New users such as pedestrians are added to navigation systems with developing lightweight, portable, low-cost technologies. The pedestrian navigation systems are currently applied in miscellaneous fields including medicine, sport, military services, animation, robotics, etc. This amount of use has attracted the attention of many scholars over the last few decades. In this paper, the paths of a firefighter, as a pedestrian, was estimated approximately by the help of an inertial measurement unit (IMU) and acceleration sensors. To reduce the measured errors and noises by the sensor, zero velocity update (ZUPT) method and Kalman filter are exploited in a pedestrian navigation system. Due to the fact that the error in blind navigation is divergent over time if the filter is not used, the use of conventional accelerometer sensors cannot produce a satisfactory result. using the combined module of an inertial measurement sensor that includes accelerometer and gyroscope, it is possible to track the person’ s position at any moment while the sensor is tracked on the shoe. The ability of ZUPT in navigation system has been discussed and interpreted by measuring a path using a sensor installed on a person’ s shoe and comparing the results with the desired predetermined path.

In this paper the feasibility of rapid alignment and calibration of a static strapdown inertial navigation system (INS) is evaluated. Resting conditions including zero-velocity update and a known initial heading direction as virtual external measurement data are integrated with INS data. By comparing the virtual external measurements with the estimates of those generated by the aligning INS, estimates of the velocity and heading errors can be obtained and these errors will be propagated in the INS as a result of alignment inaccuracies. An extended Kalman filter based on an augmented process model and a measurement model is designed to estimate alignment attitudes and biases of inertial sensors. Monte Carlo simulation results show that the integration of INS with rest conditions is very effective in rapid and fine leveling and azimuth alignment of INS, but this type of data fusion due to poor acceleration and angular rates of static condition has no chance of valuable calibration of all inertial sensor biases.