Unscented Kalman Filter with a Nonlinear Propagation Model for Navigation Applications

2025-07-14

AI Summary

Standard unscented Kalman filtering (UKF) suffers from modeling inaccuracy and degraded estimation accuracy/stability in navigation systems due to linear propagation of sigma points, which fails to capture strong nonlinearities. To address this, we propose an improved UKF based on a nonlinear error-state propagation model. Our key innovation lies in replacing the conventional linearized sigma-point propagation under the nominal dynamic model with direct evolution of sigma points governed by high-fidelity nonlinear differential equations of the navigation error state—enabling more accurate mean and covariance prediction. The method integrates the unscented transform with multi-sensor measurements from autonomous underwater vehicles (AUVs) and is implemented and validated in realistic oceanic environments. Experimental results demonstrate significant improvements over both standard UKF and extended Kalman filter (EKF) in positioning accuracy, attitude estimation precision, and filter convergence rate—particularly under highly nonlinear, aggressive maneuvering conditions, where robustness and stability are markedly enhanced.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The unscented Kalman filter is a nonlinear estimation algorithm commonly used in navigation applications. The prediction of the mean and covariance matrix is crucial to the stable behavior of the filter. This prediction is done by propagating the sigma points according to the dynamic model at hand. In this paper, we introduce an innovative method to propagate the sigma points according to the nonlinear dynamic model of the navigation error state vector. This improves the filter accuracy and navigation performance. We demonstrate the benefits of our proposed approach using real sensor data recorded by an autonomous underwater vehicle during several scenarios.

Problem

• Improving unscented Kalman filter accuracy for navigation
• Enhancing nonlinear dynamic model propagation in navigation
• Validating method with autonomous underwater vehicle data

Innovation

• Unscented Kalman Filter for nonlinear navigation
• Nonlinear propagation of sigma points
• Improved accuracy with dynamic error model