ArticleSensors (Basel, Switzerland)2019
A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Review of Nonlinear Filtering Algorithms in Integrated Navigation Systems.Sensors (Basel, Switzerland) · 2025Review
- An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems.Sensors (Basel, Switzerland) · 2025Article
- Adaptive filter parameter reconstruction technology for rocket inertial navigation/satellite integrated navigation system.PeerJ. Computer science · 2025Article
- Ego-Vehicle Speed Correction for Automotive Radar Systems Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Cubature Kalman Hybrid Consensus Filter for Collaborative Localization of Unmanned Surface Vehicle Cluster with Random Measurement Delay.Sensors (Basel, Switzerland) · 2024Article
- Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Constrained Cubature Particle Filter for Vehicle Navigation.Sensors (Basel, Switzerland) · 2024Article
- A Robust GPS Navigation Filter Based on Maximum Correntropy Criterion with Adaptive Kernel Bandwidth.Sensors (Basel, Switzerland) · 2023Article
- A real-time prediction interval correction method with an unscented Kalman filter for settlement monitoring of a power station dam.Scientific reports · 2023Article
- Sensors and System for Vehicle Navigation.Sensors (Basel, Switzerland) · 2022Article
- Statistical Study of the Performance of Recursive Bayesian Filters with Abnormal Observations from Range Sensors.Sensors (Basel, Switzerland) · 2020Article
- Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle.Sensors (Basel, Switzerland) · 2020Article
- Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty.Sensors (Basel, Switzerland) · 2020Article
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4 authors.
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Abstract
INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations. The proposed robust CKF can effectively resist the influence of abnormal observations on navigation solution and thus improves the robustness of CKF for vehicular INS/GNSS integration. Simulation and experimental results have demonstrated the effectiveness of the proposed robust CKF for vehicular navigation with INS/GNSS integration.
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