A Study on the Performance Comparison of Three Optimal Alpha-Beta-Gamma Filters and Alpha-Beta-Gamma-Eta Filter for a High Dynamic Target

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A Study on the Performance Comparison of Three Optimal Alpha-Beta-Gamma Filters and Alpha-Beta-Gamma-Eta Filter for a High Dynamic Target

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Tae-Gweon Jeong / Ann W. Njonjo / Bao Feng Pan

The Alpha-Beta-Gamma tracking filter is useful for tracking a constant acceleration target with zero lag error in the steady state. It, however, depicts a constant lag error for a maneuvering target. Various algorithms of the Alpha-Beta-Gamma tracking filter exist in literature and each one of them presents its own unique challenges and advantages depending on the design requirement. This study investigates the operation of three Alpha-Beta-Gamma tracking filter design methods which include Benedict-Bordner also known as the Simpson filter, Gray-Murray filter and the fading memory constant acceleration filter. These filters are then compared based on the ability to reduce noise and follow a maneuvering target with minimum lag error, against the jerky model Alpha-Beta-Gamma-Eta. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model in comparison with the constant acceleration models.