Ground Vehicles Classification using Multi Perspective Features in FSR Micro-Sensor Network

Authors

  • Nur Fadhilah Abdullah Applied Electromagnetic Research Group (AERG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia. Power System operation and Computational Intelligence Research Group (PSOCIRG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia.
  • Nur Emileen Abd Rashid Applied Electromagnetic Research Group (AERG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia. Power System operation and Computational Intelligence Research Group (PSOCIRG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia.
  • Kama Azura Othman Applied Electromagnetic Research Group (AERG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia. Power System operation and Computational Intelligence Research Group (PSOCIRG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia.
  • Zuhani Ismail Khan Applied Electromagnetic Research Group (AERG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia. Power System operation and Computational Intelligence Research Group (PSOCIRG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia.
  • Ismail Musirin Applied Electromagnetic Research Group (AERG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia. Power System operation and Computational Intelligence Research Group (PSOCIRG), Advanced Computing and Communication Communities Research, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia.

Keywords:

Neural Network, Principal Component Analysis, Feature Extraction, Forward Scattering Radar, Classification Accuracy,

Abstract

Automatic target classification (ATC) is examined from the viewpoint of improving classification accuracy. The challenge of automatic target classification is the selection of feature extraction (FE) technique, types of features and the type of classifier use. In this paper, the combination of Z-score and neural network (NN) is applied in order to perform the classification process for a ground target. The Z-score is used as a feature extractor where it will extract the significant data contain in the target’s signal and NN acts as a classifier to classify the targets based on their size. Different types of features are used in order to optimize the system performance. Results obtained demonstrate the improvement of classification performance when high number of features in the classification is used.

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Abdullah N. F., Rashid N., Othman K. A., and Musirin I., Vehicles

Classification using Z-score and Modelling Neural Network for

Forward Scattering Radar.

Najib M., Ali N. M., Arip M. M., Jalil M. A., and Taib M.,

Classification of Agarwood using ANN.

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Published

2017-04-01

How to Cite

Abdullah, N. F., Abd Rashid, N. E., Othman, K. A., Ismail Khan, Z., & Musirin, I. (2017). Ground Vehicles Classification using Multi Perspective Features in FSR Micro-Sensor Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 49–52. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1833

Issue

Vol. 9 No. 1-5: Smart City Technology in Communication and Computer Engineering III

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Articles

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