A novel approach to autonomous navigation for small UAVs was recently introduced by the authors. An abridged description of this approach can be found in present paper, with its main contribution being further evaluation of effects of wind on navigation performance. The vehicle dynamic model (VDM) serves as the main process model within the introduced navigation filter. The proposed method exploits the knowledge of physical properties of the UAV together with control input with the goal to significantly increase the accuracy and reliability of autonomous navigation. This is especially relevant for small UAVs with low-cost IMUs on-board and no extra sensor added to the conventional INS/GNSS setup. The improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Robustness and scalability of the navigation system against random changes in wind velocity are investigated via Monte Carlo simulations using real 3D wind velocity data. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to conventional kinematic modeling in INS/GNSS integration. Simulations also reveal that navigation errors are almost doubled as wind velocity doubles, which gives an estimation of the scalability of the navigation system.