Simple Search:

FSR vehicles classification system based on hybrid neural network with different data extraction methods


Citation

Abdullah, Nur Fadhilah and Abdul Rashid, Nur Emileen and Ibrahim, Idnin Pasya and Raja Abdullah, Raja Syamsul Azmir (2017) FSR vehicles classification system based on hybrid neural network with different data extraction methods. In: 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 23-24 Oct. 2017, Jakarta, Indonesia. (pp. 21-25).

Abstract / Synopsis

This paper evaluates the performance of Forward Scatter Radar classification system using as so called “hybrid FSR classification techniques” based on three different data extraction methods which are manual, Principal Component Analysis (PCA) and z-score. By combining these data extraction methods with neural network, this FSR hybrid classification system should be able to classify vehicles into their category: small, medium and large vehicles. Vehicle signals for four different types of cars were collected for three different frequencies: 64 MHz, 151 MHz and 434 MHz. Data from the vehicle signal is extracted using above mentioned method and feed as the input to Neural Network. The performance of each method is evaluated by calculating the classification accuracy. The results suggest that the combination of z-score and neural network give the best classification performance compares to manual and PCA methods.


Download File

[img]
Preview
PDF (Abstract)
FSR vehicles classification system based on hybrid neural network with different data extraction methods.pdf

Download (50kB) | Preview

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1109/ICRAMET.2017.8253138
Publisher: IEEE
Keywords: Z-score; Principal component analysis (PCA); Forward scattering radar (FSR); Neural network (NN); Data extraction; Classification
Depositing User: Nabilah Mustapa
Date Deposited: 08 Mar 2018 00:29
Last Modified: 08 Mar 2018 00:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICRAMET.2017.8253138
URI: http://psasir.upm.edu.my/id/eprint/59509
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item