Experimental Evaluation of a Machine Learning-Based RSS Localization Method Using Gaussian Processes and a Quadrant Photodiode

The research interest on indoor Location-Based Services (LBS) has increased during the last years, especially using LED lighting, since they can deal with the dual functionality of lighting and localization with centimetric accuracy. There are several positioning approaches using lateration and angular methods. These methods typically rely on the physical model to deal with the multipath effect, environmental fluctuations, calibration of the optical setup, etc. A recent approach is the use of Machine Learning (ML) techniques. ML techniques provide accurate location estimates based on observed data without requiring the underlying physical model to be described. This work proposes an optical indoor local positioning system based on multiple LEDs and a quadrant photodiode plus an aperture. Different frequencies are used to allow the simultaneous emission of all transmitted signals and their processing at the receiver. For that purpose, two algorithms are developed. First, a triangulation algorithm based on Angle of Arrival (AoA) measurements, which uses the Received Signal Strength (RSS) values from every LED on each quadrant to determine the image points projected from each emitter on the receiver and, then, implements a Least Squares Estimator (LSE) and trigonometric considerations to estimate the receiver's position. Secondly, the performance of a data-driven approach using Gaussian Processes is evaluated. The proposals have been experimentally validated in an area of 3 × 3 m
$^{2}$
and a height of 1.3 m (distance from transmitters to receiver). The experimental tests achieve p50 and p95 2D absolute errors below 9.38 cm and 21.94 cm for the AoA-based triangulation algorithm, and 3.62 cm and 16.65 cm for the Gaussian Processes.