Ranging Error Correction in a Narrowband, Sub-GHz, RF Localization System

Sammanfattning: Being able to keep track of ones assets is a very useful thing, from avoiding losing ones keys or phone to being able to find the needed equipment in a busy hospital or on a construction site. The area of localization is actively evolving to find the best ways to accurately track objects and devices in an energy efficient manner, at any range, and in any type of environment. This thesis focuses on the last aspect of maintaining accurate localization regardless of environment. For radio frequency based systems, challenging environments containing many obstacles, e.g., indoor or urban areas, have a detrimental effect on the measurements used for positioning, making them deceptive. In this work, a method for correcting range measurements is proposed for a narrowband sub-GHz radio frequency based localization system using Received Signal Strength Indicator (RSSI) and Time-of-Flight (ToF) measurements for positioning. Three different machine learning models were implemented: a linear regressor, a least squares support vector machine regressor and a gaussian process regressor. They were compared in their ability to predict the true range between devices based on raw range measurements. Achieved was a 69.96 % increase in accuracy compared to uncorrected ToF estimates and a 88.74 % increase in accuracy compared to RSSI estimates. When the corrected range estimates were used for positioning with a trilateration algorithm using least squares estimation, a 67.84 % increase in accuracy was attained compared to positioning with uncorrected range estimates. This shows that this is an effective method of improving range estimates to facilitate more accurate positioning.