Virtual Velocity Framework Increases Navigation Accuracy in Autonomous Underwater Vehicles

Kalman filters, algorithms estimating motion and velocity, have been studied extensively to increase navigation accuracy in autonomous underwater vehicles (AUV). But the filters don’t adequately separate ocean current velocity from the velocity errors produced by navigation estimations, making the errors difficult to identify.

To address this problem, Wang et al. added a virtual velocity framework to the Kalman filter. The framework starts with a set of velocity measurements constructed from previous data and leaves out ocean current velocity data. Based on the characteristics of the ocean current, the incremental velocity data is used along with the feedback-corrected velocity data to complete the framework.

“The framework serves as a magnifying glass that emphasizes the estimations while subtly masking the ocean current velocity, so the navigation errors can be more quickly and accurately identified,” co-author Xixiang Liu said.

Simulations and experiments show the framework improved estimation accuracy in velocity and position. The method can be easily integrated into conventional AUV navigation systems without adding sensors or other hardware or significantly increasing computational load.

Standard AUV navigation systems consist of a strapdown inertial navigation system (SINS), which tracks position and orientation, and a doppler velocity log (DVL), which estimates velocity relative to the seabed.

SINS-DVL cannot obtain the velocity relative to the ground when the distance between the AUV and seabed is over the operating range. This occurs when an AUV is traveling in the ocean’s middle layer. Instead, velocity is estimated relative to the surrounding water, an unfixed reference, resulting in errors that need to be continually addressed.

Source: “A virtual velocity based integrated navigation method for strapdown inertial navigation system and doppler velocity log coupled with unknown current,” Zixuan Wang, Xixiang Liu, Xiaoqiang Wu, Guangrun Sheng and Yongjiang Huang, Review of Scientific Instruments (2022). The article can be accessed at https://doi.org/10.1063/5.0089117 .