Filtering for Stochastic Processes with Applications to Guidance

Part I. Theory: Ordinary differential equations and stability Random processes and stochastic models Observability and controllability Filtering theory Global theory of filtering Stochastic stability Optimal filtering for correlated noise processes Approximate optimal non-linear filtering Optimum filtering for discrete time random processes Stochastic control Open questions and historical comments Part II. Applications: Application to navigation Applications of filter theory and modeling techniques Free flight and powered flight navigation Error analyses and sub-optimal modeling Errors in the filtering process Appendix A. Least squares curve fitting Appendix B. Probability review References Appendix C. The Riccati equation and its bounds Appendix D. Further references Index.