An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

• Published in: Digital signal processing Vol. 23; no. 5; pp. 1678 - 1685
• Main Authors: Amirzadeh, Ahmadreza, Karimpour, Ali
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2013
• Subjects: Acceleration; Algorithms; Augmented Kalman Filtering method; Computer simulation; Dynamical systems; Fuzzy; Fuzzy Fading Memory technique; Interacting Multiple Model algorithm; Maneuvering target tracking; Maneuvers; Nonlinear dynamics; Tracking; Augmented Kalman Filtering method; Interacting Multiple Model algorithm; Maneuvering target tracking; Fuzzy Fading Memory technique
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2013.05.002

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. •The FFM-AUKF method provides accurate results in the state estimation of non-linear dynamic systems in presence of maneuver.•The FIMM algorithm has better tracking performance than conventional IMM algorithms with a large number of sub-filters.•The FIMM algorithm does not require the predefinition and adjustment of sub-filters in terms of target maneuver properties.•The FIMM algorithm reduces the number of required sub-filters to cover the wide range of unknown target accelerations.