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A new neural network (NN) aided adaptive unscented Kalman filter (UKF) is presented for tracking high maneuvering target. In practice, the dynamic systems of many target tracking problems are usually nonlinear and incompletely observed, moreover, there may be large modeling errors when the target is maneuverable or some parameters of the system models are inaccurate or incorrect. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. On the other hand, some nonlinear filtering methods such as extended Kalman Filter (EKF) have been used to train a NN with fast convergence speed by augmenting the state with unknown connecting weights. Tackling the natural coalescent between the filtering algorithm and the NN described above, first a more efficient learning algorithm based on unscented Kalman filter (UKF) is derived, which can give a more accurate estimate of the weights and possess faster convergence rate. We then extend the algorithm to form a new NN aided adaptive UKF algorithm and use it in maneuvering target tracking applications. The NN in this algorithm is used to approximate the uncertainty of system models and is trained online, together with the target state estimation. Some simulations are also given to validate that the proposed method can give well state estimation of a highly maneuvering target.