June 2019 OES Beacon

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Model-Based Processing: An Applied Subspace Identification Approach

James V. Candy
Lawrence Livermore National Laboratory
University of California Santa Barbara


James V. Candy: Model-Based Processing: An Applied Subspace Identification Approach: John Wiley, 2019

This text encompasses the basic idea of the model-based approach to signal processing by incorporating the often overlooked, but necessary, requirement of obtaining a model initially in order to perform the processing in the first place. Here we are focused on presenting the development of models for the design of model-based signal processors using subspace identification techniques to achieve a model based identification as well as incorporating validation and statistical analysis methods to evaluate their overall performance. It presents a different approach that incorporates the solution to the system identification problem as the integral part of the model-based signal processor (Kalman filter) that can be applied to a large number of applications, but with little success unless a reliable model is available or can be adapted to a changing environment. Here, using subspace approaches, it is possible to identify the model very rapidly and incorporate it into a variety of processing problems such as state estimation, tracking, detection, classification, controls and communications to mention a few. Models for the processor evolve in a variety of ways, either from first principles accompanied by estimating its inherent uncertain parameters as in parametrically adaptive schemes, or by extracting constrained model sets employing direct optimization methodologies, or by simply fitting a black box structure to noisy data. Once the model is extracted from controlled experimental data, or a vast amount of measured data, or even synthesized from a highly complex truth model, the long-term processor can be developed for direct application. Since many real-world applications seek a real-time solution, we concentrate primarily on the development of fast, reliable identification methods that enable such an implementation. Model extraction/development must be followed by validation and testing to ensure that the model reliably represents the underlying phenomenology—a bad model can only lead to failure!

Although this proposed text is designed primarily as a graduate text, it will prove useful to practicing signal processing professionals and scientists, since a wide variety of case studies are included to demonstrate the applicability of the model-based subspace identification approach to real-world problems. It is unique in the sense that many texts cover some of its topics in piecemeal fashion. The underlying model-based approach of this text is the thread that is embedded throughout in the algorithms, examples, applications, and case studies. It is the model-based theme, together with the developed hierarchy of physics-based models, that contributes to its uniqueness coupled with the now robust, subspace model identification methods that even enable potential real-time methods to become a reality.

The ROV Manual, 2nd Edition,
Reaches a New Milestone

Robert L. Wernli Sr: The ROV Manual, 2nd Edition, Reaches a New Milestone.

Robert L. Wernli Sr., First Centurion Enterprises
Robert D. Christ, SeaTrepid

The 2nd edition of The ROV Manual, co-authored by Robert Wernli, Co-EIC of the Beacon, has reached a new milestone. The popular manual, published by Elsevier in 2014, has requested that we consider a third edition because the manual has not only sold over 1200 print copies, in addition to the digital copies, but has been translated into Chinese under the guidance of the Shanghai Jiao Tong University. Needless to say, the authors are quite honored.

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