http://iet.metastore.ingenta.com
1887

Proposed ontology for cognitive radar systems

Proposed ontology for cognitive radar systems

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Cognitive radar is a rapidly developing area of research with many opportunities for innovation. A significant obstacle to development in this discipline is the absence of a common understanding of what constitutes a cognitive radar. The proposition in this study is that radar systems should not be classed as cognitive, or not cognitive, but should be graded by the degree of cognition exhibited. The authors introduce a new taxonomy framework for cognitive radar against which research, experimental and production systems can be benchmarked, enabling clear communication regarding the level of cognition being discussed.

References

    1. 1)
      • 1. Haykin, S.: ‘Radar vision’. IEEE Int. Radar Conf., Arlington, VA, 1990, pp. 585588.
    2. 2)
      • 2. Haykin, S.: ‘Adaptive radar: evolution to cognitive radar’. IEEE Int. Symp. Phased Array Systems and Technology, Boston, MA, 2003, p. 613.
    3. 3)
      • 3. Haykin, S.: ‘Cognitive radar [a way of the future]’, IEEE Signal Process. Mag., 2006, 23, (January), pp. 3040.
    4. 4)
      • 4. Martone, A.F.: ‘Cognitive radar demystified’, URSI Radio Science Bulletin, 2014, 2014, (350), pp. 1022.
    5. 5)
      • 5. UK Department for Culture Media & Sport: ‘The UK spectrum strategy delivering the best value from spectrum for the UK’, 2014.
    6. 6)
      • 6. UK Department for Culture Media & Sport: ‘Enabling UK growth – releasing public spectrum making 500 MHz of spectrum available by 2020’, 2011.
    7. 7)
      • 7. Federal Communications Commission: ‘Auction of advanced wireless services (Aws-3) licenses closes’. Available athttps://apps.fcc.gov/edocs_public/attachmatch/DA-15-131A1.pdf, accessed June 2018.
    8. 8)
      • 8. Science and Technology Committee: ‘https://publications.parliament.uk/pa/cm201011/cmselect/cmsctech/619/61913.htm atannex 1: technology readiness levels’, 2011.
    9. 9)
      • 9. EDA: ‘Working paper – best practice guide for UMS handling’, 2012.
    10. 10)
      • 10. National Institute of Standards and Technology: ‘Autonomy levels for unmanned systems (ALFUS) framework’, 2005.
    11. 11)
      • 11. Arrabales, R., Ledezma, A., Sanchis, A.: ‘Consscale’, J. Conscious. Stud., 2010, 17, (3–4), pp. 131164.
    12. 12)
      • 12. Fuster, J.: ‘Cortex and mind: unifying cognition’ (Oxford University Press, New York, NY, 2003).
    13. 13)
      • 13. Haykin, S., Xue, Y., Davidson, T.N.: ‘Optimal waveform design for cognitive radar’. Asilomar Conf. Signals, Systems and Computers, Pacific Grove, 2008, pp. 37.
    14. 14)
      • 14. Haykin, S.: ‘Cognitive radar networks’. 1st IEEE Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing., Puerto Vallarta, Mexico, 2005.
    15. 15)
      • 15. Capraro, G., Baldygo, W., Day, R., et al: ‘Autonomous intelligent radar system (AIRS) for multi-sensor radars’. IEEE CAMSAP 2005 – First Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Puerto Vallarta, Mexico, 2005, pp. 1619.
    16. 16)
      • 16. Haykin, S., Zia, A., Arasaratnam, I., et al: ‘Cognitive tracking radar’. IEEE Radar Conf., Washington, DC, 2010, pp. 14671470.
    17. 17)
      • 17. Haykin, S., Zia, A., Xue, Y., et al: ‘Control theoretic approach to tracking radar: first step towards cognition’, Digit. Signal Process., 2011, 21, (5), pp. 576585.
    18. 18)
      • 18. Haykin, S., Fatemi, M., Setoodeh, P., et al: ‘Cognitive control’, Proc. IEEE, 2012, 100, (12), pp. 31563169.
    19. 19)
      • 19. Fatemi, M., Haykin, S.: ‘Cognitive control: theory and application’, IEEE. Access., 2014, 2, pp. 698710.
    20. 20)
      • 20. Haykin, S.: ‘Cognitive radar networks’. Fourth IEEE Workshop on Sensor Array and Multichannel Processing, Waltham, Massachusetts, 2006, pp. 930.
    21. 21)
      • 21. Haykin, S.: ‘Cognitive networks: radar, radio, and control for new generation of engineered complex networks’. IEEE Radar Conf., Ottawa, ON, 2013.
    22. 22)
      • 22. Guerci, J.R., Pillai, S.U.: ‘Adaptive transmission radar: the next ‘wave’?’. Proc. the IEEE National Aerospace and Electronics Conf., Dayton, OH, 2000, pp. 779786.
    23. 23)
      • 23. Garren, D.A., Odom, A.C., Osborn, M.K., et al: ‘Full-polarization matched-illumination for target detection and identification’, IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (3), pp. 824837.
    24. 24)
      • 24. Guerci, J.R., Baranoski, E.J.: ‘Knowledge-aided adaptive radar at DARPA’, IEEE Signal Process. Mag., 2006, 23, (1), pp. 4150.
    25. 25)
      • 25. Guerci, J.R.: ‘Cognitive radar: the next radar wave?’, Microw. J., 2011, 54, (1), pp. 2236.
    26. 26)
      • 26. Guerci, J.R.: ‘Cognitive radar: a knowledge-aided fully adaptive approach’. IEEE Radar Conf., Washington, DC, 2010, pp. 13651370.
    27. 27)
      • 27. Griffin, D.R., Friend, J.H., Webster, F.A.: ‘Target discrimination by the echolocation of bats’, J. Exp. Zool., 1965, 158, (2), pp. 155168.
    28. 28)
      • 28. von Helversen, D., von Helversen, O.: ‘Object recognition by echolocation: a nectar-feeding bat exploiting the flowers of a rain forest vine’, J. Comp. Physiol. A Sens. Neural Behav. Physiol., 2003, 189, (5), pp. 327336.
    29. 29)
      • 29. Vespe, M., Jones, G., Baker, C.J.: ‘Lessons for radar [waveform diversity in echo locating mammals]’, IEEE Signal Process. Mag., 2009, 26, (1), pp. 6575.
    30. 30)
      • 30. Baker, C.J., Woodbridge, K., Holderied, M.W., et al: ‘Analysis of acoustic echoes from a bat-pollinated plant species: insight into strategies for radar and sonar target classification’, IET Radar Sonar Navig., 2012, 6, (6), pp. 536544.
    31. 31)
      • 31. Baker, C.J., Smith, G.E., Balleri, A., et al: ‘Biomimetic echolocation with application to radar and sonar sensing’, Proc. IEEE, 2014, 102, (4), pp. 447458.
    32. 32)
      • 32. Au, W.W.L., Simmons, J.A.: ‘Echolocation in dolphins and bats’, Phys. Today, 2007, 60, (9), pp. 4045.
    33. 33)
      • 33. Wisniewska, D.M., Ratcliffe, J.M., Beedholm, K., et al: ‘Range-dependent flexibility in the acoustic field of view of echolocating porpoises (phocoena phocoena)’, ELife, 4. Available at https://doi.org/10.7554/eLife.05651, 2015, (March).
    34. 34)
      • 34. Thaler, L., Reich, G.M., Zhang, X., et al: ‘Mouth-clicks used by blind expert human echolocators – signal description and model based signal synthesis’, PLoS Comput. Biol., 2017, 13, (8), pp. 117.
    35. 35)
      • 35. Thaler, L., De Vos, R., Kish, D., et al: ‘Human echolocators adjust loudness and number of clicks for detection of reflectors at various azimuth angles’, Proc. R. Soc. B, 2018, 285, (1873), p. 285.
    36. 36)
      • 36. Balleri, A., Leighton, T.G.: ‘Editorial: biologically-inspired radar and sonar systems’, IET Radar Sonar Navig., 2012, 6, (6), pp. 507509.
    37. 37)
      • 37. Wicks, M.C.: ‘A brief history of waveform diversity’. IEEE Radar Conf., Passadena, 2009.
    38. 38)
      • 38. Wicks, M.: ‘Spectrum crowding and cognitive radar’. 2nd Int. Workshop on Cognitive Information Processing (CIP), Elba Island, 2010, pp. 452457.
    39. 39)
      • 39. Griffiths, H., Blunt, S., Cohen, L., et al: ‘Challenge problems in spectrum engineering and waveform diversity’. IEEE Radar Conf., Ottawa, 2013.
    40. 40)
      • 40. Griffiths, H., Cohen, L., Watts, S., et al: ‘Radar spectrum engineering and management: technical and regulatory issues’, Proc. IEEE, 2015, 103, (1), pp. 85102.
    41. 41)
      • 41. DeLong, D., Hofstetter, E.: ‘On the design of optimum radar waveforms for clutter rejection’, IEEE Trans. Inf. Theory, 1967, 13, (3), pp. 454463.
    42. 42)
      • 42. Stinco, P., Greco, M.S., Gini, F.: ‘Spectrum sensing and sharing for cognitive radars’, IET Radar Sonar Navig., 2016, 10, pp. 595602.
    43. 43)
      • 43. Goodman, N.A.: ‘Closed-loop radar with adaptively matched waveforms’. 2007 Int. Conf. Electromagnetics in Advanced Applications, ICEAA'07, Torino, 2007, pp. 468471.
    44. 44)
      • 44. La Manna, M., Stinco, P., Greco, M., et al: ‘Design of a cognitive radar for operation in spectrally dense environments’. IEEE Radar Conf., Ottawa, 2013.
    45. 45)
      • 45. La Manna, M., Stinco, P., Greeo, M., et al: ‘Cognitive techniques for a wideband phased array radar’. IEEE Int. Symp. Phased Array Systems and Technology, Boston, Massachusetts, 2013, pp. 389393.
    46. 46)
      • 46. Aubry, A., De Maio, A., Piezzo, M., et al: ‘Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization’, IEEE Trans. Aerosp. Electron. Syst., 2014, 50, (2), pp. 11381152.
    47. 47)
      • 47. Aubry, A., Carotenuto, V., Maio, A., et al: ‘Cognitive radar waveform design for spectral compatibility’. 2016 Sensor Signal Processing for Defence (SSPD), Edinburgh, 2016.
    48. 48)
      • 48. Aubry, A., De Maio, A., Piezzo, M., et al: ‘Cognitive design of the receive filter and transmitted phase code in reverberating environment’, IET Radar Sonar Navig., 2012, 6, (9), pp. 822833.
    49. 49)
      • 49. Blunt, S.D., Mokole, E.L.: ‘An overview of radar waveform diversity’, IEEE Trans. Aerosp. Electron. Syst. Mag., 2016, 31, (11), pp. 240.
    50. 50)
      • 50. Gjessing, D.: ‘Adaptive techniques for radar detection and identification of objects in an ‘ocean environment’, EEE J. Oceanic Eng., 1981, 6, (1), pp. 517.
    51. 51)
      • 51. Soldani, F., Alabaster, C.M.: ‘The benefits of matched illumination for radar detection of ground based targets’. Int. Waveform Diversity and Design Conf., Pisa, 2007, pp. 2327.
    52. 52)
      • 52. Romero, R.A., Bae, J., Goodman, N.A.: ‘Theory and application of SNR and mutual information matched illumination waveforms’, IEEE Trans. Aerosp. Electron. Syst., 2011, 47, (2), pp. 912927.
    53. 53)
      • 53. Romero, R.A., Goodman, N.A.: ‘Waveform design in signal-dependent interference and application to target recognition with multiple transmissions’, IET Radar Sonar Navig., 2009, 3, (4), pp. 328340.
    54. 54)
      • 54. Goodman, N.A., Venkata, P.R., Neifeld, M.A.: ‘Adaptive waveform design and sequential hypothesis testing for target recognition with active sensors’, IEEE J. Sel. Top. Sign. Proces., 2007, 1, (1), pp. 105113.
    55. 55)
      • 55. Smits, F., Huizing, A., Rossum, W., et al: ‘A cognitive radar network: architecture and application to multiplatform radar management’. European Radar Conf., Amsterdam, 2008, pp. 312315.
    56. 56)
      • 56. Nijsure, Y., Chen, Y., Boussakta, S., et al: ‘Novel system architecture and waveform design for cognitive radar radio networks’, IEEE Trans. Veh. Technol., 2012, 61, (8), pp. 36303642.
    57. 57)
      • 57. Charlish, A.B.: ‘Tasking networked multi-function radar systems for active tracking’. 14th Int. Radar Symp. (IRS), Dresden, 2013, pp. 367374.
    58. 58)
      • 58. Kershaw, D.J., Evans, R.J.: ‘Optimal waveform selection for tracking systems’, IEEE Trans. Inf. Theory, 1994, 40, (5), pp. 15361550.
    59. 59)
      • 59. Romero, R., Goodman, N.: ‘Cognitive radar network: cooperative adaptive beamsteering for integrated search-and-track application’, IEEE Trans. Aerosp. Electron. Syst., 2013, 49, (2), pp. 915931.
    60. 60)
      • 60. Kreucher, C.M., Hero, A.O., Kastella, K.D., et al: ‘An information-based approach to sensor management in large dynamic networks’, Proc. IEEE, 2007, 95, (5), pp. 978999.
    61. 61)
      • 61. Sherwani, H., Griffiths, H.D.: ‘Tracking parameter control in multifunction radar network incorporating information sharing’. FUSION 2016 – 19th Int. Conf. Information Fusion, Heidelberg, 2016.
    62. 62)
      • 62. Bell, K.L., Baker, C.J., Smith, G.E., et al: ‘Cognitive radar framework for target detection and tracking’, IEEE J. Sel. Top. Sign. Proces., 2015, 9, (8), pp. 14271439.
    63. 63)
      • 63. Smith, G.E., Cammenga, Z., Mitchell, A., et al: ‘Experiments with cognitive radar’, IEEE Aerosp. Electron. Syst. Mag., 2016, 31, (12), pp. 3446.
    64. 64)
      • 64. Oechslin, R., Smith, G.E., Aulenbacher, U., et al: ‘Cognitive radar testbed development special session on cognitive radar’. Asilomar Conf. Signals, Systems and Computers, Pacific Grove, 2016.
    65. 65)
      • 65. Anderson, S.J.: ‘Remote sensing with the JINDALEE skywave radar’, IEEE J. Ocean. Eng., 1986, 11, (2), pp. 158163.
    66. 66)
      • 66. Lu, K., Chen, X.: ‘Cognitive over-the-horizon radar’. Proc. 2011 CIE Int. Conf. Radar, 2011, pp. 993996.
    67. 67)
      • 67. Saverino, A.L., Capria, A., Berizzi, F., et al: ‘Cognitive adaptive waveform technique for HF skywave radar’. 2010 2nd Int. Workshop on Cognitive Information Processing, CIP2010, Elba Island, 2010, pp. 247252.
    68. 68)
      • 68. Holdsworth, D.A.: ‘An over-the-horizon radar performance assessment module for use in cognitive radar’. IET Int. Conf. Radar Systems, Glasgow, 2012, pp. 3434.
    69. 69)
      • 69. Wicks, M.C.: ‘Radar the next generation – sensors as robots’. Int. Radar Conf., Adelaide, 2003, pp. 714.
    70. 70)
      • 70. Greenspan, M.: ‘Potential pitfalls of cognitive radars’. IEEE Radar Conf., Cincinnati, Ohio, 2014, pp. 12881290.
    71. 71)
      • 71. Bruggenwirth, S.: ‘Design and implementation of a three-layer cognitive radar architecture’. Conf. Record – Asilomar Conf. Signals, Systems and Computers, Pacific Grove, 2017, pp. 929933.
    72. 72)
      • 72. IEEE Standards Association: ‘IEEE p686 standard radar definitions’, 2017.
    73. 73)
      • 73. Baker, C., Smith, G.: ‘The case for cognition and radar sensing’, NATO Lecture Series EN-SET-216, 2015.
    74. 74)
      • 74. Farina, A.: ‘Introduction to radar signal & data processing: the opportunity’, RTO-EN-SET-063, 2006, pp. 2829.
    75. 75)
      • 75. Inggs, M.: ‘Passive coherent location as cognitive radar’, IEEE Aerosp. Electron. Syst. Mag., 2010, 25, (5), pp. 1217.
    76. 76)
      • 76. Vernon, D., Metta, G., Sandini, G.: ‘A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents’, IEEE Trans. Evol. Comput., 2007, 11, (2), pp. 151180.
    77. 77)
      • 77. Charlish, A., Hoffmann, F.: ‘Anticipation in cognitive radar using stochastic control’. IEEE Int. Radar Conf., Arlington, VA, 2015, pp. 16921697.
    78. 78)
      • 78. Horne, C.P., Ritchie, M., Griffiths, H.D., et al: ‘Experimental validation of cognitive radar anticipation using stochastic control’. Asilomar Conf. Signals, Systems and Computers, Pacific Grove, 2016.
    79. 79)
      • 79. Tweedale, J.W.: ‘A review of cognitive decision-making within future mission systems’, Procedia Comput. Sci., 2014, 35, pp. 10431052.
    80. 80)
      • 80. Kyllonen, P., Zu, J.: ‘Use of response time for measuring cognitive ability’, J. Intell., 2016, 4, (4), p. 14.
    81. 81)
      • 81. DeLong, D., Hofstetter, E.: ‘The design of clutter-resistant radar waveforms with limited dynamic range’, IEEE Trans. Inf. Theory, 1969, 15, (3), pp. 376385.
    82. 82)
      • 82. Gjessing, D.T., Saebboe, J., Helleren, O.E.: ‘Recognition of targets by linear and non-linear (Delta K) processing of multi frequency data’. RTO SET Symp. ‘Target Identification and Recognition Using RF Systems’, Oslo, 2004.
    83. 83)
      • 83. Kreucher, C., Hero, A.O., Kastella, K.: ‘A comparison of task driven and information driven sensor management for target tracking’. Proc. the 44th IEEE Conf. Decision and Control, and the European Control Conf., CDC-ECC, Seville, 2005, pp. 40044009.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2018.5280
Loading

Related content

content/journals/10.1049/iet-rsn.2018.5280
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address