By L. T. Leondes
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Extra resources for Advances in Aerospace Systems Dynamics and Control Systems/Part 1 of 3
Aerosp. Electron. Syst. AES-21, 292-300 (1985). 9. M . Ä T H A N S et ai, "The Stochastic Control of the F-8C Aircraft Using a Multiple Model Adaptive Control ( M M A C ) Method—Part 1: Equilibrium Flight," IEEE Trans. Autom. Control AC-22, 768-780 (1977). 10. C. S. GREENE and A. S. W I L L S K Y , "An Analysis of the Multiple Model Adaptive Control Algorithm," Proc. IEEE Conf. Decision Control, Albuquerque, pp. 1142-1145 (1980). 11. R. M . HAWKES and J. B. MOORE, "Performance Bounds for Adaptive Estimation," Proc.
M . FRY and A. P. SAGE, "On Hierarchical Structure Adaptation and Systems Identification," Int. J. Control 20, 433-452 (1974). 18. P. R. L A M B and L. C. WESTPHAL, "Simplex-Directed Partitioned Adaptive Filters," Int. J. Control 30,617-627(1979). 19. J. S. T H O R P , "Optimal Tracking of Maneuvering Targets," IEEE Trans. Aerosp. Electron. Syst, AES-9, 512-519 (1973). 20. R. L. MOOSE, "An Adaptive State Estimation Solution to the Maneuvering Target Problem," IEEE Trans. Autom. Control AC-20, 359-362 (1975).
The true measurement is described by z(k) = H(k)x(k) + w(k). ) random variables with uniform spatial distribution. T o track a target in the cluttered environment, a naive approach is to take the measurement "nearest" to the predicted one as the correct measurement. This is the so-called nearest neighbor standard filter ( N N S F ) and is very easy to implement. The problem with choosing the nearest neighbor, however, is that it might not be the measurement from the target. Therefore, the N N S F sometimes uses incorrect measurements, "believing" that they are correct.
Advances in Aerospace Systems Dynamics and Control Systems/Part 1 of 3 by L. T. Leondes