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2 TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2 2017

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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

Jeong T., Njonjo A.W., Pan B.F.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2017

Vol. 11, no. 1

55--61

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.

A study of optimization of Alpha-Beta-Gamma-Eta filter for tracking a high dynamic target

Jeong T., Pan B.F., Njonjo A.W.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2017

Vol. 11, no. 1

49--53

The tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel’s position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The Alpha-Beta-Gamma filter is one of the special cases of the general solution pro-vided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity and acceleration for the nth observation, and also predicts the next position and velocity. Although found to track a maneuvering target with a good accuracy than the constant velocity, Alpha-Beta filter, the Alpha-Beta-Gamma filter does not perform impressively under high maneuvers such as when the target is undergoing changing accelerations. This study, therefore, aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The Alpha-Beta-Gamma filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration in order to improve the filter’s performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, Alpha-Beta-Gamma-Eta, algorithm as compared to the constant acceleration model, Alpha-Beta-Gamma in terms of error reduction and stability of the filter during target maneuver.