An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays

2026-04-03

AI Summary

This study addresses the challenge of achieving real-time, high-precision state estimation for cooperative underwater navigation of unmanned vehicles in GNSS-denied environments, where high-latency acoustic communications hinder conventional approaches. The authors propose an asynchronous dual-rate Kalman filtering architecture comprising a high-rate thread for real-time control and a low-rate thread for processing delayed observations. By integrating a finite-length state buffer with a variational history distillation (VHD) projection mechanism, delayed measurements are efficiently propagated to the current state without recomputing past trajectories. Evaluated under 30-second communication delays, the method achieves trajectory root-mean-square error (RMSE) comparable to batch optimization algorithms while maintaining sub-millisecond per-update computational latency, significantly outperforming traditional extended and unscented Kalman filters and enabling synergistic optimization across communication, computation, and control.

Abstract

In GNSS-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation crucial for accurate state estimation. However, the severe communication delay inherent in underwater acoustic channels poses serious challenges to real-time state estimation. Traditional filters, such as Extended Kalman Filters (EKF) or Unscented Kalman Filters (UKF), usually block the main control loop while waiting for delayed data, or completely discard Out-of-Sequence Measurements (OOSM), resulting in serious drift. To address this, we propose an Asynchronous Two-Speed Kalman Filter (TSKF) enhanced by a novel projection mechanism, which we term Variational History Distillation (VHD). The proposed architecture decouples the estimation process into two parallel threads: a fast-rate thread that utilizes Gaussian Process (GP) compensated dead reckoning to guarantee high-frequency real-time control, and a slow-rate thread dedicated to processing asynchronously delayed collaborative information. By introducing a finite-length State Buffer, the algorithm applies delayed measurements (t-T) to their corresponding historical states, and utilizes a VHD-based projection to fast-forward the correction to the current time without computationally heavy recalculations. Simulation results demonstrate that the proposed TSKF maintains trajectory Root Mean Square Error (RMSE) comparable to computationally intensive batch-optimization methods under severe delays (up to 30 s). Executing in sub-millisecond time, it significantly outperforms standard EKF/UKF. The results demonstrate an effective control, communication, and computing (3C) co-design that significantly enhances the resilience of autonomous marine automation systems.

Problem

• UUV cooperative navigation
• acoustic communication delay
• Out-of-Sequence Measurements
• real-time state estimation
• dead-reckoning drift

Innovation

• Asynchronous Two-Speed Kalman Filter
• Variational History Distillation
• Out-of-Sequence Measurements
• Underwater Cooperative Navigation
• Gaussian Process Dead Reckoning