Indoor Localization With Range-Based Measurements and Little Prior Information

Abstract

We address the problem of indoor localization with networks of sensors taking range-based measurements in the presence of very little prior information. We propose several robust methods that do not require previous measurement campaigns when a network is deployed. The focus is on networks of ultrawideband sensors, but the proposed range-based methods can also be applied to other types of sensor networks. The location of a target node is estimated from measured distances to anchor nodes of known positions. We allow for the possibility of large errors in the range measurements due to undetected direct path (UDP) propagation conditions. In mitigating the UDP effect, our approach is to combine intermediate location estimates from different subsets of beacons. We propose novel criteria for identifying the combinations that produce bad estimates. These combinations are then discarded in obtaining the final estimates. Simulations reveal that the proposed methods achieve improved performance with respect to that of existing techniques that exploit the same prior information. Under many scenarios, the proposed methods reach the performance of some algorithms that exploit more prior information.