Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook

Discusses model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions.

Presents methods for deciding on the "best" model

Presents little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters

Discusses implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared.

Provides a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering