ABSTRACT
THE previous chapters provided the basic concepts for state estimation of dy-namic systems. The foundations of these chapters still rely on the estimation results of Chapter 1 and the probability concepts introduced in Chapter 2. Applications of the fundamental concepts have been shown for various systems in Chapter 4. In this chapter these applications are extended to demonstrate the power of the sequential Kalman filter and batch estimation algorithms. As with Chapter 4, this chapter shows only the fundamental concepts of these applications, where the emphasis is upon the utility of the estimation methodologies. The interested reader is encouraged to pursue these applications in more depth by studying the references cited in this chapter.
