Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and
a microprocessor provide inertial digital data from which position and orientation is obtained by
integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes,
magnetometers can be used to derive the absolute user heading based on Earth’s magnetic field.
Unfortunately, the measurements of the magnetic field obtained with low cost sensors are
corrupted by several errors including manufacturing defects and external electro-magnetic fields.
Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading
measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is
presented to estimate the values of the bias and scale factor of low cost magnetometer. The main
advantage of this technique is the use of the artificial intelligence which does not need any error
modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the
proposed algorithm improve the heading accuracy and the results are also statistically significant.
Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined
with the INS and GPS/Wi-Fi especially in the indoor environments.
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