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In this paper, a new fuzzy alpha-beta filter for tracking a highly maneuvering target is presented. The target may maneuver at uniform angle rates by executing sharp turns within a short time. A set of fuzzy if–then rules is used to estimate the index of maneuverability (tracking index λ). This index λ is used to estimate the optimal αβ filter coefficients. The proposed filter is referred to as the Improved Fuzzy alpha-beta filter (IFαβ filter). It is shown through numerical simulations that the proposed filter does not only achieve a good tracking accuracy, but can also be used to detect the start and the end of target maneuvers. Compared to the standard fuzzy alpha-beta filter, the proposed fuzzy algorithm is better, in terms of both accuracy and computational complexity.

A commonly encountered problem for tracking community is: target tracking in Cartesian plane while the measurements are delivered by radar in polar coordinates. In this paper we consider a maneuvering target tracking problem by using the Fast Interacting Multiple Model (FastIMM) algorithm. Based on the theoretical framework of the Debiased Converted Measurements Kalman Filter (DCMKF), we propose the use of the pseudo-static versions based on the well known αβ and αβγ filters. The performances of these nonlinear filters, have been assessed by means of Monte Carlo simulations and compared to that of the standard filter. The proposed filter is also suitable for real time implementation, which makes it a potential candidate for applications on embedded systems.