Localization Using Ultra Wideband and IEEE 802.15.4 Radios with Nonlinear Bayesian Filters: a Comparative Study

In this work, we study the localization problem considering two Wireless Sensor Network (WSN) metrics commonly used for distance estimation. In this sense, we use Bayesian filters to combine odometry and distance estimations provided by the WSN devices. Our strategies aim at a general application and can be used for both indoor and outdoor environments depending only on the type of metric and radio technology employed. In this work, we investigate two metrics: Ultra Wideband (UWB) and the Received Signal Strength (RSS). The first one is a recent technology and presents better overall accuracy than other metrics, and the second is one of the most well-explored metrics in WSN-based localization approaches. We evaluated the performance of our two approaches and compare them with the Decawave® built-in application. The experiments were performed in simulated and real environments with different scenarios (indoors and outdoors) and sensor configurations. The results show the proposed strategies feasibility by improving the localization accuracy for both types of environments. In indoor environments, the proposed system has a mean position error bellow 0.09 meters and mean orientation error of 0.08 rads. Furthermore, the proposed system is in average 0.03 meters more accurate than the built-in application.