1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra. 4. Parametric Methods for Line Spectra. 5. Filter Bank Methods. 6. Spatial Methods. Appendix A: Linear Algebra and Matrix Analysis Tools. Appendix B: Cramer-Rao Bound Tools. Appendix C: Model Order Selection Tools. Appendix D: Answers to Selected Exercises. Bibliography. References Grouped by Subject. Subject Index.

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Spectral analysis of signals

N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems

Subspace identification for linear systems

Soft systems methodology (SSM) is an approach for tackling problematical, messy situations of all kinds. It is an action-oriented process of inquiry into problematic situations in which users learn their way from finding out about the situation, to taking action to improve it. The learning emerges via an organised process in which the situation is explored using a set of models of purposeful action (each built to encapsulate a single worldview) as intellectual devices, or tools, to inform and structure discussion about a situation and how it might be improved. This paper, written by the original developer Peter Checkland and practitioner John Poulter, gives a clear and concise account of the approach that covers SSM’s specific techniques, the learning cycle process of the methodology and the craft skills which practitioners develop. This concise but theoretically robust account nevertheless includes the fundamental concepts, techniques, core tenets described through a wide range of settings.

Soft Systems Methodology

Subspace-based methods for the identification of linear time-invariant systems

Publisher Summary The progress of mathematical methods for signal processing and the simultaneous progress of digital data processing hardware have generated new interest in Markovian models, which have been known for a long time. Although many papers have appeared on filtering, detection, or control using such models, very little attention has been given to (1) the study of the properties of such models, (2) the set of all models that can represent a given stochastic process, and (3) the design of efficient algorithms to compute such models. This chapter deals with the theoretical problem of studying all Markovian models corresponding to a given stochastic process. The study given in the chapter appears to have interest from a practical point of view by sorting out peculiar Markovian models, such as the statistical filter of the process. This study and the related constructing proofs also lead to the design of efficient algorithms.

Pierre L. Faurre

Papers

Stochastic Realization Algorithms

Systems Engineering Methodology