Vehicle Classification Using Empirical Mode Decomposition

DOI

10.2991/978-94-6463-858-5_289How to use a DOI?

Keywords

Empirical Mode Decomposition (EMD); Machine Learning; Traffic Monitoring; Micro-Doppler Signatures; Vehicle Classification; and Noise Reduction

Abstract

In traffic monitoring and surveillance systems that have been improved with the empirical mode decomposition (EMD) technology, this re- search suggests an effective radar-based method for vehicle classification. A type of signal known as micro-Doppler signatures can identify minute motion patterns in automobiles, trucks, motorcycles, and bicycles. However, they are so cluttered and noisy that it is hard to classify them directly. This is addressed by applying EMD to breakdown raw micro-Doppler signals into intrinsic mode functions (IMFs), which reduces noise and isolates the most informative components. The discriminative properties of the signals are improved by choosing and rebuilding the most pertinent IMFs, which gives a better depiction of the distinct motion patterns of various vehicle kinds. Machine learning classifiers like SVM and CNN are then fed these EMD-enhanced features.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. JayaLakshmi
AU  - B. Shailaja
AU  - S. Chandini Yaseen Farha
AU  - P. Haritha
AU  - C. Ahalya
PY  - 2025
DA  - 2025/11/04
TI  - Vehicle Classification Using Empirical Mode Decomposition
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3455
EP  - 3463
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_289
DO  - 10.2991/978-94-6463-858-5_289
ID  - JayaLakshmi2025
ER  -