Range Error Mitigation

Range error mitigation focuses on improving the accuracy of distance measurements, crucial for precise localization systems like those using Ultra-wideband (UWB) technology. Current research emphasizes using deep learning models, particularly deep generative models and optimized neural networks, to correct for errors caused by factors such as multipath propagation and non-line-of-sight conditions. These advancements enable more reliable and power-efficient localization in diverse environments, impacting applications ranging from indoor navigation to robotics and asset tracking.