The tracking filter plays a key role in accurate estimation and prediction of maneuvering the 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 α- β - γ filter is one of the special cases of the general solution provided 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 predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity α - β filter, the α - β - γ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The α - β - γ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration 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, α - β - γ - η algorithm as compared to the constant acceleration model, α - β - γ in terms of error reduction and stability of the filter during target maneuver.

“Tracking” here refers to the estimation of a moving object with some degree of accuracy where at least one measurement is given. The measurement, which is the sensor-obtained output, contains systemic errors and errors that are due to the surrounding environment. Tracking filters play the key role of the target-state estimation after the updating of the tracking system; therefore, the type of filter that is used for the conduction of the estimations is crucial in the determining of the reliability of the updated value, and this is especially true since the performances of different filters vary when they are subjected to different environmental and initial conditions. The purpose of this paper is the conduction of a comparison between the performances of the α-β-γ filter and the Kalman filter regarding an ARPA-system tracking module that is used on board high-dynamic warships. The comparison is based on the capability of each filter to reduce noise and maintain a stable response. The residual error is computed from the difference between the true and predicted positions and the true and estimated positions for the given sample. The results indicate that the tracking accuracy of the Kalman filter is higher compared with that of the optimal α-β-γ filter; however, the response of the optimal α-β-γ filter is more stable.