Maneuver target tracking with an acceleration estimator using target past positions

Abstract

SUMMARYTarget tracking filters with a low computational load are desired in air traffic control systems in order to simultaneously search and track multiple aircraft. The applications of the α-β filter and the Kalman filter have been widely studied. Although excellent tracking accuracy can be obtained for linear motion by the α-β filter, the tracking accuracy degrades for a maneuvering target, the difference between the predicted position and the actual target position becomes large, and mistracking where the target is lost may occur. While there has been research on filters that estimate the acceleration, these filters could only be used when there is little maneuvering and the computational load is huge. In this research, we propose a method that adds an acceleration term during turning motion as the tracking filter for maneuvering motion and not just linear motion and estimates the turning acceleration from past estimated positions of the target. We also reduce the tracking errors for a maneuvering target. We use simple trajectories of the target in computer simulations and compare the proposed method to conventional filters to demonstrate its effectiveness. And we evaluate the performance of the proposed acceleration estimation method.

Adaptive Sliding Mode Guidance Law Considering Input Saturation and Autopilot Lag

“…In realistic flight control system, these variables cannot be measured directly and therefore must be estimated. Many estimation methods were used in previous works, such as Kalman Filter [15], α β − Filter [16], divided difference filters [17]. In this study, we incorporate a relatively simple estimator called sliding mode observer / differentiator to demonstrate the estimation effect of the interception accuracy.…”

Video-microscopy-based automated trajectory determination

“…A number of techniques use a Kalman filter ( 7 ) for this purpose ( 8 ). Constraints on the acceleration/deceleration, radius of turn, or inertia can be used to isolate only the objects of interest, increasing computational efficiency ( 9 , 10 , 11 , 12 ). However, one limitation is that these methods are primarily intended for tracking single objects under low noise conditions, although modifications exist to remove these constraints ( 13 , 14 , 15 ).…”

Video-Microscopy-Based Automated Trajectory Determination for High-Velocity, Densely Clustered, Indistinguishable Objects Moving in A Directed Force Field

“…A number of techniques use a Kalman filter (Kalman, 1960) for this purpose (Cerveri et al, 2003). Constraints on the acceleration/deceleration, radius of turn, or inertia can be used to isolate only the objects of interest increasing computational efficiency (Barniv, 1985; Fortmann et al, 1983; Hashiro et al, 2002; Logothetis et al, 2002). However, one limitation is that these methods are primarily intended for tracking single objects under low noise conditions, although modifications exist to remove these constraints (Blanding et al, 2007; Chen and Tugnait, 2001; Hong et al, 1998).…”