Practical aspects of Kalman filtering applications are considered in this chapter, as follows:
how performance of the Kalman filter can degrade due to computer roundoff errors and alternative implementation methods with better robustness against roundoff;
how to determine computer memory, word length, and throughput requirements for implementing Kalman filters in computers;
ways to implement real‐time monitoring and analysis of filter performance;
the Schmidt–Kalman suboptimal filter, designed for reducing computer requirements;
covariance analysis, which uses the Riccati equations for performance‐based predictive design of sensor systems; and
Kalman filter architectures for GPS/INS integration.
Kalman Filter Engineering
2000-12-15
36 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Wiley | 2000
|AIAA | 2015
|Observability and Kalman Filter
Springer Verlag | 2017
|