Model-Based Processing

A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems
An Applied Subspace Identification Approach
The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features:
Model-Based Processing: An Applied Subspace Identification Approach

Автор: James V. Candy
ISBN: 9781119457770

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