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

Abstract: The Global Positioning System (GPS) and an Inertial Navigation System (INS)are two basic navigation systems. Due to their complementary characters in manyaspects, a GPS/INS integrated navigation system has been a hot research topic inthe recent decade. Both advantages and disadvantages of each individual systemare analyzed.The Micro Electrical Mechanical Sensors (MEMS) successfully solved theproblems of price, size and weight with the traditional INS. Therefore they arecommonly applied in GPS/INS integrated systems. The biggest problem ofMEMS is the large sensor errors, which rapidly degrade the navigationperformance in an exponential speed. By means of different methods, i.e.autoregressive model, Gauss-Markov process, Power Spectral Density and AllanVariance, we analyze the stochastic errors within the MEMS sensors. Real testson a MEMS based inertial measurement unit for each method are carried out. Theresults show that different methods give similar estimates of stochastic errorsources. These error coefficients can be used further in the Kalman filter for betternavigation performance and in the Doppler frequency estimate for fasteracquisition after the GPS signal outage.Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra tightGPS/IMU navigation, are introduced with a brief analysis of each character. Theloose integration principles are given with detailed equations as well as the basicINS navigation principles.The Extended Kalman Filter (EKF) is introduced as the basic data fusionalgorithm, which is also the core of the whole navigation system to be presented.The kinematic constraints of land vehicle navigation, i.e. velocity constraint andheight constraint, are presented. These physical constraints can be used asadditional information to further reduce the navigation errors. The theoreticalanalysis of the Kalman filter with constraints are given to show the improvementon the navigation performance. As for the outliers in practical applications, theequivalent weight is introduced to adaptively reduce the influence on positioningaccuracy.A detailed implementation process of the GPS/IMU integration system is given.Based on the system model, we show the propagation of position standard errorswith the tight integration structure under different scenarios. Even less than 4observable satellites can contribute to the integrated system. Especially 2 satellitescan maintain the orientation errors at a reasonable level due to the benefit of thetight integration. A real test with loose integration structure is carried out, and theEKF performance as well as the physical constraints are analyzed in detail. Also atest with random outliers at the resolution level is carried out to show theeffectiveness of the equivalent weight. Finally some suggestions on future researchare proposed.