Filtering and system identification are powerful techniques for building models of complex systems in communications, signal processing, control, and other engineering disciplines. This book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Particular focus is placed on the least squares approach as applied to estimation problems of increasing complexity to retrieve missing information about a linear state-space model. The authors start with key background topics including linear matrix algebra, signal transforms, linear system theory, and random variables. They then cover different estimation and identification methods in the state-space model, a broad range of filtering and system identification problems are analyzed, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics, and state estimator directly from the data. The final chapter on the system-identification cycle prepares the reader for tackling real-world problems. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical, and aerospace engineering. It is also a useful reference for practitioners.

Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127. Book jacket.