ArticleSensors (Basel, Switzerland)2023
Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 46 citations in OpenAlex.
- Robust Post-Processing for Marine GNSS/INS Integration: An Adaptive RTS Smoothing Approach via Huber M-Estimation.Sensors (Basel, Switzerland) · 2026Article
- Motion-Induced Errors in Buoy-Based Wind Measurements: Mechanisms, Compensation Methods, and Future Perspectives for Offshore Applications.Sensors (Basel, Switzerland) · 2026Review
- Forecasting extremes of football players' performance in matches.Scientific reports · 2024Article
- Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV).Sensors (Basel, Switzerland) · 2024Article
- Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments.Sensors (Basel, Switzerland) · 2024Article
- Vehicle State Estimation Combining Physics-Informed Neural Network and Unscented Kalman Filtering on Manifolds.Sensors (Basel, Switzerland) · 2023Article
- A Multi-Global Navigation Satellite System (GNSS) Time Transfer Method with Federated Kalman Filter (FKF).Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
High-precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch-Tung-Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF-RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF-RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF-RTS algorithm is more robust and provides more accurate localization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.