Navigation Under GNSS Denied Environments: Zero Velocity and Zero Turning Update

Abstract

The objective of this paper is to present a method which bounds the error of an inertial navigation system (INS) when Global Navigation Satellite System (GNSS) is not available. Inertial navigation systems utilize gyroscopes and accelerometers, and calculate velocity, position and attitude, essentially by integrating the measurements obtained from these sensors. Due to the nature of integration, INS are notoriously prone to sensor biases and drifts. Typically, GNSS is used to correct the navigation system errors caused by the inertial sensor measurements. However, in GNSS degraded or denied environments, alternative solutions are required. If the platform on which an INS is mounted is known or estimated to be stationary, zero-velocity update (ZUPT) and/or zero turning update (ZTUPT) algorithms can be applied in order to bound the navigation system errors. Under certain assumptions, ZUPT based algorithms can be applied when the platform is not stationary. If a vehicle’s motion is constrained by the design of its kinematics, i.e. if it can be assumed that the vehicle cannot move or rotate along one or more of its body axes, ZUPT assisted Kalman estimators can be used to correct the errors along those axes. Potentially, ZUPT based estimation algorithms can also be utilized when a sufficiently high fidelity vehicle model is available. In this paper, the implementation of zero-velocity update (ZUPT) and zero turning update (ZTUPT) algorithms are analyzed for the purpose of estimating and bounding inertial navigation errors. The basic principle in navigation is based on combining the data obtained from the sensors onboard and the inertial navigation system through an Extended Kalman filter. Although this process requires additional software components, it potentially offers increased system accuracy and reliability. Incorporating the kinematics of the vehicle, along with a ZUPT and/or ZTUPT algorithm, provides additional data to feed into the Kalman filter and increases the efficiency of error estimation. Estimated error is then fed back into the INS algorithm in order to counteract the sources of error.