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access icon free Radar cross-sections of pedestrians at automotive radar frequencies using ray tracing and point scatterer modelling

© The Institution of Engineering and Technology
Received 03/10/2019, Accepted 07/02/2020, Revised 28/12/2019, Published 18/02/2020

Simulation of radar cross-sections of pedestrians at automotive radar frequencies forms a key tool for software verification test beds for advanced driver assistance systems. Two commonly used simulation methods are the computationally simple scattering centre model of dynamic humans and the shooting and bouncing ray technique based on geometric optics. The latter technique is more accurate but computationally complex. Hence, it is usually used only for modelling scattered returns of still human poses. In this work, the authors combine the two methods in a linear regression framework to accurately estimate the scattering coefficients or reflectivities of point scatterers in a realistic automotive radar signal model which they subsequently use to simulate range-time, Doppler-time and range-Doppler radar signatures. The simulated signatures show a normalised mean square error <10% and a structural similarity >81% with respect to measurement results generated with an automotive radar at 77 GHz.

Inspec keywords: radar cross-sections; radar target recognition; driver information systems; regression analysis; Doppler radar; electromagnetic wave scattering; road vehicle radar; radar imaging; ray tracing; radar signal processing; mean square error methods

Other keywords: simulated signatures; reflectivities; bouncing ray technique; advanced driver assistance systems; realistic automotive radar signal model; scattered returns; computationally simple scattering centre model; scattering coefficients; range-Doppler radar signatures; point scatterer; software verification test beds; frequency 77.0 GHz; automotive radar frequencies; shooting; point scatterers

Subjects: Automobile electronics and electrics; Interpolation and function approximation (numerical analysis); Radar equipment, systems and applications; Traffic engineering computing; Signal processing and detection

References

    1. 1)
      • 23. Erol, B., Gurbuz, S.Z.: ‘A kinect-based human micro-Doppler simulator’, IEEE Aerosp. Electron. Syst. Mag., 2015, 30, (5), pp. 6–17.
    2. 2)
      • 20. Boulic, R., Thalmann, N.M., Thalmann, D.: ‘A global human walking model with real-time kinematic personification’, Vis. Comput., 1990, 6, (6), pp. 344–358.
    3. 3)
      • 24. Software, S.M. (Ed.) ‘Poser’, Smith Micro Software, 2019. Available at http://my.smithmicro.com/poser-3d-animation-software.html.
    4. 4)
      • 38. INRAS (Ed.): ‘Radarlog product description’, INRAS. Available at http://www.inras.at/en/products/new-radarlog.html.
    5. 5)
      • 5. Severino, J.V.B., Zimmer, A., dos Santos Coelho, L., et al: ‘Radar based pedestrian detection using support vector machine and the micro Doppler effect’. European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 2018.
    6. 6)
      • 35. Chen, M., Chen, C.C., Chien, S.Y.P., et al: ‘Artificial skin for 76–77 GHz radar mannequins’, IEEE Trans. Antennas Propag., 2014, 62, (11), pp. 5671–5679.
    7. 7)
      • 2. NHTSA: ‘Traffic safety facts 2017 data: pedestrians’. Available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/81268, retrieved March 2019.
    8. 8)
      • 36. Williams, G.: ‘Overdetermined systems of linear equations’, Am. Math. Mon., 1990, 97, (6), pp. 511–513.
    9. 9)
      • 46. Fujimoto, A., Iwata, K.: ‘Accelerated ray tracing’, in Kunii, T.L. (Ed.): ‘Computer graphics’ (Springer, Tokyo, Japan, 1985), pp. 41–65.
    10. 10)
      • 39. Yamada, N., Tanaka, Y., Nishikawa, K.: ‘Radar cross section for pedestrian in 76 GHz band’. 2005 European Microwave Conf., Paris, France, 2005, vol. 2, pp. 4.
    11. 11)
      • 42. Ludloff, A.K.: ‘Praxiswissen radar und radarsignalverarbeitung’ (Springer-Verlag, Germany, 2013).
    12. 12)
      • 32. Alnujaim, I., Oh, D., Kim, Y.: ‘Generative adversarial networks to augment micro-Doppler signatures for the classification of human activity’. IGARSS 2019-2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, 2019, pp. 9459–9461.
    13. 13)
      • 41. Ruck, G.T., Barrick, D.E., Stuart, W.D., et al: ‘Radar cross section handbook’, vol. 1 (Plenum Press, New York, 1970).
    14. 14)
      • 3. Schiele, B., Wojek, C.: ‘Camera based pedestrian detection’, in Winner, H., Hakuli, S., Lotz, F., et al (Eds.): ‘Handbook of driver assistance systems: basic information, components and systems for active safety and comfort’ (Springer, Germany, 2016), pp. 525–545.
    15. 15)
      • 25. Chattopadhyay, S., Bhandarkar, S.M., Li, K.: ‘Human motion capture data compression by model-based indexing: a power aware approach’, IEEE Trans. Vis. Comput. Graphics, 2007, 13, (1), pp. 5–14.
    16. 16)
      • 40. Amin, M.G.: ‘Through-the-wall radar imaging’ (CRC press, Boca Raton, FL, USA, 2017).
    17. 17)
      • 28. Le, C., Dogaru, T.: ‘Numerical modeling of the airborne radar signature of dismount personnel in the UHF-, L-, Ku-, and Ka-bands’ (Army Research Lab, Adelphi, 2007).
    18. 18)
      • 45. Rubin, S.M., Whitted, T.: ‘A 3-dimensional representation for fast rendering of complex scenes’. ACM SIGGRAPH Computer Graphics, Seattle, WA, USA, 1980, vol. 14, pp. 110–116.
    19. 19)
      • 7. Held, P., Steinhauser, D., Kamann, A., et al: ‘Radar-based analysis of pedestrian micro-Doppler signatures using motion capture sensors’. 2018 IEEE Intelligent Vehicles Symp. (IV), Changshu, China, 2018, pp. 787–793.
    20. 20)
      • 26. Dogaru, T., Nguyen, L., Le, C.: ‘Computer models of the human body signature for sensing through the wall radar applications’ (Army Research Lab, Adelphi, 2007).
    21. 21)
      • 11. Angelov, A., Robertson, A., Murray-Smith, R., et al: ‘Practical classification of different moving targets using automotive radar and deep neural networks’, IET Radar Sonar Navig., 2018, 12, (10), pp. 1082–1089.
    22. 22)
      • 14. Du, R., Fan, Y., Wang, J.: ‘Pedestrian and bicyclist identification through micro Doppler signature with different approaching aspect angles’, IEEE Sens. J., 2018, 18, (9), pp. 3827–3835.
    23. 23)
      • 4. Ragesh, N., Rajesh, R.: ‘Pedestrian detection in automotive safety: understanding state-of-the-art’, IEEE Access, 2019, 7, pp. 47864–47890.
    24. 24)
      • 15. Ram, S.S., Gurbuz, S.Z., Chen, V.C.: ‘Modeling and simulation of human motions for micro-Doppler signatures’, in Amin, M. (Ed.): ‘Radar for indoor monitoring’ (CRC Press, Boca Raton, FL, USA, 2017), pp. 39–69.
    25. 25)
      • 33. Garcia-Fernandez, A.F., Yeste-Ojeda, O.A., Grajal, J.: ‘Facet model of moving targets for ISAR imaging and radar back-scattering simulation’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (3), pp. 1455–1467.
    26. 26)
      • 30. Ram, S.S., Christianson, C., Kim, Y., et al: ‘Simulation and analysis of human micro-Dopplers in through-wall environments’, IEEE Trans. Geosci. Remote Sens., 2010, 48, (4), pp. 2015–2023.
    27. 27)
      • 19. Tsai, Y.Y., Lin, W.C., Cheng, K.B., et al: ‘Real-time physics-based 3D biped character animation using an inverted pendulum model’, IEEE Trans. Vis. Comput. Graphics, 2010, 16, (2), pp. 325–337.
    28. 28)
      • 6. Majer, F., Yan, Z., Broughton, G., et al: ‘Learning to see through haze: radar-based human detection for adverse weather conditions’. 2019 European Conf. on Mobile Robots (ECMR), Prague, Czech Republic, 2019, pp. 1–7.
    29. 29)
      • 13. Duggal, G., Ram, S.S., Mishra, K.V.: ‘Micro-Doppler and Micro-range detection via Doppler-resilient 802.11 ad-based vehicle-to-pedestrian radar’. 2019 IEEE Radar Conf. (RadarConf), 2019.
    30. 30)
      • 31. Erol, B., Gurbuz, S.Z., Amin, M.G.: ‘GAN-based synthetic radar micro-Doppler augmentations for improved human activity recognition’. 2019 IEEE Radar Conf. (RadarConf), Boston, MA, USA, 2019, pp. 1–5.
    31. 31)
      • 10. Stolz, M., Schubert, E., Meinl, F., et al: ‘Multi-target reflection point model of cyclists for automotive radar’. 2017 European Radar Conf. (EURAD), Nuremburg, Germany, 2017, pp. 94–97.
    32. 32)
      • 12. Prophet, R., Hoffmann, M., Ossowska, A., et al: ‘Pedestrian classification for 79 GHz automotive radar systems’. 2018 IEEE Intelligent Vehicles Symp. (IV), Changshu, China, 2018, pp. 1265–1270.
    33. 33)
      • 9. Fairchild, D.P., Narayanan, R.M.: ‘Classification of human motions using empirical mode decomposition of human micro-Doppler signatures’, IET Radar Sonar Navig., 2014, 8, (5), pp. 425–434.
    34. 34)
      • 43. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
    35. 35)
      • 1. WHO: ‘Global status report on road safety 2018 (2018)’, 2019, https://www.who.int/violence_injury_prevention/road_safety_status/2018/English-Summary-GSRRS2018.pdf.
    36. 36)
      • 17. Khomchuk, P., Stainvas, I., Bilik, I.: ‘Pedestrian motion direction estimation using simulated automotive MIMO radar’, IEEE Trans. Aerosp. Electron. Syst., 2016, 52, (3), pp. 1132–1145.
    37. 37)
      • 16. Park, J., Johnson, J.T., Majurec, N., et al: ‘Simulation and analysis of polarimetric radar signatures of human gaits’, IEEE Trans. Aerosp. Electron. Syst., 2014, 50, (3), pp. 2164–2175.
    38. 38)
      • 8. Steinhauser, D., HeId, P., Kamann, A., et al: ‘Micro-Doppler extraction of pedestrian limbs for high resolution automotive radar’. 2019 IEEE Intelligent Vehicles Symp. (IV), Paris, France, 2019, pp. 764–769.
    39. 39)
      • 34. Ling, H., Chou, R.C., Lee, S.W.: ‘Shooting and bouncing rays: calculating the RCS of an arbitrarily shaped cavity’, IEEE Trans. Antennas Propag., 1989, 37, (2), pp. 194–205.
    40. 40)
      • 44. Wenger, J.: ‘Automotive radar-status and perspectives’. IEEE Compound Semiconductor Integrated Circuit Symp., 2005. CSIC'05, Palm Springs, CA, USA, 2005, pp. 4–pp.
    41. 41)
      • 29. van Dorp, P., Groen, F.: ‘Human walking estimation with radar’, IEE Proc., Radar Sonar Navig., 2003, 150, (5), pp. 356–365.
    42. 42)
      • 18. Smith, G.E., Woodbridge, K., Baker, C.J.: ‘Multistatic micro-Doppler signature of personnel’. 2008 IEEE Radar Conf. RADAR'08, Rome, Italy, 2008, pp. 1–6.
    43. 43)
      • 21. Ram, S.S., Ling, H.: ‘Simulation of human micro Dopplers using computer animation data’. 2008 IEEE Radar Conf. RADAR'08, Rome, Italy, 2008, pp. 1–6.
    44. 44)
      • 37. Schepers, M., Giuberti, M., Bellusci, G., et al (Eds.): ‘Xsens MVN: consistent tracking of human motion using inertial sensing’, Xsens, 2018. Available at https://www.xsens.com/functions/inertial-sensor-modules/.
    45. 45)
      • 27. Dogaru, T., Le, C.: ‘Validation of Xpatch computer models for human body radar signature’ (Army Research Lab, Adelphi, 2008).
    46. 46)
      • 22. Ram, S.S., Ling, H.: ‘Micro Doppler signature simulation of computer animated human and animal motions’. 2008 IEEE Antennas and Propagation Society Int. Symp. AP-S 2008, San Diego, CA, USA, 2008, pp. 1–4.
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