This book offers an advanced introduction to inertial data processing—specifically, determining attitude, velocity, and position—and explores the design architectures and algorithms that support inertial navigation systems (INS), with a focus on high-end, navigation-grade sensors and systems used in aerospace.
Inertial navigation and its supporting techniques (which help reduce error drift) are complex and multidisciplinary, involving mathematics, physics, and various engineering fields. The book is intended as an introduction for students and newcomers, covering specialized topics like rotation matrices, quaternions, and relevant stochastic processes. Readers should have a basic understanding of vectors, matrices, and introductory calculus.
The book begins by explaining the fundamentals of updating inertial position, velocity, and attitude in a simple inertially-fixed reference frame, before addressing the complexities introduced by the rotating, spheroidal Earth. The Kalman filter is introduced in a scalar context to build intuition about prediction error covariance and Kalman gain, before moving on to the full matrix formulations. By the end, readers will have a comprehensive understanding of modern aided-INS architectures.
