Apple seeks to patent machine learning correction of GPS estimates

GPS and similar navigational systems rely on orbiting satellites to triangulate users' locations, a process that's inherently susceptible to inaccuracy due to the vast distances between satellites and moving users on the ground. But Apple thinks it can improve location accuracy by applying machine learning to Kalman estimation filters, a just-published patent application reveals. The basic concept is that while navigation systems generally rely on live location-determining pings from multiple satellites -- a process that can take precious time, during which the user may move -- a machine learning model can be trained to provide interim location estimates for the user based on previously gathered data from the environment. For instance, a given city block might have fairly constant satellite signal reflection characteristics, commonly introducing errors into user location readings, so machine learning could counterbalance the inaccuracies. While GPS is the best-known satellite location system, Apple's application goes beyond it to include various types of global navigation satellite systems (GNSS), assuming in each case that each triangulation of raw satellite data and the machine learning-corrected version will be handed off to a Kalman linear quadratic estimation filter.