Localization based on TDoA projection for autonomous vehicles in 6G cellular networks

6G cellular networks are expected to support a full service for autonomous driving. A super-resolution localization is required for the full service. When TDoA measurements are available, most localization systems usually rely on Kalman filtering for tracking of high-speed vehicles. However, TDoA outages considerably deteriorates the localization performance for Kalman filtering especially in wireless environments such as 6G cellular networks. This paper addresses a localization method based on TDoA projection for the autonomous vehicles in 6G cellular networks. The proposed approach exploits a TDoA projection in order to replace the TDoA measurement whenever a TDoA measurement outage occurs. For the TDoA projection, we derive the probability distribution of the TDoA measurement conditioned on the previous location estimate. Then, the presented technique establishes the hyper-ellipsoid for the TDoA projection using the conditional distribution. Finally, the approach selects a TDoA projection sample on the hyper-ellipsoid, which gives a minimum distance between the projection sample and the measurement-based coordinate. Simulation results exhibit that the projection method effectively avoids the performance degradation due to the TDoA outages.