Inertial navigation systems represent the ideal in automatic navigation. They operate entirely without external assistance, other than information as to their starting conditions, which makes them of great interest to both military and civil operators.

For over forty years, this Company has been one of the world's significant players in the field of iner tial navigation. From its inception, as an almost impossible, barely affordable technology for guiding strategic missiles, to today's acceptance of it as an everyday fact of life, the art and craft of iner tial navigation still retains some of its mystique, and continues to provide stretching challenges in engineering. This article doesn't pretend to be a textbook there are plenty of excellent and readable texts available (see, for example, refs. (1 3) ). This paper will attempt to explain only what is necessary in order to understand the significance of the current and future trends.

https://deanspacedrive.org/wp-content/uploads/2010/07/inertial_navigation_introduction.pdf

Developments in Inertial Navigation

Inertial Navigation - Forty Years of Evolution

In future lunar exploration, spatial disorientation may become an increasingly critical issue for astronauts as the area of exploration increases from several kilometers in the Apollo missions to over one hundred kilometers from the main base station in future landed missions. To address this problem, the Mapping & GIS Laboratory at The Ohio State University, working with partners, is developing a Lunar Astronaut Spatial Orientation and Information System (LASOIS) designed to provide astronauts with continuous navigation updates. Due to specific environmental conditions on the lunar surface (lack of familiar landmarks, ambiguous depth and shading cues, etc.), a multiple-sensor approach is proposed to overcome challenges to astronaut orientation. In this approach, data from on-suit sensors (stereo cameras, MEMS Inertial Measurement Unit (IMU), and foot-mounted pressure sensors) and from off-suit sensors (Lunar Reconnaissance Orbiter Camera) are integrated through an Extended Kalman Filter (EKF). The Zero Velocity Update (ZUPT) technique is used to compensate for distance errors while data from vision sensors are used to compensate for any IMU gyro drift. The spatial information generated by this integrated-data approach will be provided to astronauts through a wrist-mounted OLED (Organic Light-Emitting Diode) interface. Extensive field tests incorporating all of the above-mentioned sensors were performed in a Lunar-like environment at Moses Lake, WA. When compared to GPS-derived ground truth, the trajectory generated by the developed system was found to have a disclosure of 6 m for a total traverse of 107 m (5.6% accuracy). By integrating additional sensor systems (tactical grade IMU, radio-frequency identification beacons, star tracker) and improving data-processing algorithms, it is expected that this system ultimately will be able to achieve a disclosure of less than 2% overall.

methods

Development of a lunar astronaut spatial orientation and information system (lasois)

Along with advanced digital computing a revolutionary new inertial sensor has changed the design of inertial navigation systems. This new sensor, the ring laser gyroscope, is discussed

Ring laser gyros

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463300009024

Presidential Address: The Provenance of Navigational Science

In the unknown marine environment, due to special circumstances, the velocity measurement error of Doppler velocity log will increase, which will reduce the navigation accuracy of a SINS/GNSS/DVL integrated navigation system. To tackle this problem, an adaptive Kalman filter algorithm using Q-learning reinforcement learning is proposed. By taking the integrated navigation system as the environment, and the reciprocal of current position error as the reward, the proposed algorithm adaptively obtains the optimal velocity measurement noise estimation. Simulation results show that the proposed algorithm can effectively overcome the influence of the external current velocity, reduce the velocity error and position error, and improve the integrated navigation performance. 

Application of Reinforcement Learning in SINS/GNSS/DVL Integrated Navigation

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463300000874

Developments in Inertial Navigation

Citations

Inertial Navigation - Forty Years of Evolution

Development of a lunar astronaut spatial orientation and information system (lasois)

Ring laser gyros

Presidential Address: The Provenance of Navigational Science

Application of Reinforcement Learning in SINS/GNSS/DVL Integrated Navigation

Related Papers (5)

Inertial sensors technologies for navigation applications: state of the art and future trends

Strapdown inertial navigation technology

Low-Cost Inertial Navigation

Fundamentals of Inertial Navigation

Status and Development Trend of Inertial Technology