Maneuvering Target Tracking Using Intelligent Control Techniques

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, an intelligent Kalman filter (IKF) and an intelligent interacting multiple model (IIMM) method are proposed. The proposed methods can overcome the mathematical limits of conventional methods and can simultaneously improve the tracking performance by using the fuzzy logic and the genetic algorithm (GA). The tracking performances of the proposed method are compared with that of the adaptive interacting multiple model (AIMM) algorithm in computer simulations.