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So, their performances depend on whether the rules can describe maneuver characteristics exactly and completely.
Unfortunately, these requirements are difficult to satisfy because of unknown maneuver characteristics in real situations.
Therefore, RLSF can be applied to estimate the target state by employing λ k to describe maneuver characteristics.
First, the proposed filter utilizes observation residuals and heading changes to describe maneuver characteristics of a maneuvering target and employs the fading factor of RLSF to express the magnitude of the target maneuver.
The paper employs the fading factor of RLSF to describe maneuver characteristics of the motion model, analyzes the relationship of the fading factor with observation residuals and heading changes, and maps the relationship into a set of fuzzy rules.
Parametric adaptive filters, generally with less computation, describe maneuver characteristics as unknown random parameters with certain probability distribution functions (pdf) and estimate maneuver models jointly by both the parameters and the target states[2].
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It was the autopilot, not the maneuvering characteristics augmentation system.
The technology that is under scrutiny is the maneuvering characteristics augmentation system, or MCAS.
MCAS is the acronym for an anti-stall feature known as the Maneuvering Characteristics Augmentation System.
Boeing designed that software, known as the Maneuvering Characteristics Augmentation System (MCAS).
Muilenburg recognized the role in both crashes of a Boeing-approved flight system, called Maneuvering Characteristics Augmentation System.
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