Satellite navigation works pretty well in the open field. In other outdoor environments the performance of satellite navigation may suffer from degradation due to multipath and shadowing. This holds especially in urban canyons. In indoor environments satellite navigation may even fail due to large attenuation and blockage of signals. In order to enable navigation also in these environments other sensors are being considered.
Thus, the Institute also works on multisensor navigation. In addition to satellite navigation signals we consider signals from mobile radio stations, WLAN and RF ID tags as well as sensors such as MEMS, compass and altitude meters to enhance and enable navigation in challenging environments. We also consider movement models for pedestrian users and maps. Our approach is to apply Bayesian estimation to optimally combine the various sensors. The work also results in prototypes such as a LTE testbed for mobile radio based positioning and the DLR Navshoe with the integrated MEMS. In order to carefully estimate the performance of multisensor navigation we apply several methods to compare our results with ground truth including a holodeck for highest accuracy. Applications encompass pedestrian navigation, relative positioning in car-to-car environments and exploration.