Iterative Bias Estimation for an Ultra-Wideband Localization System

Three bias estimation frameworks are presented that mitigate position-dependent ranging errors often present in ultra-wideband localization systems. State estimation and control are integrated, such that the positioning accuracy improves over iterations. The frameworks are experimentally evaluated on a quadcopter platform. Two state augmentation frameworks show that the anchor placement has a significant influence on the observability of the problem. A third framework circumvented any observability issues by using a classifier. This framework performed best as it improves the tracking performance with respect to ground truth, and also smoothens the overall flight by significantly reducing unwanted oscillations; see https://youtu.be/J-htfbzf40U for a video.