Inductive Electromagnetic Signature Analysis for Vehicle Characterization in Embedded Weigh-In-Motion Applications
Langgeng Asmoro(1,3,*) , siti shofiah(3), Mitra Djamal(1) , Rudy Hermawan Karsaman(2), Maria Evita (1)

1 Electronics and Instrumentations Research Group, Faculty of Mathematics and Science, Institut Teknologi Bandung, Bandung 40132, Indonesia
2 Transport Engineering Research Group, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung 40132, Indonesia
3 Departement of Automotive Engineering Technology, Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia
*langgeng.as[at]gmail.com

Abstract

This study presents an embedded Weigh-In-Motion (WIM) framework based on inductive electromagnetic signature analysis for dynamic vehicle characterization under real traffic conditions. Unlike conventional inductive loop systems that are primarily used for vehicle presence detection, the proposed method utilizes temporal inductive waveforms generated by moving vehicles as electromagnetic signatures for vehicle profiling and validation. An embedded sensing platform consisting of a rectangular inductive loop sensor, LDC1612 inductance acquisition module, and ESP32-based real-time processing unit was developed to capture high-resolution vehicle magnetic profiles during vehicle motion.
Experimental data were collected from multiple vehicle categories, including motorcycles, passenger cars, pickup vehicles, and medium trucks, under various motion conditions. The acquired waveform dataset revealed distinct signature characteristics for different vehicle types in terms of amplitude variation, pulse duration, peak distribution, and signal transition patterns. Signal preprocessing and feature extraction were performed in both time and frequency domains using moving-average filtering and Discrete Fourier Transform (DFT)-based analysis to improve waveform stability and feature consistency.
The experimental results demonstrate that each vehicle category produces distinguishable electromagnetic signature patterns influenced by axle configuration, metallic body structure, vehicle dimensions, and dynamic motion behavior. Preliminary classification analysis showed that inductive signature features can effectively support vehicle characterization and anti-false-positive validation in embedded WIM applications. The proposed system provides a low-cost and lightweight alternative for intelligent roadside vehicle monitoring while extending the functionality of inductive loop sensors beyond conventional triggering mechanisms. This study establishes inductive electromagnetic signature profiling as a promising approach for next-generation intelligent transportation and embedded dynamic weighing systems.

Keywords: Inductive loop sensor- electromagnetic signature- vehicle characterization- embedded WIM- vehicle magnetic profile- intelligent transportation systems- signal processing- vehicle classification

Topic: Instrumentation and Computational Physics