GPS/IMU Integrated System for Land Vehicle Navigation based on MEMS

The Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in the recent decade. Both advantages and disadvantages of each individual system are analyzed. The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with the traditional INS. Therefore they are commonly applied in GPS/INS integrated systems. The biggest problem of MEMS is the large sensor errors, which rapidly degrade the navigation performance in an exponential speed. By means of different methods, i.e. autoregressive model, Gauss-Markov process, Power Spectral Density and Allan Variance, we analyze the stochastic errors within the MEMS sensors. Real tests on a MEMS based inertial measurement unit for each method are carried out. The results show that different methods give similar estimates of stochastic error sources. These error coefficients can be used further in the Kalman filter for better navigation performance and in the Doppler frequency estimate for faster acquisition after the GPS signal outage. Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra tight GPS/IMU navigation, are introduced with a brief analysis of each character. The loose integration principles are given with detailed equations as well as the basic INS navigation principles. The Extended Kalman Filter (EKF) is introduced as the basic data fusion algorithm, which is also the core of the whole navigation system to be presented. The kinematic constraints of land vehicle navigation, i.e. velocity constraint and height constraint, are presented. These physical constraints can be used as additional information to further reduce the navigation errors. The theoretical analysis of the Kalman filter with constraints are given to show the improvement on the navigation performance. As for the outliers in practical applications, the equivalent weight is introduced to adaptively reduce the influence on positioning accuracy. A detailed implementation process of the GPS/IMU integration system is given. Based on the system model, we show the propagation of position standard errors with the tight integration structure under different scenarios. Even less than 4 observable satellites can contribute to the integrated system. Especially 2 satellites can maintain the orientation errors at a reasonable level due to the benefit of the tight integration. A real test with loose integration structure is carried out, and the EKF performance as well as the physical constraints are analyzed …