Abstract
Maneuvering target tracking is an important technology in engineering applications [1]-[3]. The traditional methodologies for designing it can be divided into two categories: modelbased and model-free algorithms. Almost all tracking algorithms are model based. The main idea behind modelbased tracking algorithms is to choose a representation that fits the actual state trajectories of the target movement and then to estimate the state based on the noisy observations recorded by sensors. The Kalman filter and its extensions are the most popular methods to estimate the state of a system. However, the stability and convergence rate of these algorithms depend directly on the accurate initial state estimation, unknown parameters, and covariance matrices of the process and measurement noise.
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