Disclosed is an indoor and outdoor personal navigation
algorithm based on INS / GPS integration of MEMS. The method is implemented through the following steps of, in an INS / GPS loosely-coupled integrated
navigation error model, selecting the attitude error, the speed error and the
position error of INS and the constant drift errors of a
gyroscope and an
accelerometer as the state variables of an integrated
navigation system. Achieving personal position purely through inertial devices can lead to increase of navigation errors over time. The indoor and outdoor personal navigation
algorithm based on INS / GPS integration of the MEMS determines whether the
system enters an integrated mode through the accuracy of adjacent effective information detected by GPS to GPS measured positions. On the traditional basis of determining through the number of receiving satellites and precision
estimation factors, the indoor and outdoor personal navigation
algorithm based on INS / GPS integration of the MEMS adds a determining identification window. By fusing the
particle filter algorithm of indoor maps and determining whether particle calculation results meet objective facts, the indoor and outdoor personal navigation algorithm based on INS / GPS integration of the MEMS can correct the course errors of an original algorithm and accordingly optimize
pedestrian tracks, thereby well providing long-time and high-precision indoor autonomous navigation.