پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 419 |
| تعداد مقالات | 5,514 |
| تعداد مشاهده مقاله | 6,253,177 |
| تعداد دریافت فایل اصل مقاله | 5,422,900 |
General Linear Chirplet Transform and Radar Target Classification | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 1، دوره 51، شماره 2، اسفند 2019، صفحه 113-122 اصل مقاله (2.14 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2019.15276.5257 | ||
| نویسندگان | ||
| Reza Amiri* 1؛ Ali Shahzadi2 | ||
| 1Islamic Azad University, South Tehran Branch | ||
| 2Semnan University | ||
| چکیده | ||
| In this paper, we design an attractive algorithm aiming to classify moving targets including human, animal, vehicle and drone, at ground surveillance radar systems. The non-stationary reflected signal of the targets is represented with a novel mathematical framework based on behavior of the signal components in reality. We further propose using the generalized linear chirp transform for the analysis stage. To enhance the classification performance, the rotation invariant pseudo Zernike-Moments are extracted from the time-frequency map. Consequently, the obtained features are trained to the k-NN classifier. In the numerical experiments we show the superiority of the proposed method in comparison with the existing recent counterparts, for both performance as well as the computational complexity. The results indicate that the proposed method obtains the rate of 95% accuracy in classification performance, when the signal to noise ratio is higher than 25dB. In fact, a rotating propeller on a fixed-wing aircraft, the multiple spinning rotor blades of a helicopter, or an Unmanned Aerial Vehicle (UAV); the vibrations of an engine shaking a vehicle; an antenna rotating on a ship; the flapping wings of birds; the swinging arms and legs of a walking person; and many other sources are the source of micromotion, are known as the micro-Doppler, and can be used for target classification and reduction of the sensor false alarm rate. | ||
| کلیدواژه‌ها | ||
| Automatic Target Recognition (ATR)؛ General Linear Chirplet Transform (GLCT)؛ Moving Target Detector (MTD)؛ Radar Target Classification؛ Short Time Fourier Transform (STFT) | ||
| مراجع | ||
|
[1] W. L. Melvin and J. A. Scheer, Principles of modern radar. SciTech Publishing, 2014. [2] G. Zhao, Y. Fu, L. Nie, and Z. Zhuang, “Imaging and micro Doppler analysis of vibrating target in Multi-Input-Multi Output synthetic aperture radar,” IET Radar, Sonar Navigation, vol. 9, no. 9, pp. 1360–1365, 2015. [3] X. Y. Pan, J. Liu, L. T. Xu, X. Ai, Q. Xie, B. Yu, and C. Li, “Extraction of micro-Doppler frequency from HRRPs of rotating targets,” IEEE Access, vol. 5, pp. 26162–26174, 2017. [4] L. Li, Z. Liu, and T. Li, “Radar high resolution range profile recognition via multi-SV method,” Journal of Systems Engineering and Electronics, vol. 28, pp. 879–889, Oct 2017. [5] C. Bentes, D. Velotto, and B. Tings, “Ship classification in TerraSAR-X images with convolutional neural networks,” IEEE Journal of Oceanic Engineering, vol. 43, pp. 258–266, Jan 2018. [6] M. Alaee and H. Amindavar, “Chirplet-based target recognition using RADAR technology,” in 2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 451–454, July 2008. [7] D. Tahmoush, “Review of micro-Doppler signatures,” IET Radar, Sonar Navigation, vol. 9, no. 9, pp. 1140–1146, 2015. [8] D. P. Fairchild and R. M. Narayanan, “Multistatic micro Doppler radar for determining target orientation and activity classification,” IEEE Transactions on Aerospace and Electronic Systems, vol. 52, pp. 512–521, February 2016. [9] W. Li, B. Xiong, and G. Kuang, “Target classification and recognition based on micro-doppler radar signatures,” in 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), pp. 1679–1684, Nov 2017. [10] M. Skolnik, Radar Handbook, Third Edition. Electronics electrical engineering, McGraw-Hill Education, 2008. [11] M. Alaee, H. Amindavar, and A. M. Reza, “A new approach to moving terrestrial targets recognition using ground surveillance pulse Doppler RADARs,” in 2008 IEEE Radar Conference, pp. 1–6, May 2008. [12] S. G. Pelluri and T. V. Sreenivas, “Parameter estimation of a moving acoustic source: Linear chirplet transform vs wvd,” in 2017 Twenty-third National Conference on Communications (NCC), pp. 1–6, March 2017. [13] C. Clemente, L. Pallotta, A. D. Maio, J. J. Soraghan, and A. Farina, “A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal pseudo-Zernike polynomials,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, pp. 417–430, January 2015. [14] B. Tekeli, S. Z. Gurbuz, and M. Yuksel, “Information-theoretic feature selection for human micro-Doppler signature classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 2749–2762, May 2016. [15] L. Du, L. Li, B. Wang, and J. Xiao, “Micro-Doppler feature extraction based on time-frequency spectrogram for ground moving targets classification with low-resolution radar,” IEEE Sensors Journal, vol. 16, pp. 3756–3763, May 2016. [16] A. K. Singh and Y. H. Kim, “Automatic measurement of blade length and rotation rate of drone using w-band micro-Doppler radar,” IEEE Sensors Journal, vol. 18, pp. 1895–1902, March 2018. [17] C. Clemente, L. Pallotta, D. Gaglione, A. D. Maio, and J. J. Soraghan, “Automatic target recognition of military vehicles with krawtchouk moments,” IEEE Transactions on Aerospace and Electronic Systems, vol. 53, pp. 493–500, Feb 2017. [18] G. Yu and Y. Zhou, “General linear chirplet transform,” Mechanical Systems and Signal Processing, vol. 70-71, pp. 958 – 973, 2016. [19] G. Yu and Y. Zhou, “General linear chirplet transform,” Mechanical Systems and Signal Processing, vol. 70-71, pp. 958 – 973, 2016. [20] A. Taebi and H. A. Mansy, “Analysis of seismocardiographic signals using polynomial chirplet transform and smoothed pseudo wigner-ville distribution,” in 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1–6, Dec 2017. [21] U. Singh and S. N. Singh, “Detection and classification of power quality disturbances based on time–frequency-scale transform,” IET Science, Measurement Technology, vol. 11, no. 6, pp. 802–810, 2017. [22] X. Tu, Y. Hu, F. Li, S. Abbas, and Y. Liu, “Instantaneous frequency estimation for nonlinear fm signal based on modified polynomial chirplet transform,” IEEE Transactions on Instrumentation and Measurement, vol. 66, pp. 2898–2908, Nov 2017. [23] J. Sharma, V. Thakur, and S. D. Sharma, “Evaluation of time frequency tools for multi-component chirp signals,” in International Conference on Signal Processing (ICSP 2016), pp. 1–6, Nov 2016. [24] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classifcation with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012. [25] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision, pp. 818–833, Springer, 2014. [26] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015. [27] Y. Kim and T. Moon, “Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks,” IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 8–12, Jan 2016. [28] B. Vandersmissen, N. Knudde, A. Jalalvand, I. Couckuyt, A. Bourdoux, W. D. Neve, and T. Dhaene, “Indoor person identifcation using a low-power FMCW radar,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, pp. 3941–3952, July 2018. [29] S. Deng, L. Du, C. Li, J. Ding, and H. Liu, “Sar automatic target recognition based on euclidean distance restricted autoencoder,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, pp. 3323–3333, July 2017. [30] Y. Lu, E. Oruklu, and J. Saniie, “Fast chirplet transform with fpga-based implementation,” IEEE Signal Processing Letters, vol. 15, pp. 577–580, 2008. [31] Q. Jiang and B. W. Suter, “Instantaneous frequency estimation based on synchrosqueezing wavelet transform,” Signal Processing, vol. 138, pp. 167 – 181, 2017. | ||
|
آمار تعداد مشاهده مقاله: 1,165 تعداد دریافت فایل اصل مقاله: 386 |
||