Ground Vehicles Classification using Multi Perspective Features in FSR Micro-Sensor Network
Fairchild D. P. and Narayanan R. M. 2014. Classification of human motions using empirical mode decomposition of human micro-Doppler signatures. Radar, Sonar & Navigation, IET. 8: 425-434.
Youngwook K., Sungjae H., and Jihoon K. 2015. Human Detection Using Doppler Radar Based on Physical Characteristics of Targets. Geoscience and Remote Sensing Letters, IEEE. 12: 289-293.
Kabakchiev C., Garvanov I., Behar V., Daskalov P., and Rohling H. 2014. Study of moving target shadows using passive Forward Scatter radar systems," in Radar Symposium (IRS), 2014 15th International.1-4.
Abdullah R., Saripan M., and Cherniakov M. 2007. Neural network based for automatic vehicle classification in forward scattering radar.
Lee M. A., Aanstoos J. V., Bruce L. M., and Prasad S. 2012. Application of omni-directional texture analysis to SAR images for levee landslide detection," in Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. 1805-1808.
Qian W., Yiran L., Changzhi L., and Pal R. 2014. Gesture recognition for smart home applications using portable radar sensors. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. 6414-6417.
Davis S. K., Van Veen B. D., Hagness S. C., and Kelcz F. 2008. Breast Tumor Characterization Based on Ultrawideband Microwave
Backscatter," Biomedical Engineering, IEEE Transactions. 55: 37-246.
Nagashree R. N., Aswini N., Dyana A., and Rao C. H. S. 2014. Detection and classification of ground penetrating radar image using textrual features," in Advances in Electronics, Computers and Communications (ICAECC), 2014 International Conference.. 1-5.
Sobolewski S., Adams W. L., and Sankar R. 2012. Automatic Modulation Recognition techniques based on cyclostationary and multifractal features for distinguishing LFM, PWM and PPM waveforms used in radar systems as example of artificial intelligence implementation in test. AUTOTESTCON, 2012 IEEE, 2012. 335-340.
Nagel D. and Smith S. 2012. Creating a likelihood vector for ground moving targets in the exo-clutter region of airborne radar signals," in Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop. 49-54.
Cherniakov M., Abdullah R., Jančovič P., Salous M., and Chapursky V. 2006.Automatic ground target classification using forward scattering radar. IEE Proceedings-Radar, Sonar and Navigation, vol. 153, pp. 427-437,
Rashid N., Antoniou M., Jancovic P., Sizov V., Abdullah R., and Cherniakov M. 2008. Automatic target classification in a low
frequency FSR network. Radar Conference, 2008. EuRAD 2008. European. 68-71.
Cherniakov M., Abdullah R. R., Jancovic P., and Salous M. 2005. Forward scattering micro sensor for vehicle classification. in Radar Conference, 2005 IEEE International, 2005. 184-189.
Vespe M., Baker C. J., and Griffiths H. D. 2007. Radar target classification using multiple perspectives. Radar, Sonar & Navigation, IET. 1: 300-307.
Ibrahim N., Abdullah R. R., and Saripan M. 2009. Artificial neural network approach in radar target classification," Journal of Computer Science. 5: 23.
Abdullah N. F., Rashid N. E. A., Othman K. A., and Musirin I. 2014. Vehicles classification using Z-score and modelling neural network for forward scattering radar," in Radar Symposium (IRS), 2014 15th International. 1-4.
Jayalakshmi T. and Santhakumaran A. 2011. Statistical normalization and back propagation for classification," International Journal of Computer Theory and Engineering, 3: 1793-8201.
Najib M. S., Taib M. N., Ali N. A. M., Arip M. N. M., and Jalil A. M.,
Classification of Agarwood grades using ANN," in Electrical, Control
and Computer Engineering (INECCE), 2011 International Conference. 367-372.
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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