An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for 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.

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.