A Maximum Likelihood UWB Localization Algorithm Exploiting Knowledge of the Service Area Layout

In this paper a method for ultra-wideband (UWB) localization for indoor applications is proposed. Beacons at known locations exchange signals with a tag to the purpose of estimating its position from range measurements. These measurements are accurate only when the ray corresponding to the direct path (DP) from tag to beacon is strong enough. In an UWB indoor environment, however, the DP may be blocked by thick walls or metallic obstacles, giving rise to large range errors. Several methods are available to mitigate this problem, exploiting different degrees of prior information. Techniques exploiting range error models or based on traditional fingerprinting lead to better results than methods that do not require any prior knowledge. We propose a new method that combines the maximum likelihood principle with range error models and special fingerprints. Its performance, assessed by simulation and compared to other techniques, is shown to be superior to traditional fingerprinting in the presence of environmental changes.