- Hardcover: 530 pages
- Publisher: McGraw-Hill Education; 1 edition (April 15, 2008)
- Language: English
- ISBN-10: 0071493298
- ISBN-13: 978-0071493291
- Product Dimensions: 6.3 x 1.3 x 9.3 inches
- Shipping Weight: 1.7 pounds (View shipping rates and policies)
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #1,427,484 in Books (See Top 100 in Books)
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Aided Navigation: GPS with High Rate Sensors 1st Edition
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About the Author
About the Author Jay A. Farrell, Ph.D., is a Professor and former Chair of the Department of Electrical Engineering at the University of California, Riverside. Dr. Farrell is the author of over 150 technical publications, as well as co-author of the books, Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches and McGraw-Hill's The Global Positioning System and Inertial Navigation.
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The problem is, Groves (Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems (GNSS Technology and Applications)) does it better, more logically and, as a practitioner, more useful in implementing a GPS/INS system.
It does do some things better than Groves: it introduces Doppler radar, barometric measurements and other sensors and shows how they can be put into the framework. It also does quaternions, something barely addressed in Groves.
However, from a practitioner's point of view, the "advantage" of leaving the rotation description as neutral makes the presentation more difficult to follow. Groves sticks to DCM for the sake of clarity and it pulls off.
Aided Navigation lays out Farrell's way of designing positioning systems. His designs take a subsystem formed through integration of several sensors that give good short term changes in state (e.g. position changes from inertial sensors) and aid it with one or more complimentary subsystems that do better at following the state over a longer time period (e.g. GPS receiver output.) This is a fairly classical approach, but his survey of the prerequisite theory and explanation of the finer implementation details is refreshingly well-organized.
In Part I, he builds up the analytical tools needed to rigorously execute his design methodology. It includes lots of simple examples along the way. Part II is a catalog of detailed real-world designs.
I like how the whole text coheres around derivation and demonstration of a particular design methodology. You get the feeling that he's built systems that had to work in the real world and not just on paper. Although he does outline a recipe for analyzing these sorts of systems, his theoretical depth and copious reference list saves the book from being an unthinking cookbook. It has helped me decode a lot of positioning articles I've read and given me a good 'reference trajectory' for my own design journey.
If I had to keep just two positioning references on my bookshelf, this would be one and Dan Simon's _Optimal State Estimation_ would be the other (not counting my linear algebra, physics, and calculus textbooks.)
I have used this text (and its previous version) both in teaching coursework and in professional application. This text provides a modern approach to combining GPS with a variety of sensors, and does so using the language of applied linear algebra and its associated popular tools (typically Matlab and Simulink, examples of which are available from a dedicated site).
This text also has strong tutorial value in the application of Kalman Filters and its modern derivatives with substantial real world applications. Perhaps of most value to students is the author's use of progressive examples of the internal model principles of the KF applied to common sensor errors.
The appendices include an excellent discussion on the Quaternion parameterization of frame rotations, which is still a rare find in text book form.
The appendix on Euler and quaternion kinematic rate equations is quite exceptional. This book is first-rate.
In practice though I find myself looking in this book only for coordinate and quaternion information, while Groves is the superior text for building the GNSS+INS kalman filter that works.
This book has a prominent place on the bookshelf, but it doesn't quite stand alone.
It leads the reader from deterministic and stochastic systems to optimal state estimation and navigation system design. The strong focus on practical implementation and the clear structure makes it a perfect guide for self study in this interesting topic.