Digital Signal Processing, ( ISI ), Volume (23), No (5), Year (2013-5) , Pages (1678-1685)

Title : ( An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking )

Authors: Ahmadreza Amirzadeh , Ali Karimpour ,

Access to full-text not allowed by authors

Citation: BibTeX | EndNote

Abstract

In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of maneuver. It is shown that the AUKF method in conjunction with the FFM technique (FFM- AUKF) can estimate the target states appropriately since the FFM tunes the covariance matrix of the AUKF method in presence of unknown target accelerations by using a fuzzy system. In addition, the benefits of both FFM technique and AUKF method are employed in the scheme of well-known Interacting Multiple Model (IMM) algorithm. The proposed Fuzzy IMM (FIMM) algorithm does not need the predefinition and adjustment of sub-filters with respect to the target maneuver and reduces the number of required sub-filters to cover the wide range of unknown target accelerations. The Monte Carlo simulation analysis shows the effectiveness of the above-mentioned methods in maneuvering target tracking.

Keywords

, Maneuvering target tracking, Augmented Kalman Filtering method, Fuzzy Fading Memory technique Interacting Multiple Model algorithm