Our dependency on Global Navigation Satellite System (GNSS) for getting directions, tracking items, locating friends, or getting maps of the world has increased tremendously over the last decade. However, as soon as we enter a building, the signal strength of the satellites is too low, and we need to resort to other technologies to achieve the same goals. An Indoor Positioning System (IPS) may utilize a wide range of methods for positioning a device, such as fingerprinting, multilateration, or sensor fusion, while using one or several radio technologies to measure Received Signal Strength (RSS) or Time of Arrival(ToA). Sensor fusion is an efficient approach where an Inertial Measurement Unit (IMU) is combined with, e.g., RSS measurements converted to distances. But this approach has significant drawbacks in areas where, e.g., walls or large objects obstruct the signal path, which introduces bias in the distance estimates. This paper addresses the bias caused by signal path obstruction by compensating the measured RSS with localized RSS attenuation adjustments and thereby increasing the accuracy of the sensor fusion model significantly. We also show that a system can learn the compensation parameters over time, reducing the installationefforts and achieving higher accuracy than a fingerprinting-based system.