Inertial Navigation Systems (INS)

INS Systems for Unmanned Vehicles

Inertial Guidance Systems for Drones, UAVs, UGVs

Due to their self-reliance, inertial navigation systems are extremely well-suited to the field of unmanned systems, widely used on Unmanned Aerial Vehicles (UAV) Unmanned Surface Vessels (USV) and Unmanned Ground Vehicles (UGV). Inertial guidance systems can be utilised in harsh environments, are not susceptible to jamming, and emit no significantly detectable radiation.

Unmanned vehicles such as drones, UAVs, USV, UGV incorporating INS systems are utilised in a wide range of applications including defense and security, aerial mapping and survey, and search and rescue.

MEMS INS & FOG INS

The inertial sensors in an INS will typically be constructed using either MEMS (microelectromechanical systems) or FOG (fiber optic gyro technology). As a general rule, MEMS INS will be smaller, lighter and lower-cost, whereas FOG INS provide higher performance but can be larger, heavier and more costly.

Embedded GPS Inertial Navigation System

VectorNav VN-300 Dual GNSS INS Inertial Navigation System

Small errors in the measured acceleration can, over time, be compounded into larger errors in system-calculated velocity and position. To provide the system with greater accuracy, drone INS may be combined with another source of information such as a GPS, or, under conditions where GPS navigation may be unreliable or unavailable, a computer vision processing unit.

The ability of the IMU to measure rapid changes in linear acceleration and angular velocity is thus combined with the extra information from the second system to form a more accurate estimation of the UAV’s navigational parameters.

Combining INS and GPS Data

A GNSS-INS navigation system that hybridises inertial navigation with GPS or computer vision will use an algorithm that fuses together the multiple sources of information. One of the most common algorithms is the Kalman filter.

The Kalman filter continuously tracks and updates estimates of the UAV’s position, using the computed errors to improve future estimates. If one of the alternative sources of information is computed to be more accurate than the INS, it can be used to help correct the overall drone position estimate.