Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation

arXiv:2402.14131v1 Announce Type: cross
Abstract: It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10 to 40 meters. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain …

abstract accuracy anomaly architectures arxiv case case study computing cs.lg earth eess.sp environment forests gps information machine navigation networks neural networks physics.data-an random random forests study type