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Maneuver detection algorithm based on probability density function estimation with use of RBF and HRBF neural network

The paper presents a new maneuver detection algorithm used in variable state dimension estimator. The proposed method is based on statistical test of two hypothesis which checks probability density function of innovation process of a tracking filter. The neural networks with radial and hyperradial basis functions are applied as probability density function and distribution function estimators. The results of numerical simulations are presented. The presented approach is also suitable for fault detection and diagnosis in dynamical systems.