Accurate indoor positioning has been the subject of investigation for many years. Modern smartphones have a wide suite of internal sensors that allow us to measure different signals. However, traditional positioning methods, such as GPS, typically fail when measurements are taken indoors. Many different solutions have been proposed that rely on various data inputs, including Wi-Fi and camera input. Some proposed methods have used auxiliary data inputs such as BLE Beacons. However, any auxiliary input would require additional infrastructure to purchase and maintain, increasing expenses. This paper explores a variety of indoor positioning techniques that do not require any additional infrastructure beyond what is typically found in a commercial environment. This research explores, implements, and measures, through standardized tests, Wi-Fi RSSI, RTT, and marker-based trilateration, as well as, fingerprinting with two separate machine learning models, and also tests an implementation of PoseNet. It compares and contrasts the various methods, categorizing them according to a proposed set of criteria for evaluating a commercially deployable indoor positioning solution. The paper closes with a brief summary of the techniques that were studied and proposes investigation into various related topics and improvements, as well as future directions.
