Drift Improvement with Reinforcement Training of Inertial Sensors

Analyzing the use of Reinforcement Learning (RL) to extend the holdover time of inertial sensors in the absence of a Global Navigation Satellite System (GNSS).

Naval Information Warfare Center, San Diego, CA

The main objective of the Drift Improvement through Reinforcement Training - Inertial sensors (DIRT-I) project is to extend the holdover time of inertial sensors in the absence of a Global Navigation Satellite System (GNSS), through the use of Reinforcement Learning (RL) or training. For the purposes of this document, the acronyms GNSS and GPS (Global Positioning System) are used interchangeably. This report is a continuation of the year one effort that was reported on previously. The year two effort (and this report) focus on the use of different inertial sensors with a wide range of performance specifications.

The goal was to determine if the RL system offered similar performance regardless of the inertial sensor being used, or if the inertial sensor's performance limited the amount of improvement the RL system could offer. To answer this question, the same setup that was used in first year report was utilized for this work. The main difference is that data was logged from multiple inertial sensors (instead of one) and the same RL algorithm was used (rather than comparing multiple algorithms).