Predictive Modeling in AUV Navigation: A Perspective from Kalman Filtering
Authors: Zizhan Tang, Yao Liu, Jessica Liu
Organizations: Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
We present a safety-oriented framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss. Acoustic signals emitted by the AUV are detected by a network of fixed buoys, which compute Time-Difference-of-Arrival (TDOA) range-difference measurements serving as position observations. These observations are subsequently fused with a Kalman-based prediction model to obtain continuous, noise-robust state estimates. The combined method achieves significantly better localization precision and trajectory stability than TDOA-only baselines. Beyond real-time tracking, our framework offers targeted search-and-recovery capability by predicting post-disconnection motion and explicitly modeling uncertainty growth. The search module differentiates between continued navigation and propulsion failure, allowing search resources to be deployed toward the most probable recovery region. Our framework fuses multi-buoy acoustic data with Kalman filtering and uncertainty propagation to maintain navigation accuracy and yield robust search-region definitions during communication loss.
Figures & tables
Fig. 1: Illustration of the TDOA-based localization system. The submersible emits acoustic signals received by multiple buoys at different times, from which the travel time differences are used to estimate its position.
Fig. 2: Mechanical interpretation of the submersible motion under combined engine and ocean-current accelerations.
Fig. 3: Illustration of the search process under communication loss. The predicted trajectory (red) is generated by the Kalman filter, while the blue curve denotes the true motion. The shaded circles illustrate the evolving search regions under different operating conditions.
Fig. 4: Localization results for 1000 test points. Blue circles denote true positions; yellow markers represent TDOA estimates.
Fig. 5: Cumulative Distribution Function (CDF) of Mean Absolute Error (MAE) for Submarine Localization. The CDF curve illustrates the probability that the localization error is less than or equal to a given MAE threshold. Nearly all test points have errors below 4 m, demonstrating high localization accuracy.
Fig. 6: Simulation result of the prediction model. The real trajectory, TDOA measurements, and Kalman filter predictions are shown together for comparison.
Fig. 7: MSE comparison between the TDOA model and Kalman filter prediction. The Kalman filter achieves consistently lower error across time steps.
Fig. 8: Simulation of the search process when disconnection occurs at t=50s . The predicted trajectory (red) closely follows the real trajectory (blue) along the predetermined route.
Fig. 9: Simulation of the search process when disconnection occurs at t=80s . The divergence between predicted and real positions increases with time as uncertainty accumulates.
Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking survey scale and anchor placement to acoustic coverage, a conflict-graph-based TDMA uplink scheduler for shared-channel servicing, and delayed fusion of received USBL updates with drift-prone dead reckoning. The framework is evaluated in the HoloOcean simulator using heterogeneous ASV-AUV teams executing lawnmower coverage missions. The results show that localization performance is shaped by the interaction of survey scale, acoustic coverage, team composition, and ASV-formation geometry. Further, the spatial-reuse scheduler improves per-AUV fix delivery rate without violating the no-collision constraint, while maintaining low end-to-end fix latency.
Pranav Kedia, Rajini Makam, Heiko Hamann +1
Dept of Computer and Information Science, University of Konstanz, Germany · Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany · Dept of Aerospace Engineering, Indian Institute of Science, Bangalore, India
The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent interruptions. When communication outages occur, standard state estimators such as the Unscented Kalman Filter (UKF) will be forced to make open-loop predictions. If the environment contains unmodeled dynamic factors, such as unknown ocean currents, this estimation error will grow rapidly, which may eventually lead to mission failure. To address this critical issue, this paper proposes a Variational History Distillation (VHD) approach. VHD regards trajectory prediction as an approximate Bayesian reasoning process, which links a standard motion model based on physics with a pattern extracted directly from the past trajectory of the UUV. This is achieved by synthesizing ``virtual measurements'' distilled from historical trajectories. Recognizing that the reliability of extrapolated historical trends degrades over extended prediction horizons, an adaptive confidence mechanism is introduced. This mechanism allows the filter to gradually reduce the trust of virtual measurements as the communication outage time is extended. Extensive Monte Carlo simulations in a high-fidelity environment demonstrate that the proposed method achieves a 91% reduction in prediction Root Mean Square Error (RMSE), reducing the error from approximately 170 m to 15 m during a 40-second communication outage. These results demonstrate that VHD can maintain robust state estimation performance even under complete communication loss.
Shuyue Li, Miguel López-Benítez, Eng Gee Lim +5
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China · School of Engineering, University of Liverpool, Liverpool, UK · Jiangsu JITRI Tsingunited Intelligent Control Technology Co., Ltd., Wuxi, China +4
Autonomous underwater vehicles (AUVs) commonly rely on inertial navigation systems (INS) aided by Doppler velocity logs (DVLs) for reliable underwater navigation. Accurate DVL velocity estimation is therefore essential for successful operation. Recent learning-based methods have demonstrated improved DVL velocity estimation, particularly under degraded measurement conditions. However, their training objectives typically rely on conventional regression losses that are highly sensitive to large residuals and corrupted observations. Additionally, they do not explicitly account for the physical consistency and measurement uncertainty associated with the underlying sensing process. To address these limitations, this paper introduces navigation-aware loss (NAViLoss), a robust and uncertainty-aware objective function for learning-based AUV velocity estimation. NAViLoss jointly penalizes the velocity-estimation residual in the navigation-state domain and the beam-consistency residual in the DVL measurement domain. Its bounded formulation limits the influence of large residuals, while an adaptive mechanism regulates the uncertainty in beam geometry. Furthermore, NAViLoss is integrated with a DeepONet architecture to form a novel NAVi-DeepONet model for seamless estimation of an underwater vehicle's velocity. Lastly, our model is evaluated using approximately 10,000m of semi-synthetic AUV experimental data collected during multiple real-world sea trials. Experimental results demonstrate a 44% improvement in velocity-estimation accuracy compared with conventional and learning-based baselines. These results demonstrate the effectiveness of navigation-aware and uncertainty-adaptive loss design for robust learning-based underwater velocity estimation.
Arup Kumar Sahoo, Itzik Klein
The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, 3498838, Israel