Magnetic Field Aided Indoor Navigation
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Many current aiding methods do not work well in an indoor environment, like aiding using the Global Positioning System. The method presented in this research uses magnetic field intensity data from a three-axis magnetometer in order to estimate position using a maximum - likelihood approach. The position measurements are then combined with a motion model using a Kalman filter. The magnetic field navigation algorithm is tested using a combination of simulated and real measurements. The result of these tests show that the position aiding algorithm is capable of generating positon estimates from ...
Many current aiding methods do not work well in an indoor environment, like aiding using the Global Positioning System. The method presented in this research uses magnetic field intensity data from a three-axis magnetometer in order to estimate position using a maximum - likelihood approach. The position measurements are then combined with a motion model using a Kalman filter. The magnetic field navigation algorithm is tested using a combination of simulated and real measurements. The result of these tests show that the position aiding algorithm is capable of generating positon estimates from real data within less than 1 meter of the true trajectory, with most estimates .3 meters away from the true trajectory in a laboratory hallway environment. To further explore the capabilities of the position aiding algorithm, a leader-follower scenario is implemented. In this scenario, the follower uses magnetic field intensity data collected by the leader to estimate its current position and attempt to follow the leader's trajectory. The results show that tracking is possible, and that the measurement span of the leader has a large impact on the result.
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