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Extra info for Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 3 of 3

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57 (min) Fig. 22. L o c a l D U filter harmonization error performance {Αηχ; Αην; Δηζ). 00 GAIN TRANSFER A N DHIERARCHICAL 51 ESTIMATION ο ο • - φχ ο ! 00 Time (min) Fig. 23. G l o b a l reconstructed tilt error (φχ; φ). 14 Time (min) Fig. 24. G l o b a l reconstructed heading error {φζ). 52 W I L L I A M T. G A R D N E R local estimator. The global estimator i n f o r m a t i o n a n d not directly process need only, therefore, integrate the observations. O f i n t e r e s t , g l o b a l l y , t o t h i s p r o b l e m is t h e t i l t a n d h e a d i n g e r r o r v a r i a n c e s .

E v r i a o i t n o f h t e g a n i a r t n s e f r a g l h o t m i r o f o l b a l s a t e t e r o r c o v a n i r c e b e n g s i w h t i h t e p o r p a o ffilter rF a s e q u e n a i l t c e n z a r e i t l d filter. ,c f(! 1 h t1e+2 (fcs a 6+ tg 1 )e11 GAIN TRANSFER A N D HIERARCHICAL ESTIMATION £ t £ Pt(k £ £ = ^c f(- T \) Ri(k 1_ f I + f|ic 1 + iy {xi(k f f -Hj(k+ + f|ic + l) £ £ 30 W I L L I A M T. GARDNER Substituting (26) into (27), = £ { [ χ , _ (k+l\k+\) l PJ(k+l\k+l) + KJ(k + l){y,-(/c + 1) - HJ(k + l ) x , _ ,(fc + 11 k + l ) ] [ x j _ , ( * + 11 fc + 1) T + KJ(k + l ) [ y # + 1) - HJ(k + l ) x , - _ , ( * + 11 k + 1 ) ] } } .

00 Fig. 17. Local TA filter tilt error performance {φχ; φν). Figure 18 shows the results of the local T A filter ability to estimate heading error, φζ. T h e " S " t u r n maneuver is required to cause this t e r m to become observable. O n c e the initial effects of the linear and angular accelerations are imparted to the filter, the filter is insensitive to external influences. T h e local D U filter performance for the propagation of the tilt errors is shown in Fig. 19. These values represent the reconstructed tilt error values, since the D U filter does not directly measure these states.

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