Evolutionary Particle Filter for Indoor Navigation and Location

Indoor positioning technology can provide accurate location services for pedestrians. MEMS inertial sensors are inexpensive and small in size. Therefore, inertial navigation and positioning become popular research direction. The inertial sensor, which contains 3-axis accelerometer and 3-axis gyroscope, collects the acceleration and angular velocity information. The relative position of the pedestrian is calculated by integrating the acceleration and the angular velocity. The extended Kalman filter estimates attitude, angular velocity, position, velocity and acceleration system state errors. The system state error is updated when the foot touches the ground. Directional drift is the main problem of inertial navigation. Correcting heading by adding auxiliary basic information is one of the more common methods, such as GPS, geomagnetism, and Wi-Fi, but the additional basic information adds to the extra cost. We propose a novel algorithm based on the fact that pedestrians cannot cross the wall during walking. After the extended Kalman filter, the step size and the azimuth change are used as the observed state to establish the walking motion model. Considering the map information, the particle filter estimates the pedestrian position. For the particle impoverishment problem, the mutation operation of the genetic algorithm is used. A healthy male participates in the experiment. The results show an absolute error of 1.6 m.