ArticleSensors (Basel, Switzerland)2020
Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Long-Term Assessment of the Properties of Load Sensors Applied in Weigh-in-Motion Systems.Sensors (Basel, Switzerland) · 2025Article
- Weigh-in-Motion Site for Type Approval of Vehicle Mass Enforcement Systems in Poland.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion.Sensors (Basel, Switzerland) · 2023Article
- Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present the results of a comparison of two estimators of the gross vehicle weight (GVW) and the static load of individual axles of vehicles. The estimators were used to process measurement data derived from Multi-Sensor Weigh-In-Motion systems (MS-WIM). The term estimator is understood as an algorithm according to which the dynamic axle load measurement results are processed in order to determine the static load. The result obtained is called static load estimate. As a measure of measurement uncertainty, we adopted the standard deviation of the static load estimate. The mean value and the maximum likelihood estimators were compared. Studies were conducted using simulation methods based on synthetic data and experimental data obtained from a WIM system equipped with 16 lines of polymer axle load sensors. We have shown a substantially lower uncertainty of estimates determined using the maximum likelihood estimator. The results obtained have considerable practical significance, particularly during long-term usage of multi-sensor WIM systems.
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