Intelligent adaptive gain adjustment and error compensation for improved tracking performance

While a standard Kalman filter (or α-β filter) is commonly used for target tracking, it is well known that the filter performance is often degraded when the target heavily maneuvers. The usual way to accommodate maneuver is to adaptively adjust the filter gain. Our aim is to reduce the tracking error during substantial maneuvering using a combination of nontraditional "intelligent" algorithms. In particular, we propose an effective gain control using fuzzy rule followed by position error compensation via neural network. A Monte-Carlo simulation is performed for various target paths of representative maneuvers employing the proposed algorithm. The results of the simulation indicate a significant improvement over conventional methods in terms of stability, accuracy, and computational load.