Distributed processing method for multi‐GNSS/SINS integration system

Abstract

Integration of global navigation satellite system (GNSS) and strap-down inertial navigation system (SINS) in pseudo-range and pseudo-range rate measurements level has been demonstrated as an effective way to provide more reliable navigation solutions. Under this condition, the dimension of measurements matrix of the integration filter increases dramatically along with more GNSSs included in the integration, traditional centralized integration filter processing method cannot address the computational load well. In this study, a distributed integration filter processing method is proposed for the integration of multiple GNSSs and SINS using the pseudo-range and pseudo-range rates as the measurements. Measurement difference inner a same satellite constellation is conducted to remove the clock related variables, which reduces the dimension of the state vector. Secondly, a federated Kalman filter is employed to obtain global optimal estimation of the state variables, which reduces the measurement vector dimension. Mathematical model of the distributed processing model with the simplified integration filter scheme is given in detail. Through the simulation based on a dynamic trajectory, the performance of the proposed method is investigated and compared with the centralized integration processing method. Results show that new method has analogous position and velocity accuracy with better computation efficiency.