Dynamic Indoor Localization Algorithm Based on Compressed Sensing and Inertial Navigation

In the process of indoor dynamic localization, the signal is liable to be disturbed by noise signals and the localization accuracy is not high. In this paper, inertial navigation units in mobile devices are used as auxiliary measurement means to realize WIFI dynamic localization process based on sparse representation. The extended Kalman filter is used to fuse the inertial navigation algorithm with the compressed sensing algorithm. The experimental results show that the system achieves a better localization and tracking effect, and the maximum dynamic localization error of the system is not more than 1.7m. The positioning results can be better compared with K-means, Dead reckoning and other algorithms. The tracking effect of estimated trajectory and experimental trajectory can be better achieved.