Automatic Target Classification
We are working on automatic target recognition problem for ground surveillance radar. The goal of this research is to develop an automatic target classifier that can classify ground targets. The work is based on extracting the micro-Doppler features from the backscattered signal using signal processing and then classifying them using machine learning techniques.
In addition, we are developing high resolution 2D time-frequency representation of pedestrian micro-Doppler signature.
This work has resulted in a number of publications :
- A. Javed, S. Liaqat, M. B. Ihsan, “Support vector machine based micro-Doppler signature classification of ground targets”, Radar Conference (EuRAD), 2013, 10th European, Oct. 9 2013 – Oct. 11 2013, pp. 515-518.
- A. Javed, A. Ejaz, S. Liaqat, A. Ashraf, M. B. Ihsan, "Automatic target classifier for a ground surveillance radar using linear discriminant analysis and logistic regression", in Radar Conference (EuRAD), 2012 9th European, Oct 31 2012-Nov 2, 2012, pp 302-305.
- S. Liaqat, S. A. Khan, A. I. Bhatti, M. B. Ihsan, S. Z. Asghar, A, Ejaz, "Automatic recognition of ground radar targets based on target RCS and short time spectrum variance", in Innovations in Intelligent Systems and Applications 2011 (INISTA 2011), International Symposium on, pp 164-167, Jun 2011.
Joint time-frequency Representation:
- S. Liaqat , M. B. Ihsan, S. Z. Asghar , A. Ejaz, A. Javed, “́High resolution 2D time frequency representation of radar micro-Doppler pedestrian signal”, Radar Conference (EuRAD), 2013, 10th European, Oct. 9 2013-Oct. 11 2013, pp 168 - 171.