A distributed and low-order GPS/SINS algorithm of flight parameters estimation for unmanned vehicle

A distributed, low-order and highly efficient strapdown navigation algorithm is developed to estimate the real flight parameters for flight control system. Firstly, the attitude estimation Kalman filter is designed according to the relationship between angular and angular rate. Then, the measured angular rate is compensated by solving the drift noise of gyro. Furthermore, the accuracy of attitude is improved by compensating the accelerometer of the rigid body from differential velocity of GPS. Secondly, the improved attitude is used as an input into the filter algorithms of velocity and position by using the concept of distributed Kalman filter, which reducing the order of Kalman filter effectively. The velocity and position filter algorithms, which provide smooth feedback parameters for control system, are developed based on the rigid body dynamic equations and GPS parameters. Finally, the numerical simulation indicates that the attitude provided by attitude Kalman filter is of high accuracy in flight condition, and the performance of the attitude velocity and position algorithms are verified by flight tests of a small unmanned tiltrotor.