ArticleSensors (Basel, Switzerland)2020
Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- An Extended Kalman Filter with Remainder Terms and Correlation Compensation for Nonlinear State Monitoring and Soft Sensing.Sensors (Basel, Switzerland) · 2026Article
- Research on Object Detection and Tracking Methods for aLow-Speed Mobile Platform.Sensors (Basel, Switzerland) · 2025Article
- Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR.Sensors (Basel, Switzerland) · 2025Article
- A Novel Particle Filter Based on One-Step Smoothing for Nonlinear Systems with Random One-Step Delay and Missing Measurements.Sensors (Basel, Switzerland) · 2025Article
- Object Detection, Recognition, and Tracking Algorithms for ADASs-A Study on Recent Trends.Sensors (Basel, Switzerland) · 2023Review
- Probabilistic Dual-Space Fusion for Real-Time Human-Robot Interaction.Biomimetics (Basel, Switzerland) · 2023Article
- Characterization of the iPhone LiDAR-Based Sensing System for Vibration Measurement and Modal Analysis.Sensors (Basel, Switzerland) · 2023Article
- Trust Model Concept for IoT Blockchain Applications as Part of the Digital Transformation of Metrology.Sensors (Basel, Switzerland) · 2022Article
- Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform.Sensors (Basel, Switzerland) · 2022Article
- Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework.Sensors (Basel, Switzerland) · 2022Article
- Sensors and Sensor's Fusion in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2021Article
- Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation.Sensors (Basel, Switzerland) · 2021Article
- Three State Estimation Fusion Methods Based on the Characteristic Function Filtering.Sensors (Basel, Switzerland) · 2021Article
- Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data.Sensors (Basel, Switzerland) · 2020Article
- kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation.Sensors (Basel, Switzerland) · 2020Article
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2 authors.
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Abstract
Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study's method produced accurate distance estimations.
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