Augmentation to the Extended Kalman-Bucy filter for single target tracking

Tracking agile aircraft under high accelerations generally demands sophisticated models for determining trajectories with desirable dynamics and accuracy. Often this raises complexity of the estimation algorithm as it gives rise to more elaborated methods for both taking model nonlinearities into account and handling a greater number of state variables that describe the model. The approach of this work recalls a 3D model based on flight dynamics of a point of mass for which augmentation to the Extended Kalman-Bucy filter (EKBF) is proposed. Two methods of augmentation to the EKBF filter are studied: (i) use of second-order terms to approximate the model according to Daum's theory; (ii) deployment of a neural network coupled to the filter for compensation of modeling and calculation errors. The evaluation of the filters performance is accomplished by measuring nonlinearities, bias, accuracy and robustness. The designed filters are suitably accurate and robust for tracking targets in air combat scenario.