Adaptive Bias-Removal Technique for Tracking Maneuvering Targets

摘要

This paper investigates a new adaptation model for tracking maneuvering targets. The model consists of two Kaiman filters operated instantaneously to track any sudden change in the target behavior. While the first filter processes the conventional target kinetics, the second filter processes the residual sequence to estimate any unwanted bias during the maneuver course. This bias estimate is fed to the first main filter to activate modification and correction stages adequately developed to reflect the internal relationship of the bias parameter to other kinetic variables. The ultimate goal of the presented analysis is to develop an efficient adaptation policy by which the overall tracking system performance can be adequately enhanced. Results show that this configuration has better performance compared to other traditional systems and it doesn't need burden computations.