THIS document describes the development and implementation of the extended Kalman filters (EKF) utilized for navigation algorithms of small unmanned aerial vehicles. The states of a vehicle and sensor biases are estimated using only on-board sensors and the knowledge of statistical properties of the sensor performance. An accelerometer, gyroscope, global positioning system, magnetometer, barometer, and pitot-statis system are used to estimate the position, velocity, and attitude of the vehicle, sensor biases, and the wind speed. The results of the Monte Carlo simulations with synthetic vehicle trajectory are provided for multiple scenarios. The discussion includes the trade studies on implementation regarding sensor placement, unknown local magnetic disturbances, filter design, and computational cost.