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Target Classification Enhancement in VHF Radar Using Support Vector Machine

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Abstract

The performance of target recognition in VHF radar is evaluated through the use of support vector machine (SVM) classifier. Automatic target recognition in low resolution radars has been an interesting research issue for many years. Indeed, some intrinsic properties of low resolution radars such as low pulse repetition frequency, narrow bandwidth and low time on target, restrict the accuracy of target recognition in such systems. Since high range resolution (HRR) techniques cannot be used in these radars, a fast Fourier transform (FFT) is applied in the pre-processing step. In this paper, it is shown that adequate use of an accurate classifier such as SVM provides acceptable results even when a simple FFT is applied. Two different multi-class SVM algorithms: one versus one (OVO) and one versus all (OVA), are analyzed by taking four conventional kernel functions into account. To properly evaluate the performance of a classifier a large enough dataset is required. Therefore, huge amount of data points are modeled using Gaussian mixture model based on the parameter vectors that are extracted from real data of helicopter, airplane and fighter aircrafts. The classifier performance is evaluated by calculating probability of correct classification, reliability factor and central processing unit calculation time for each algorithm.

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References

  • Afifi A, Zanaty EA, Ghoniemy S (2013) Improving the classification accuracy using support vector machines (SVMs) with new kernel. J Global Res C Sci 4(2):1409–1424

    Google Scholar 

  • Andral PS (2002) The equivalence of support vector machine and regularization neural networks. Neural Process Lett 145(2):97–104

    Article  Google Scholar 

  • Baker C (2011) Pulse Doppler radar target recognition using a two-stage SVM procedure. IEEE T Aerosp Electron Syst 47(2):1450–1457

    Article  Google Scholar 

  • Borden B (1999) Radar Imaging of Airborne Targets a Primer for Applied Mathematicians and Physicists. Naval air warfare center weapons division China lake, California

  • Boulay T et al (2013) High-dimensional range profile geometrical visualization and performance estimation of radar target classification via a gaussian mixture model. J Geom Sci Inf 8085:829–836

    Article  MATH  Google Scholar 

  • Chalechale A, Safaei F (2008) Visual-based interface using hand gesture recognition and object tracking. IJSTE 32(B3):279–293

    MATH  Google Scholar 

  • Chen V, Cripps SC (2011) The micro-Doppler effect in radar. Artech House, London

    Google Scholar 

  • Chongming W, Xiaodan W, Chunying Z (2008) Classifying HRRP by AdaBoostSVM. In: ICSP, Beijing, Oct 26–29, pp 1582–1585

  • Du J, Hu Y, Jiang H (2011) Boosted mixture learning of gaussian mixture hidden markov models based on maximum likelihood for speech recognition. IEEE Trans Audio Speech 19(7):2091–2100

    Article  Google Scholar 

  • Du L, Wang B, Li Y, Liu H (2013) Robust classification scheme for airplane targets with low resolution radar based on EMD-CLEAN feature extraction method. IEEE Sensor J 13(12):4648–4662

    Article  Google Scholar 

  • Duan K, Keerthi SS, Poo AN (2003) Evaluation of simple performance measures for tuning SVM hyperparameters. J Neurocomput 51:41–59

    Article  Google Scholar 

  • Eravci B, Barshan B (2012) Automatic radar antenna scan type recognition in electronic warfare. IEEE Trans Aerosp Electron Syst 48(4):2908–2931

    Article  Google Scholar 

  • Gini F, Rangaswamy M (2008) Knowledge-based radar detection tracking and classification, 1st edn. Wiley, New Jersey

    Book  Google Scholar 

  • Guo Y et al (1999) Covering numbers for support vector machines. IEEE Trans Inf Theory 48:239–250

    MathSciNet  MATH  Google Scholar 

  • Helzel T, Kniephioff M, Petersen L (2010) Oceanography radar system WERA: features, accuracy, reliability and limitations. Turk J Electr Eng Comput Sci 18(3):389–397

    Google Scholar 

  • JalaliRad A, Amindavar H, Kirlin RL (2011) Characteristic function based method for SVM classification of maneuvering over the horizon targets. In: IEEE Africon 2011 Conf., Livingstone, Sept 13–15, pp 1–4

  • Jinsha Y et al (2008) K-mean algorithm with a distance based on the characteristic of differences. In: I C WIREL COMM NETW., Dalian, Oct 12–14, pp 1–4

  • Jun W, Jinping S, Peng L (2014) Classification of free rigid targets with micro-motions using inertial characteristic from radar signatures. IEEE Electron Lett 50(13):950–952

    Article  Google Scholar 

  • Kim KT, Seo DK, Kim HT (2002) Efficient radar target recognition using the MUSIC algorithm and invariant features. IEEE T Antennas Propag 50(3):325–337

    Article  Google Scholar 

  • Kuschel H (2002) VHF/UHF radar part 2: operational aspects and applications. Electron Commun Eng 14(3):101–111

    Article  Google Scholar 

  • Kylmala J et al (2013) Search radar modification for long range ISAR target recognition. In: IEEE 14th Int. Radar. Symp., Dresden, June 19–21, pp 597–600

  • Lakshmanan V et al (2012) A statistical approach to mitigating persistent clutter in radar reflectivity data. IEEE J Sel Topics Appl Earth Obs 5(2):652–662

    Article  Google Scholar 

  • Lakshmi SL et al (2010) Doppler profile estimation in VHF radar signals using wavelets. In: ICSP, Chennai, Dec 15–17, pp 243–249

  • Li S, Wang M, He J (2013) Prediction of banking systemic risk based on support vector machine. Math Probl Eng. 2013:1052–1067

    MathSciNet  MATH  Google Scholar 

  • Lia Q, Xiea W (2013) Classification of aircraft targets with surveillance radars based on fuzzy fractal features. J Electromagn Wave 27:65–77

    Google Scholar 

  • Liu H et al (2005) Radar high range resolution profiles recognition based on wavelet packet and subband fusion. In: IEEE. Int. Conf. Acoust. Spee., Philadelphia, Mar 18–23, pp v/445–v/448

  • Maji S, Berg AC, Malik J (2013) Efficient classification for additive kernel SVMs. IEEE Trans Pattern Anal 35(1):66–77

    Article  Google Scholar 

  • Matej K, Aleˇs L (2013) Online discriminative kernel density estimator with gaussian kernels. IEEE Trans Cybern 44(3):55–365

    Google Scholar 

  • Molchanov PO et al (2013) Moving target classification in ground surveillance radar ATR System by using novel bicepstral-based information features, EURASIP. J Adv Signal Proc 2013:1–13

    Article  Google Scholar 

  • Nanzer JA, Rogers RL (2009) Bayesian classification of humans and vehicles using micro-Doppler signals from a scanning-beam radar. IEEE Microwave Wirel Compon Lett 19(5):338–340

    Article  Google Scholar 

  • Peng L et al (2011) Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features. Int J Electron Commun (AEU) 65(10):806–813

    Article  Google Scholar 

  • Sangeetha R, Kalpana B (2011) Performance evaluation of kernels in multi-class support vector machines. Int J Soft Comput Eng 1(5):138–145

    Google Scholar 

  • Setlur P, Ahmad F, Amin M (2011) Helicopter radar return analysis: estimation and blade number selection. J Signal Process 91(6):1409–1424

    Article  Google Scholar 

  • Smith GE et al (2008) Radar classification evaluation. In: IEEE Radar. Conf., Rome, May 26–30, pp 1–6

  • Strausberger DJ et al (1992) Modeling and performance of HF/OTH radar target classification systems. IEEE T Aerosp Electron Syst 28(2):396–403

    Article  Google Scholar 

  • Tait P (2008) Introduction to radar target recognition (Radar, Sonar and Navigation). Institution of engineering and technology, London

  • Wang X, Paliwal KK (2003) Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. J Pattern Recognit 36(10):2429–2439

    Article  MATH  Google Scholar 

  • Wei C et al (2012) An approach for high resolution radar target recognition based on BP neural network. Lect Notes Comput Sci 6838:33–39

    Google Scholar 

  • Woodbridge K, Baker CJ, Smith GE (2008) Naïve Bayesian radar micro-Doppler recognition. In: IEEE Radar. Conf., Adelaide, Sept 2–5, pp 111–116

  • Youngwook K, Sungjae H, Jihoon K (2015) Human detection using Doppler radar based on physical characteristics of targets. IEEE Geosci Remote Sens Lett 12(2):1831–1834

    Google Scholar 

  • Yunfei S et al (2011) Landmine detection using boosting classifiers with adaptive feature selection. In: IWAGPR, Aachen, Jun 22–24, pp 1–5

  • Zyweck A (1995) Preprocessing issues in high resolution radar target classification, PhD Thesis, University of Adelaide, Advanced Institute of Science and Technology, Australia

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Authors and Affiliations

  1. Department of Electrical Engineering, Fars Science and Research Branch, Islamic Azad University, Shiraz, Iran

    Zeynab Khodkar

  2. Electronic and Communication Department, Imam Hossein Comprehensive University, Tehran, Iran

    Seyed Mohammad Alavi

  3. Islamic Azad University of Shiraz, Pardis Site, Kilometer 5 of Sadra Road, Shiraz, Iran

    Zeynab Khodkar

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  1. Zeynab Khodkar
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  2. Seyed Mohammad Alavi
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Correspondence to Zeynab Khodkar.

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Khodkar, Z., Alavi, S.M. Target Classification Enhancement in VHF Radar Using Support Vector Machine. Iran. J. Sci. Technol. Trans. Electr. Eng. 40, 51–62 (2016). https://doi.org/10.1007/s40998-016-0004-2

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  • DOI: https://doi.org/10.1007/s40998-016-0004-2

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