An automatic radar based aerial target recognition framework

References

• Sommer, H., and Salerno, J. Radar target identification system. U.S. Patent 3, pp. 614–779 (1971).
   Google Scholar
• Jacobs, S., & O’Sullivan, J,Automatic target recognition using sequences of high resolution radar range-profiles, IEEE Transactions on Aerospace and Electronic Systems, 36, 2 , pp. 364–381(2000). doi: 10.1109/7.845214
   Web of Science ®Google Scholar
• Van Der Heiden, R., & Groen, F, The box-cox metric for nearest neighbour classification improvement,Pattern Recognition, Vol. 30, pp. 273– 279(1997). doi: 10.1016/S0031-3203(96)00077-5
   Web of Science ®Google Scholar
• Zwart, J., Van Der Heiden, R., Gelsema, S., and Groen, F. “Fast translation invariant classification of HRR range profiles in a zero phase representation” IEE Proceedings - Radar, Sonar and Navigation, Vol. 150, pp. 411–418(2003). doi: 10.1049/ip-rsn:20030428
   Google Scholar
• Smith, C., & Goggans, P, Radar target identification, IEEE Antennas and Propagation Magazine, Vol. 35, pp. 27–38(1993). doi: 10.1109/74.207649
   Google Scholar
• Zhou, D., Liu, G., & Wang, J, Spatio-temporal target identification method of high-range resolution radar, Pattern Recognition, Vol. 33, pp.1–7(2000). doi: 10.1016/S0031-3203(98)00052-1
   Web of Science ®Google Scholar
• V. C. Chen & H. Wechsler, Micro-Doppler effect in radar: phenomenon, model, and simulation study, IEEE Trans. Aerosp. Electron. Syst., Vol. 42, pp. 2–21(2006). doi: 10.1109/TAES.2006.1603402
   Web of Science ®Google Scholar
• T. Thayaparan, S Abrol, & E Riseborough, Analysis of radar micro-Doppler signatures from experimental helicopter and human data, IET Radar Sonar Navig, Vol. 1(4), pp. 289–299(2007). doi: 10.1049/iet-rsn:20060103
   Web of Science ®Google Scholar
• Van Der Heiden, R. Aircraft recognition with radar range profiles. Ph.D. thesis, University of Amsterdam, (1998).
   Google Scholar
• Chen, V.C., & Ling, H, Time-frequency transform for radar imaging and signal analysis, Boston: Artech House(2002).
   Google Scholar
• P. Lei, K. Li, & Y. Liu, Feature extraction and target recognition of missile targets based on micro-motion, IEEE 11th Int. Conf. Signal Process, pp. 1914–1919(2012).
   Google Scholar
• X. Liao, P. Runkle, & L. Carin, Identification of ground targets from sequential high-range-resolution radar signatures, IEEE Trans. Aerosp. Electron. Syst., Vol. 38, pp. 1230–1242 (2002). doi: 10.1109/TAES.2002.1145746
   Web of Science ®Google Scholar
• A. K. Singh & Y. H. Kim, Analysis of human kinetics using millimeter-wave micro-Doppler radar, Proc. Comput. Sci., Vol. 84, pp. 36–40 (2016). doi: 10.1016/j.procs.2016.04.063
   Google Scholar
• Hamed Haghshenas & Mohammad M. Nayebi, A novel method to detect rotor blades echo, IEEE Radar Conference,pp. 1331–1334(2010).
   Google Scholar
• Tait, P., Introduction to Radar Target Recognition, London:IEE (2005).
   Google Scholar
• W. Yu & J. Wang, Phase adjustment for extraction of micro-motion information of ballistic targets, 5th Int. Congr. Image Signal Process., No. Cisp, pp. 1837–1840 (2012).
   Google Scholar
• Chen, V. C.,Radar signatures of rotor blades, Proceedings of SPIE on Wavelet Applications VIII, Vol. 4391, pp. 63–70(2001). doi: 10.1117/12.421231
   Google Scholar
• Jiaojiao Wu, Lei Zuo, & Ming Li, Micro-Doppler of helicopter with different blade shapes, Electronics Letters,Vol.54,pp. 1053-1054(2018). doi: 10.1049/el.2018.5390
   Web of Science ®Google Scholar
• Melino, R., Kodituwakku, S. & Tran, H.-T, Orthogonal matching pursuit and matched filter techniques for the imaging of rotating blades, IEEE Radar Conference (2015).
   Google Scholar
• Singh, A.K & Kim, Y.H, Automatic measurement of blade length and rotation rate of drone using w-band micro-doppler radar, IEEE Sensor Journal, vol. 18, pp. 1895-1902(2018). doi: 10.1109/JSEN.2017.2785335
   Web of Science ®Google Scholar
• C. Clemente, and J. J. Soraghan. Passive Bistatic Radar for helicopters classification: A feasibility study, Proceedings of IEEE Radar Conf, Atlanta (2012).
   Google Scholar
• S. L. Marple, Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data,Sixth Int. Symp. Signal Process. its Appl., Vol. 1, pp. 260-263(2001).
   Google Scholar
• C. Rotander, & H.H von Sydow, Classification of helicopters by the L/N quotient, Proceedings of IEEE 1997 Radar Conf., (1997).
   Google Scholar
• S. Yoon, B. Kim, & Y. Kim, Helicopter classification using time-frequency analysis, Electronics Letters, Vol. 36, pp. 1871–1872(2000). doi: 10.1049/el:20001306
   Web of Science ®Google Scholar
• J. J. M. de Wit, P. van Dorp, & A. G. Huizing, Classification of air targets based on range Doppler diagrams, Proc. of European Radar Conference, pp. 89–92, (2016).
   Google Scholar
• S. L. Phung, F. H. C. Tivive, & A. Bouzerdoum. Classification of micro-Doppler signatures of human motions using log-Gabor filters, IET Radar, Sonar Navig., Vol. 9, pp. 1188–1195(2015). doi: 10.1049/iet-rsn.2015.0113
   Web of Science ®Google Scholar
• R. M. Narayanan & D. P. Fairchild. Classification of human motions using empirical mode decomposition of human micro-doppler signatures, IET Radar, Sonar Navig., Vol. 8,pp. 425–434, (2014). doi: 10.1049/iet-rsn.2013.0165
   Web of Science ®Google Scholar
• Javier Martinez & Martin Vossiek, Deep Learning-Based Segmentation for the Extraction of Micro-Doppler Signatures Proceedings of the 15th European Radar Conference, Spain(2018).
   Google Scholar
• V. H. A. Ribeiro, G. R. Meza & L. d. S. Coelho, Comparison of different classifiers for automatic target recognition systems, IEEE Latin America Transactions, Vol. 16 (2018). doi: 10.1109/TLA.2018.8795116
   Web of Science ®Google Scholar
• Basanta K. Panigrahi, Prakash K. Ray, Pravat K. Rout & Kumaresh Pal, Detection and classification of faults in a microgrid using wavelet neural network, Journal of Information and Optimization Sciences, 39:1, 327-335, (2018) DOI: 10.1080/02522667.2017.1374738
   Web of Science ®Google Scholar
• Sunil Singh, D. Vishwakarma, Amit & Shashank, A novel methodology for fault detection, classification and location in transmission system based on DWT & ANFIS, JIOS, 38:6, 791-801, (2017) DOI: 10.1080/02522667.2017.1372129
   Google Scholar
• Munish Sabharwal. Contemporary research: intricacies and aiding software tools based on expected characteristics, AIMA Journal for Management & Research, Vol. 10, Issue 2/4, pp. 1-16, (2016).
   Google Scholar
• Munish Sabharwal, The use of soft computing technique of decision tree in selection of appropriate statistical test for hypothesis testing, SoCTA, Amity University, Jaipur (2016).
   Google Scholar
• Agnihotri Vikas, Sabharawal Munish and Goyal, Vinay, Effect of transmitted frequency on Micro-Doppler Signatures of Helicopter,Proceeding of icABCD 2019, Durban(2019).
   Google Scholar
• Agnihotri Vikas, Sabharwal Munish and Goyal, Vinay , “The Extraction of key Distinct Features for Identification and Classification of Helicopters using Micro-Doppler Signatures”., Proceeding 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/ PiCom/CBDCom/CyberSciTech), Fukuoka, Japan., 2019.
   Google Scholar