In response to the growing demand for sustainable transportation solutions, bike-sharing systems have gained prominence in supporting an eco-friendly means of commuting. Within this landscape, Skopei, a forward-thinking company specializing in innovative sharing propositions, has developed a smart bike lock capable of autonomous rentals and returns. This master’s thesis delves into the application of radio frequency-based distance measurements to create an indoor positioning system for the efficient management of bike storage. Specifically, the system is designed to determine whether bikes have been correctly parked at docking locations, enabling users to conclude their rentals autonomously. The architecture of this system uses a network of anchor nodes (known-location routers) that should ascertain the positions of mobile nodes (bikes) within the bicycle storage area. Notably, the solution developed in this thesis employs sophisticated distance measurement techniques, including frequency hopping and phase shift analysis. By finding the amplitude and phase shift over multiple frequencies, we can find the channel impulse response and estimate the distance using machine learning. We employ a novel Multi-layer Perceptron neural network regressor to improve the accuracy in the presence of complex environmental factors in bike storage environments. In the bike storage test case, we achieved a mean absolute error in position estimation of 1.68m compared to 3.80m of a naive approach. We improved the parking state classification from 75.99% of a naive approach to 98.09% with our machine-learning-based approach. This thesis underscores the importance of cutting-edge distance measurement methods and real world field studies in advancing indoor positioning systems, specifically for smart bike storage management. By bridging the gap between technology and sustainable transportation, this work aims to make urban bike-sharing systems more scalable, efficient, user-friendly, and environmentally conscious.