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
10. T w o local filters process velocity and attitude match observations and propagate the error variances by standard K a i m a n filter processes presented by (13) and (14). I n this simulation the local filters are represented as parallel paths so indicated in Fig. 10. I n parallel, a central hierarchical estimator is formulating the global filter state estimates using input from these local system estimators. This is represented in Fig. 10 as the central path. Periodically, the local gains KH HH and RT matrices are passed to the central estimator according to (38), thus reconstructing the effects of observations that the local estimators are processing.
Local D U filter tilt error performance (φχ; ψγ). 57 Time (min) Fig. 20. L o c a l D U filter heading error performance (φζ). 57 Time (min) Fig. 2 1 . L o c a l T A filter master-to-slave misalignment performance ( Δ ζ χ ; Δζγ; Δ ζ ζ) . 00 W I L L I A M T. G A R D N E R 50 e r r o r is e s t i m a t e d w e l l , a s s h o w n i n F i g . 1 8 ; c o n s e q u e n t l y t h e l o c a l T A also estimating t h e misalignments Figure 2 2 shows the performance h a r m o n i z a t i o n e r r o r s , Αηχ, of the local D U a n d Αηζ.
Subsequent results are compared to these results to judge the performance of the gain transfer algorithm. Before the gain transfer algorithm is applied directly to a decentralized hierarchical estimator, the results of a simplified application of the gain transfer algorithm are presented. I n fact, this application parallels the use of the gain transfer approach for a decentralized hierarchical estimator, since in both cases one filter is reconstructing the effects of another filter without direct knowledge of observations.