By C. T. Leonides
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Additional resources for Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 3 of 3
G A R D N E R 46 ο Ε <° I ι h s β • — h-—ηι Τ 1 m Tie m (ni) Fig. 16. 00 Ι _ — , 54-state and hierarchical model. T h e local attitude preselected observations intervals the and gain filters propagate transfer independently local filter technique is process velocity state covariances. applied and At the local estimators sequentially pass their gain, m e a s u r e m e n t , a n d m e a s u r e m e n t noise ζ m a t r i c e s to the centralized e s t i m a t o r in the process described earlier. I n manner the updated estimator, thereby objectives of of a l o c a l i n f o r m a t i o n is p a s s e d reconstructing decentralized observations, yet the the effects o f the l o c a l system global to the central are met, estimator is that observations.
T h e c o m m u n i c a t i o n b a n d w i d t h m a y be r e d u c e d if the transfer o f l o c a l g a i n a n d o b s e r v a t i o n m a t r i c e s is n o t r e q u i r e d e a c h u p d a t e . Issues o f stability a n d robustness h a v e also n o t b e e n addressed w i t h i n . each of the simulations that were performed, the gain transfer In algorithm provided g o o d stability characteristics, but a wider study of general stability a n d robustness characteristics s h o u l d be p e r f o r m e d .
R d n i a o t r s i u p d e a d t b y n i o f m r o a i t n o r f m e a c h o f a re tD n s e f r a g l h o t m i r u s e s h t e s e q u e n a i l t p a s a g e o f h t o p e p r l u p d e a t K H a n d R m c e a i r s t o t e r c o n s r o r v a i r n c e s . L o c a l o b s e v o r a i n t s a e r n o t p o r c e s e d h tn u s h t e d a t c o m m o u c n i a t i b a n d w d h t i s i e r d u c e d . Ism n e a c h c a s e a c o v a n i r c e s m i u o a i l n t s i p o e m f r a v g i o a i t n .