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UWB radar recognition system based on HOS and SVMs

UWB radar recognition system based on HOS and SVMs

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This study proposes an original ultra-wideband short-range radar (UWB-SRR) recognition system based on higher-order statistics (HOS) and support vector machines (SVMs). The main purpose of this work is to improve the road safety by implementing these techniques for detection and recognition of the uncovered road users such as pedestrians and cyclists. The combination of HOS and cell-averaging constant false alarm rate (CA-CFAR) radar detector has been proposed and investigated. The results show that a combination of HOS and CA-CFAR promises a good performance for UWB radar detector. The authors have also evaluated the performance of SVM-based target recognition system using normalised radar signature as input features. A total of 1000 signatures have been extracted for each class including pedestrian, cyclist, and car, where 50% of them have been used for the training data and the rest for the validation data. The results show that the SVM gives a good performance for the proposed system, where the recognition rates are up to 96.23, 95.25 and 97.23% for the cyclist, pedestrian and car. In the real testing performance using their scenarios, the system has successfully identified 92.77% of the right cyclist, 90.82% of the right pedestrian and 90.73% of the right car.

References

    1. 1)
      • 1. Niewoehner, W., Alexander Berg, F.: ‘Endangerment of pedestrians and bicyclists at intersections by right turning trucksProceedings – 19th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Washington, DC, USA, June 2005, pp. 5344.
    2. 2)
      • 2. Farooq, J.: ‘Object detection and identification using SURF and BoW model’. Int. Conf. Computing, Electronic and Electrical Engineering (ICE Cube), Buttems, Quetta, Pakistan, April 2016, pp. 318323.
    3. 3)
      • 3. Hamidoun, K., Elassali, R., Elhillali, Y., et al: ‘A new multi-user ultra wide band system based on modified Gegenbauer functions and M-OAM modulation for communication of intelligent transportation systems’, Wirel. Pers. Commun., 2015, 82, (4), pp. 21152134.
    4. 4)
      • 4. Sakkila, L., Elhillali, Y., Zaidouni, J., et al: ‘High order statistic receiver applied to UWB radar’. IEEE Pacific Rim Conf. Communications, Computers and Signal Processing PacRim 2009, Victoria, BC, August 2009, pp. 643647.
    5. 5)
      • 5. Damien, S.: ‘OFCOM infomailing no. 8’ (Federal Office of Communication, Switzerland, 2007), pp. 1115.
    6. 6)
      • 6. Sakkila, L., Rivenq, A., Boukour, F., et al: ‘Collision avoidance radar system using UWB waveforms signature for road applications’. 2009 Ninth Int. Conf. Intelligent Transport Systems Telecommunications (ITST), Lille, France, 2009, pp. 223226.
    7. 7)
      • 7. Qasim, S.M., Khan, A.A., Alshebeili, S., et al: ‘FPGA based architecture for the computation of fourth-order cross moments’. 2007 Int. Conf. Intelligent and Advanced Systems, Kuala Lumpur, 2007, pp. 14001403.
    8. 8)
      • 8. Mendel, J.M.: ‘Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications’, Proc. IEEE, 1991, 79, (3), pp. 278305.
    9. 9)
      • 9. Sanullah, M.: ‘A review of higher order statistics and spectra in communication systems’, Global J. Sci. Front. Res. Phys. Space Sci., 2013, 13, (4), available at: https://journalofscience.org/index.php/GJSFR/article/view/807 Accessed July 2017.
    10. 10)
      • 10. Tugnait, J.K.: ‘Time delay estimation with unknown spatialy correlated Gaussian noise’, IEEE Trans. Signal Process., 1993, 42, (2), pp. 549558.
    11. 11)
      • 11. Byun, H., Seong-Whan Lee, S-W.: ‘Applications of support vector machines for pattern recognition: a survey’. SVM 2002, Berlin, Germany, 2002 (LNCS 2388), pp. 213236.
    12. 12)
      • 12. Hsu, C.-W., Chang, C.-C., Lin, C.-J.: ‘A practical guide to support vector classification’ (Department of Computer Science National Taiwan University, Taipei 106, Taiwan).
    13. 13)
      • 13. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’. ACM Transactions on Intelligent Systems and Technology, 2011, 2, (3), pp. 2:27:127:27. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm, accessed February 2017.
    14. 14)
      • 14. UMAIN.Inc: ‘User's manual HST-D3 evaluation kit with Raspberry Pi 3 development version. Doc no. HST-D3_Manual_V1.9.3_ENG’, 2017. Manual available at http://www.umain.co.kr, accessed December 2017.
    15. 15)
      • 15. Ewell, G.W.: ‘Design of digital moving target indication radar processors’. PhD thesis, Georgia Institute of Technology, April 1974.
    16. 16)
      • 16. Meikle, H.: ‘Modern radar systems’ (Artech House, 2008).
    17. 17)
      • 17. Galushko, V.G.: ‘Analysis of the CA-CFAR algorithm as applied to detection of stationary Gaussian signals against a normal noise background’. 2016 Ninth Int. Kharkiv Symp. Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW), Kharkiv, 2016, pp. 13.
    18. 18)
      • 18. Richards, M.A.: ‘Fundamentals of radar signal processing’ (McGraw-Hill, New York, 2005).
    19. 19)
      • 19. Constant false-alarm rate (CFAR) detectors’. Available at https://fr.mathworks.com/help/phased/examples/constant-false-alarm-rate-cfar-detection.html, accessed June 2018.
    20. 20)
      • 20. Liu, J., Fang, N., Xie, Y.J., et al: ‘Radar target classification using support vector machine and subspace methods’, IET Radar Sonar Navig., 2015, 9, (6), pp. 632640.
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