Abstract: The purpose of this paper is to provide a practical
example to the Linear Quadratic Gaussian (LQG) controller. This
method includes a description and some discussion of the discrete
Kalman state estimator. One aspect of this optimality is that the
estimator incorporates all information that can be provided to it. It
processes all available measurements, regardless of their precision, to
estimate the current value of the variables of interest, with use of
knowledge of the system and measurement device dynamics, the
statistical description of the system noises, measurement errors, and
uncertainty in the dynamics models.
Since the time of its introduction, the Kalman filter has been the
subject of extensive research and application, particularly in the area
of autonomous or assisted navigation. For example, to determine the
velocity of an aircraft or sideslip angle, one could use a Doppler
radar, the velocity indications of an inertial navigation system, or the
relative wind information in the air data system. Rather than ignore
any of these outputs, a Kalman filter could be built to combine all of
this data and knowledge of the various systems- dynamics to
generate an overall best estimate of velocity and sideslip angle.
Abstract: Launch and recovery helicopter wind envelope for a
ship type was determined as the first step to the helicopter
qualification program. Flight deck velocities data were obtained by
means of a two components laser Doppler anemometer testing a
1/50th model in the wind tunnel stream. Full-scale flight deck
measurements were obtained on board the ship using a sonic
anemometer. Wind tunnel and full-scale measurements were
compared, showing good agreement and finally, a preliminary launch
and recovery helicopter wind envelope for this specific ship was
built.