Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors
Accurate time period estimation (TPE) between sensor signals is essential for vehicle speed measurement in intelligent transportation systems (ITSs). In this context, we focus on time period estimation using signals acquired from a dual pyroelectric infrared (PIR) sensor setup. To estimate the time...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6255 |
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| author | Bui Hai Dang Vu Toan Thang Vu Van Quang |
| author_facet | Bui Hai Dang Vu Toan Thang Vu Van Quang |
| author_sort | Bui Hai Dang |
| collection | DOAJ |
| description | Accurate time period estimation (TPE) between sensor signals is essential for vehicle speed measurement in intelligent transportation systems (ITSs). In this context, we focus on time period estimation using signals acquired from a dual pyroelectric infrared (PIR) sensor setup. To estimate the time period between these signals, this paper analyzes and compares two correlation-based methods—conventional cross-correlation (CCF) and Hilbert transform-enhanced cross-correlation (CCFHT). An analytical framework is developed to quantify the bias and variance of each method under practical conditions, including sensor mismatch and noise. The PIR sensor signals are modeled based on their dynamic response characteristics, enabling theoretical analysis supported by simulations and field experiments. Results show that although both methods yield negligible bias under ideal conditions, CCFHT significantly reduces estimation variance in noisy or mismatched scenarios. These findings confirm the advantages of CCFHT for achieving robust and precise vehicle speed estimation using low-cost PIR sensor systems, and provide insights into practical deployment within ITSs. |
| format | Article |
| id | doaj-art-46ea4793fc1641cfa4d65fc7dbedc2c8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-46ea4793fc1641cfa4d65fc7dbedc2c82025-08-20T03:46:38ZengMDPI AGApplied Sciences2076-34172025-06-011511625510.3390/app15116255Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared SensorsBui Hai Dang0Vu Toan Thang1Vu Van Quang2Precision Engineering & Smart Measurement Lab, School of Mechanical Engineering, Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Hanoi 100000, VietnamPrecision Engineering & Smart Measurement Lab, School of Mechanical Engineering, Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Hanoi 100000, VietnamPrecision Engineering & Smart Measurement Lab, School of Mechanical Engineering, Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Hanoi 100000, VietnamAccurate time period estimation (TPE) between sensor signals is essential for vehicle speed measurement in intelligent transportation systems (ITSs). In this context, we focus on time period estimation using signals acquired from a dual pyroelectric infrared (PIR) sensor setup. To estimate the time period between these signals, this paper analyzes and compares two correlation-based methods—conventional cross-correlation (CCF) and Hilbert transform-enhanced cross-correlation (CCFHT). An analytical framework is developed to quantify the bias and variance of each method under practical conditions, including sensor mismatch and noise. The PIR sensor signals are modeled based on their dynamic response characteristics, enabling theoretical analysis supported by simulations and field experiments. Results show that although both methods yield negligible bias under ideal conditions, CCFHT significantly reduces estimation variance in noisy or mismatched scenarios. These findings confirm the advantages of CCFHT for achieving robust and precise vehicle speed estimation using low-cost PIR sensor systems, and provide insights into practical deployment within ITSs.https://www.mdpi.com/2076-3417/15/11/6255time period estimationpyroelectric infrared sensorvehicle speed measurementcross-correlation functionHilbert transformbias and variance analysis |
| spellingShingle | Bui Hai Dang Vu Toan Thang Vu Van Quang Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors Applied Sciences time period estimation pyroelectric infrared sensor vehicle speed measurement cross-correlation function Hilbert transform bias and variance analysis |
| title | Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors |
| title_full | Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors |
| title_fullStr | Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors |
| title_full_unstemmed | Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors |
| title_short | Evaluation of Correlation-Based Methods for Time Period Estimation in Vehicle Speed Measurement Using Pyroelectric Infrared Sensors |
| title_sort | evaluation of correlation based methods for time period estimation in vehicle speed measurement using pyroelectric infrared sensors |
| topic | time period estimation pyroelectric infrared sensor vehicle speed measurement cross-correlation function Hilbert transform bias and variance analysis |
| url | https://www.mdpi.com/2076-3417/15/11/6255 |
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