This paper describes a method that how a relatively compensate the position errors in the using of low cost Inertial Measurement Unit (IMU) has been evaluated and compared with the well established method based on a Kalman Filter (KF). The compensation algorithm for IMU has been applied to the problem of integrating information in Inertial Navigation System (INS). The KF is used to estimate and compensate the errors of an INS by using the integrated INS velocity and position, respectively.
First by using Kalman Filter, we try to reduce noise of acceleration data, where two of an acceleration, constant drift and period drift, are considered. With the constant drift, it depends on sensor and it always keeps on constant error. When using double integration for calculates distance and velocity, these kinds of drifts can make increasing velocity and position errors. So, we tried to find these errors and used constant compensation algorithm for compensation of errors in data.
Second, external environment circumstance is changed ordinarily. Almost of them can be changed on periodic time. The average drift can be obtained during constant periodic time. And use this value, we consider with a factor as a periodic external disturbance which affects to the exact position. We used a repetitive method to reduce the external environment change. We verified the proposed algorithm by simulation results.