Gray, that is, by using asymptotically equivalent sequences of matrices. As applications of these results, the geometric minimum mean square error (MMSE) for both an infinite-length multivariate linear predictor and an infinite-length decision feedback equalizer (DFE) for multiple-input-multiple-output (MIMO) channels, are obtained as a limit of the corresponding finite-length cases. Similarly, a short derivation of the well-known capacity of a time-invariant MIMO Gaussian channel with intersymbol interference (ISI) and fixed input covariance matrix is also presented.
The fundamental theorems on the asymptotic behavior of eigenvalues, inverses, and products of "finite section" Toeplitz matrices and Toeplitz matrices with absolutely summable elements are derived in a tutorial manner. Mathematical elegance and generality are sacrificed for conceptual simplicity and insight in the hopes of making these results available to engineers lacking either the background or endurance to attack the mathematical literature on the subject. By limiting the generality of the matrices considered the essential ideas and results can be conveyed in a more intuitive manner without the mathematical machinery required for the most general cases. As an application the results are applied to the study of the covariance matrices and their factors of linear models of discrete time random processes. Acknowledgements The author gratefully acknowledges the assistance of Ronald M. Aarts of the Philips Research Labs in correcting many typos and errors in the 1993 revision, Liu Mingyu in pointing out errors corrected in the 1998 revision, Paolo Tilli of the Scuola Normale Superiore of Pisa for pointing out an incorrect corollary and providing the correction, and to David Neuho# of the University of Michigan for pointing out several typographical errors and some confusing notation. For corrections, comments, and improvements to the 2001 revision thanks are due to William Trench, John Dattorro, and Young Han-Kim. In particular, Trench brought the Wielandt-Ho#man theorem and its use to prove strengthened results to my attention. Section 2.4 largely follows his suggestions, although I take the blame for any introduced errors. Contents 1
Block Toeplitz Matrices: Asymptotic Results and Applications provides a tutorial introduction and in-depth exposé of this important mathematical technique used in Communications, Information Theory, and Signal Processing. The matrix representations of discrete-time causal finite impulse response (FIR), multiple-input multiple-output (MIMO) filters, and correlation matrices of vector wide sense stationary (WSS) processes are all block Toeplitz. The monograph deals with the asymptotic behaviour of eigenvalues, products and inverses of block Toeplitz matrices, with the concept of asymptotically equivalent sequences of matrices being the key concept. However, since the blocks of block Toeplitz matrices are, in general, not square, the definition of asymptotically equivalent sequences of matrices are extended to sequences of non-square matrices. Furthermore, the monograph covers any function of block Toeplitz matrices and considers block Toeplitz matrices generated by the Fourier coefficients of any continuous matrix-valued function. Block Toeplitz Matrices is an advanced level tutorial on a mathematical technique with applications in many engineering disciplines.
The Pisarenko method estimates the power spectral density (PSD) of Gaussian wide sense stationary (WSS) 1-dimensional (scalar) processes when the PSD is Lipschitz. In this paper we modify the Pisarenko method to estimate the generating function of a sequence of block Toeplitz matrices from another sequence of matrices when both sequences are asymptotically equivalent. This modified version of the Pisarenko method allows us to estimate the PSD of any asymptotically WSS (AWSS) multidimensional (vector) process even if the process is not Gaussian and even if the PSD is continuous, but not Lipschitz.
The present paper considers a special class of vector random processes that we call multivariate asymptotically wide sense stationary (WSS) processes. A multivariate random process is said to be asymptotically WSS if it has constant mean and the sequence of its autocorrelation matrices is asymptotically equivalent (a.e.) to the sequence of autocorrelation matrices of some multivariate WSS process. It is shown that this class of processes contains meaningful processes other than multivariate WSS processes. In particular, we give sufficient conditions for multivariate moving average (MA) processes, multivariate autoregressive (AR) processes and multivariate autoregressive moving average (ARMA) processes to be asymptotically WSS. Furthermore, in order to solve multiple-input-multiple-output (MIMO) problems in communications and signal processing involving this kind of processes, we extend the Gray definition of a.e. sequences of matrices and his main results on these sequences to non-square matrices. As an example, the derived results on a.e. sequences of non-square matrices are applied to compute the differential entropy rate and the minimum mean square error (MMSE) for a linear predictor of a multivariate asymptotically WSS process.
The estimation of spectra of random stationary processes is an important part of the statistics of random processes. There are several books on spectral analysis, e.g. Blackman & Tukey, Hannan, and Jenkins & Watts. As a rule, spectral estimators are quadratic functions of the realizations. Recently Capon suggested a new method for estimation of spectra of random fields, in which a non-quadratic function of the realization is used: he considered a homogeneous random field ξ(t, x1, x2), i.e. one which is stationary in time and space and a random function of the time and space co-ordinates t, x1, x2. For the sake of expository convenience we shall consider ordinary stationary processes of time only, ξ(t); the generalization of our results to the case of random fields is easy.
Comparison of the conventional spectral estimator and the ‘high-resolution’ estimator for an artificial example showed that the latter has less smoothing effect on the true spectrum (Capon). This was later confirmed by examples using real data (Capon). However, it was not clear whether for a finite realization the high-resolution estimator distorted the true spectrum, i.e. whether it behaved for example like a conventional estimator raised to some power.
In the present paper we introduce and study a new class of spectral estimators which are generally non-linear and non-quadratic functionals of the realizations. These estimators include the conventional and high-resolution ones, for which we shall give the approximate distributions. We derive under rather general conditions the limiting distribution of the new class of estimators, and illustrate them with several examples. As a matter of fact, these new estimators are weighted means of the eigenvalues of the covariance matrix, e.g. the arithmetic mean, geometric mean, and so on.
Since covariance matrices of weakly stationary random processes are Toeplitz, much of the theory involving asymptotic results for such processes is simply the theory of the asymptotic behavior of Toeplitz forms. The fundamental theorem of this type is the Szegö theorem on the asymptotic eigenvalue distribution of Toeplitz matrices. This theorem is often quoted but relatively little understood in the engineering literature. In this tutorial paper we prove the Szegiö theorem for the special case of finite-order Toeplitz matrices. In this setting the mathematical sophistication of the classical proofs is not required and the proof is both simple and intuitive--yet it contains the important concepts involved in the most general case.