In-flight Heading Estimation of Strapdown Magnetometers using Particle Filters

This paper presents a real-time heading estimation algorithm using IMU and strapdown magnetometer without any other external heading reference. To calibrate the magnetic deviation, sensor errors caused by hard iron effect and initial heading of strapdown magnetometers are considered. In our approach, sensor output distortion due to the soft iron effect is ignored, which is relatively small. First, for the estimation of heading angle, system and measurement model is derived, which is nonlinear. Then particle filter and extended Kalman filter is introduced for performance comparison. The proposed algorithm for the integration of IMU and magnetometer is verified via numerical simulation in Matlab. Simulation result demonstrates accurate heading estimation error within 1 degree for both algorithms when there exists small initial heading error and hard iron effect, yet particle filter provides more robust and precise result than the extended Kalman filter in case the initial heading error and biases are large.