Abstract
Machine Learning framework adds a new dimension to the localization estimation problem; it tries to find the most likely position using processed features in a radio map. This paper compares the performance of two machine learning tools, Random Forest (RF) and XGBoost, in exploiting the multipath information for outdoor localization problem. The investigation was carried out in a noisy outdoor scenario, where non-line-of-sight between target and sensors may affect the location of a radio-frequency emitter strongly. It is possible to improve the position system performance by using fingerprints techniques that employ multipath information in a Machine Learning framework, which operate a dataset generated by ray-tracing simulation. Usually, real measurements produce the fingerprints localization features, and there is mismatching with the simulated data. Another drawback of NLOS features extraction is the noise level that occurs in position processing. Random Forest algorithm uses fully grown decision trees to classify possible emitter position, trying to achieve error mitigation by reducing variance. On the other hand, XGBoost approach uses weak learners, defined by high bias and low variance. The results of the simulation performed aims to be used as a design parameter to perform hyperparameter refinements in similar multipath localization problems.
A high-bias, low-variance introduction to Machine Learning for physicists
