By C. T. Leonides
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Extra resources for Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 3 of 3
A a l g r e n u m b e r e c e s s a y r , n s i c e h t e c e n z a r e i t l d e m s i t a o t r s i p o r c e s n s i g d r e i c y l t . A a l g r e n u m b e r o f o b s e v o r a i n t s a e r e r q u e r e ipo m s trTh a e t s a n d e r o r c o v a i n r c e s . e f l o w a d g i a r m o f r h t e s m i u o a i l n t o f h t e c e n z a r e i t l p a g o a i n t s i s h o w n n i F g i . 8 . T h e f l o w a d g i a r m d u n i r g e x e c n u i g t o f h t e s m i u o a i l t n . n I t a i y l , a l m c e a i r h ts e p o r p e r n i t a i l c o n o d i t n i s .
0 i e (mn i) 21 Fig. 15. Reconsrtucetd tilt eror ( ψ ; φ ) in the DU filter. χ ν Similarly, Fig. 16 shows the reconstructed heading error in the D U filter. The heading error is more sensitive to changes in flight parameters, as shown by the large increase in covariance during the initiation of the full " S " turn. After about half a minute, however, the heading error covariance approximates that of the centralized filter. The application of the gain transfer algorithm is thus shown to successfully reconstruct filter state covariances in one filter without direct processing of observations for those filter states.
This structures structures and must s t a b i l i z e d b y a series o f sensors w h i c h m u s t b e i n i t i a l l y a l i g n e d p r o p e r l y Without proper sensor alignment the ability to achieve proper be . track a s s o c i a t i o n i n a m u l t i t a r g e t t r a c k i n g e n v i r o n m e n t is c o m p l i c a t e d a n d , i n f a c t , p r e v e n t e d if the sensor a l i g n m e n t errors are t o o large [ 2 0 ] . Also, direct application of the gain transfer algorithm may simplify the multitarget/multisensor tracking problem.
Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 3 of 3 by C. T. Leonides