Sonar Sensing

  • Lindsay KleemanEmail author
  • Roman Kuc
Part of the Springer Handbooks book series (SHB)


Sonar or ultrasonic sensing uses the propagation of acoustic energy at higher frequencies than normal hearing to extract information from the environment. This chapter presents the fundamentals and physics of sonar sensing for object localization, landmark measurement and classification in robotics applications. The source of sonar artifacts is explained and how they can be dealt with. Different ultrasonic transducer technologies are outlined with their main characteristics highlighted.

Sonar systems are described that range in sophistication from low-cost threshold-based ranging modules to multitransducer multipulse configurations with associated signal processing requirements capable of accurate range and bearing measurement, interference rejection, motion compensation, and target classification. Continuous-transmission frequency-modulated (CTFM ) systems are introduced and their ability to improve target sensitivity in the presence of noise is discussed. Various sonar ring designs that provide rapid surrounding environmental coverage are described in conjunction with mapping results. Finally the chapter ends with a discussion of biomimetic sonar, which draws inspiration from animals such as bats and dolphins.


Pulse Shape Beam Pattern Interaural Time Difference Sonar System Interference Rejection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





complementary metal-oxide-semiconductor


continuous-transmission frequency modulation


discrete Fourier transform


digital signal processor


fast Fourier transform


field-programmable gate array


false range


hidden Markov model


interaural amplitude difference


interaural time difference


microelectromechanical system


maximum likelihood estimate


multiple reflection


pseudo-amplitude scan


polyvinylidene fluoride


standard deviation


simultaneous localization and mapping




virtual object


  1. 30.1
    L.E. Kinsler, A.R. Frey, A.B. Coppens, J.V. Sanders: Fundamentals of Acoustics (Wiley, New York 1982)Google Scholar
  2. 30.2
    R.C. Weast, M.J. Astle (Eds.): CRC Handbook of Chemistry and Physics, 59th edn. (CRC, Boca Raton 1978)Google Scholar
  3. 30.3
    J. Borenstein, H.R. Everett, L. Feng: Navigating Mobile Robots (Peters, Wellesley 1996)zbMATHGoogle Scholar
  4. 30.4
    R. Kuc, M.W. Siegel: Physically-based simulation model for acoustic sensor robot navigation, IEEE Trans. Pattern Anal. Mach. Intell. 9(6), 766–778 (1987)CrossRefGoogle Scholar
  5. 30.5
    SensComp: 7000, (2007)
  6. 30.6
    H.H. Poole: Fundamentals of Robotics Engineering (Van Nostrand, New York 1989)CrossRefGoogle Scholar
  7. 30.7
    J.E. Piercy: American National Standard: Method for Calculation of the Absorption of Sound by the Atmosphere, Vol. ANSI SI-26-1978 (Acoust. Soc. Am., Washington 1978)Google Scholar
  8. 30.8
    B. Barshan, R. Kuc: A bat-like sonar system for obstacle localization, IEEE Trans. Syst. Man Cybern. 22(4), 636–646 (1992)CrossRefGoogle Scholar
  9. 30.9
    R. Kuc: Three dimensional docking using qualitative sonar. In: Intelligent Autonomous Systems IAS-3, ed. by F.C.A. Groen, S. Hirose, C.E. Thorpe (IOS, Washington 1993) pp. 480–488Google Scholar
  10. 30.10
    R. Kuc: Biomimetic sonar locates and recognizes objects, J. Ocean Eng. 22(4), 616–624 (1997)CrossRefGoogle Scholar
  11. 30.11
    L. Kleeman, R. Kuc: Mobile robot sonar for target localization and classification, Int. J. Robotics Res. 14(4), 295–318 (1995)CrossRefGoogle Scholar
  12. 30.12
    B. Stanley: A Comparison of Binaural Ultrasonic Sensing Systems, Ph.D. Thesis (University of Wollongong, Wollongong 2003)Google Scholar
  13. 30.13
    Material Systems Inc.:
  14. 30.14
    F.L. Degertekin, S. Calmes, B.T. Khuri-Yakub, X. Jin, I. Ladabaum: Fabrication and characterization of surface micromachined capacitive ultrasonic immersion transducers, J. Microelectromech. Syst. 8(1), 100–114 (1999)CrossRefGoogle Scholar
  15. 30.15
    B. Barshan, R. Kuc: Differentiating sonar reflections from corners and planes by employing an intelligent sensor, IEEE Trans. Pattern Anal. Mach. Intell. 12(6), 560–569 (1990)CrossRefGoogle Scholar
  16. 30.16
    A. Freedman: A mechanism of acoustic echo formation, Acustica 12, 10–21 (1962)MathSciNetzbMATHGoogle Scholar
  17. 30.17
    A. Freedman: The high frequency echo structure of somae simple body shapes, Acustica 12, 61–70 (1962)MathSciNetzbMATHGoogle Scholar
  18. 30.18
    Ö. Bozma, R. Kuc: A physical model-based analysis of heterogeneous environments using sonar – ENDURA method, IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 497–506 (1994)CrossRefGoogle Scholar
  19. 30.19
    Ö. Bozma, R. Kuc: Characterizing pulses reflected from rough surfaces using ultrasound, J. Acoust. Soc. Am. 89(6), 2519–2531 (1991)CrossRefGoogle Scholar
  20. 30.20
    P.J. McKerrow: Echolocation – from range to outline segments. In: Intelligent Autonomous Systems IAS-3, ed. by F.C.A. Groen, S. Hirose, C.E. Thorpe (IOS, Washington 1993) pp. 238–247Google Scholar
  21. 30.21
    J. Thomas, C. Moss, M. Vater (Eds.): Echolocation in Bats and Dolphins (University of Chicago Press, Chicago 2004)Google Scholar
  22. 30.22
    J. Borenstein, Y. Koren: Error eliminating rapid ultrasonic firing for mobile robot obstacle avoidance, IEEE Trans. Robotics Autom. 11(1), 132–138 (1995)CrossRefGoogle Scholar
  23. 30.23
    L. Kleeman: Fast and accurate sonar trackers using double pulse coding, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (1999) pp. 1185–1190Google Scholar
  24. 30.24
    R. Kuc: Pseudo-amplitude sonar maps, IEEE Trans. Robotics Autom. 17(5), 767–770 (2001)CrossRefGoogle Scholar
  25. 30.25
    H. Peremans, K. Audenaert, J.M. Van Campenhout: A high-resolution sensor based on tri-aural perception, IEEE Trans. Robotics Autom. 9(1), 36–48 (1993)CrossRefGoogle Scholar
  26. 30.26
    A. Sabatini, O. Di Benedetto: Towards a robust methodology for mobile robot localization using sonar, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1994) pp. 3142–3147Google Scholar
  27. 30.27
    L. Kleeman: Advanced sonar with velocity compenstation, Int. J. Robotics Res. 23(2), 111–126 (2004)CrossRefGoogle Scholar
  28. 30.28
    A. Elfes: Sonar-based real world mapping and navigation, IEEE Trans. Robotics Autom. 3, 249–265 (1987)CrossRefGoogle Scholar
  29. 30.29
    S. Thrun, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Haehnel, C. Rosenberg, N. Roy, J. Schulte, D. Schulz: MINERVA: A second geration mobile tour-guide robot, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1999) pp. 1999–2005Google Scholar
  30. 30.30
    K. Konolige: Improved occupancy grids for map building, Auton. Robotics 4, 351–367 (1997)CrossRefGoogle Scholar
  31. 30.31
    R. Grabowski, P. Khosla, H. Choset: An enhanced occupancy map for exploration via pose separation, Proc. IEEE/RSJ Int. Conf. Intell. Robotics Syst. (IROS) (2003) pp. 705–710Google Scholar
  32. 30.32
    J.D. Tardos, J. Neira, P.M. Newman, J.J. Leonard: Robust mapping and localization in indoor environments using sonar data, Int. J. Robotics Res. 21(6), 311–330 (2002)CrossRefGoogle Scholar
  33. 30.33
    O. Aycard, P. Larouche, F. Charpillet: Mobile robot localization in dynamic environments using places recognition, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1998) pp. 3135–3140Google Scholar
  34. 30.34
    B. Kuipers, P. Beeson: Bootstrap learning for place recognition, Proc. 18th Nat. Conf. Artif. Intell. (ANAI) (2002)Google Scholar
  35. 30.35
    A. Bandera, C. Urdiales, F. Sandoval: Autonomous global localization using Markov chains and optimized sonar landmarks, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (2000) pp. 288–293Google Scholar
  36. 30.36
    R. Kuc: Biomimetic sonar and neuromorphic processing eliminate reverberation artifacts, IEEE Sens. J. 7(3), 361–369 (2007)CrossRefGoogle Scholar
  37. 30.37
    A.M. Sabatini: A stochastic model of the time-of-flight noise in airborne sonar ranging systems, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44(3), 606–614 (1997)CrossRefGoogle Scholar
  38. 30.38
    C. Biber, S. Ellin, E. Sheck, J. Stempeck: The Polaroid ultrasonic ranging system, Proc. 67th Audio Eng. Soc. Conv. (1990)Google Scholar
  39. 30.39
    R. Kuc: Forward model for sonar maps produced with the Polaroid ranging module, IEEE Trans. Robotics Autom. 19(2), 358–362 (2003)CrossRefGoogle Scholar
  40. 30.40
    M.K. Brown: Feature extraction techniques for recognizing solid objects with an ultrasonic range sensor, IEEE J. Robotics Autom. 1(4), 191–205 (1985)CrossRefGoogle Scholar
  41. 30.41
    N.L. Harper, P.J. McKerrow: Classification of plant species from CTFM ultrasonic range data using a neural network, Proc. IEEE Int. Conf. Neural Netw. (1995) pp. 2348–2352CrossRefGoogle Scholar
  42. 30.42
    Z. Politis, P.J. Probert: Target localization and identification using CTFM sonar imaging: The AURBIT method, Proc. IEEE Int. Symp. Comput. Intell. Robotics Autom. (CIRLA) (1999) pp. 256–261Google Scholar
  43. 30.43
    R. Mueller, R. Kuc: Foliage echoes: A probe into the ecological acoustics of bat echolocation, J. Acoust. Soc. Am. 108(2), 836–845 (2000)CrossRefGoogle Scholar
  44. 30.44
    P.N.T. Wells: Biomedical Ultrasonics (Academic, New York 1977)Google Scholar
  45. 30.45
    J.L. Prince, J.M. Links: Medical Imaging Signals and Systems (Prentice Hall, Upper Saddle River 2006)Google Scholar
  46. 30.46
    F.J. Alvarez, R. Kuc: High resolution adaptive spiking sonar, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56(5), 1024–1033 (2009)CrossRefGoogle Scholar
  47. 30.47
    F.J. Alvarez, R. Kuc, T. Aguilera: Identifying fabrics with a variable emission airborne spiking sonar, IEEE Sens. J. 11(9), 1905–1912 (2011)CrossRefGoogle Scholar
  48. 30.48
    J.J. Leonard, H.F. Durrant-Whyte: Mobile robot localization by tracking geometric beacons, IEEE Trans. Robotics Autom. 7(3), 376–382 (1991)CrossRefGoogle Scholar
  49. 30.49
    R. Kuc: Generating B-scans of the environmental with conventional sonar, IEEE Sens. J. 8(2), 151–160 (2008)CrossRefGoogle Scholar
  50. 30.50
    P.M. Woodward: Probability and Information Theory with Applications to Radar, 2nd edn. (Pergamon, Oxford 1964)zbMATHGoogle Scholar
  51. 30.51
    A. Heale, L. Kleeman: Fast target classification using sonar, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (2001) pp. 1446–1451Google Scholar
  52. 30.52
    S. Fazli, L. Kleeman: A real time advanced sonar ring with simultaneous firing, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (2004) pp. 1872–1877Google Scholar
  53. 30.53
    T. Yata, A. Ohya, S. Yuta: A fast and accurate sonar-ring sensor for a mobile robot, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1999) pp. 630–636Google Scholar
  54. 30.54
    L. Kleeman: Scanned monocular sonar and the doorway problem, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (1996) pp. 96–103CrossRefGoogle Scholar
  55. 30.55
    G. Kao, P. Probert: Feature extraction from a broadband sonar sensor for mapping structured environments efficiently, Int. J. Robotics Res. 19(10), 895–913 (2000)CrossRefGoogle Scholar
  56. 30.56
    B. Stanley, P. McKerrow: Measuring range and bearing with a binaural ultrasonic sensor, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (1997) pp. 565–571Google Scholar
  57. 30.57
    P.T. Gough, A. de Roos, M.J. Cusdin: Continuous transmission FM sonar with one octave bandwidth and no blind time. In: Autonomous Robot Vehicles, ed. by I.J. Cox, G.T. Wilfong (Springer, Berlin, Heidelberg 1990) pp. 117–122CrossRefGoogle Scholar
  58. 30.58
    L. Kay: A CTFM acoustic spatial sensing technology: Its use by blind persons and robots, Sens. Rev. 19(3), 195–201 (1999)CrossRefGoogle Scholar
  59. 30.59
    L. Kay: Auditory perception and its relation to ultrasonic blind guidance aids, J. Br. Inst. Radio Eng. 24, 309–319 (1962)Google Scholar
  60. 30.60
    P.J. McKerrow, N.L. Harper: Recognizing leafy plants with in-air sonar, IEEE Sens. J. 1(4), 245–255 (2001)CrossRefGoogle Scholar
  61. 30.61
    K. Audenaert, H. Peremans, Y. Kawahara, J. Van Campenhout: Accurate ranging of multiple objects using ultrasonic sensors, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1992) pp. 1733–1738Google Scholar
  62. 30.62
    J. Borenstein, Y. Koren: Noise rejection for ultrasonic sensors in mobile robot applications, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1992) pp. 1727–1732Google Scholar
  63. 30.63
    K.W. Jorg, M. Berg: Mobile robot sonar sensing with pseudo-random codes, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1998) pp. 2807–2812Google Scholar
  64. 30.64
    S. Shoval, J. Borenstein: Using coded signals to benefit from ultrasonic sensor crosstalk in mobile robot obstacle avoidance, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (2001) pp. 2879–2884Google Scholar
  65. 30.65
    K. Nakahira, T. Kodama, T. Furuhashi, H. Maeda: Design of digital polarity correlators in a multiple-user sonar ranging system, IEEE Trans. Instrum. Meas. 54(1), 305–310 (2005)CrossRefGoogle Scholar
  66. 30.66
    A. Heale, L. Kleeman: A sonar sensor with random double pulse coding, Aust. Conf. Robotics Autom. (2000) pp. 81–86Google Scholar
  67. 30.67
    A. Diosi, G. Taylor, L. Kleeman: Interactive SLAM using Laser and Advanced Sonar, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (2005) pp. 1115–1120Google Scholar
  68. 30.68
    S.A. Walter: The sonar ring: obstacle detection for a mobile robot, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1987) pp. 1574–1578Google Scholar
  69. 30.69
    S. Fazli, L. Kleeman: Wall following and obstacle avoidance results from a multi-DSP sonar ring on a mobile robot, Proc. IEEE Int. Conf. Mechatron. Autom. (2005) pp. 432–436Google Scholar
  70. 30.70
    S. Fazli, L. Kleeman: Sensor design and signal processing for an advanced sonar ring, Robotica 24(4), 433–446 (2006)CrossRefGoogle Scholar
  71. 30.71
    D. Browne, L. Kleeman: An advanced sonar ring design with 48 channels of continuous echo processing using matched filters, Proc. IEEE/RSJ Intell. Robots Syst. Conf. (IROS) (2009) pp. 4040–4046Google Scholar
  72. 30.72
    D.C. Browne, L. Kleeman: A sonar ring with continuous matched filtering and dynamically switched templates, Robotica 30(6), 891–912 (2012)CrossRefGoogle Scholar
  73. 30.73
    L. Kleeman, Akihisa Ohya: The design of a transmitter with a parabolic conical reflector for a sonar ring, Aust. Conf. Robotics Autom. (ICRA), Auckland (2006)Google Scholar
  74. 30.74
    D.C. Browne, L. Kleeman: A double refresh rate sonar ring with FPGA-based continuous matched filtering, Robotica 30(7), 1051–1062 (2012)CrossRefGoogle Scholar
  75. 30.75
    J. Steckel, A. Boen, H. Peremans: Broadband 3-D sonar system using a sparse array for indoor navigation, IEEE Trans. Robotics 91, 1–11 (2012)Google Scholar
  76. 30.76
    W.W.L. Au: The Sonar of Dolphins (Springer, Berlin, Heidelberg 1993)CrossRefGoogle Scholar
  77. 30.77
    R. Kuc, V. Kuc: Bat wing air pressures may deflect prey structures to provide echo cues for detecting prey in clutter, J. Acoust. Soc. Am. 132(3), 1776–1779 (2012)CrossRefGoogle Scholar
  78. 30.78
    B. Barshan, R. Kuc: Bat-like sonar system strategies for mobile robots, Proc. IEEE Int. Conf. Syst. Man Cybern. (1991)Google Scholar
  79. 30.79
    R. Kuc: Biologically motivated adaptive sonar, J. Acoust. Soc. Am. 100(3), 1849–1854 (1996)CrossRefGoogle Scholar
  80. 30.80
    V.A. Walker, H. Peremans, J.C.T. Hallam: One tone, two ears, three dimensions: A robotic investigation of pinnae movements used by rhinolophid and hipposiderid bats, J. Acoust. Soc. Am. 104, 569–579 (1998)CrossRefGoogle Scholar
  81. 30.81
    L. Gao, S. Balakrishnan, W. He, Z. Yan, R. Mueller: Ear deformations give bats a physical mechanism for fast adaptation of ultrasonic beam patterns, Phys. Rev. Lett. 1007, 214–301 (2011)Google Scholar
  82. 30.82
    R. Kuc: Biomimetic sonar system recognizes objects using binaural information, J. Acoust. Soc. Am. 102(2), 689–696 (1997)CrossRefGoogle Scholar
  83. 30.83
    R. Kuc: Recognizing retro-reflectors with an obliquely-oriented multi-point sonar and acoustic flow, Int. J. Robotics Res. 22(2), 129–145 (2003)CrossRefGoogle Scholar
  84. 30.84
    T. Horiuchi, T. Swindell, D. Sander, P. Abshire: A low-power CMOS neural amplifier with amplitude measurements for spike sorting, Proc. Int. Symp. Circuits Syst. (ISCAS), Vol. IV (2004) pp. 29–32Google Scholar
  85. 30.85
    R. Kuc: Neuromorphic processing of moving sonar data for estimating passing range, IEEE Sens. J. 7(5), 851–859 (2007)CrossRefGoogle Scholar
  86. 30.86
    R. Kuc: Binaural sonar electronic travel aid provides vibrotactile cues for landmark, reflector motion, and surface texture classification, IEEE Trans. Biomed. Eng. 49(10), 1173–1180 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer Systems EngineeringMonash UniversityMelbourneAustralia
  2. 2.Department of Electrical EngineeringYale UniversityNew HavenUSA

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