Comparative analysis of different approaches to target differentiation and localization with sonar

Pattern Recognition

Volumn 36, Issue 5, 2003, Pages 1213-1231

  Barshan, Billur ^a   Ayrulu, Birsel ^a  

^a BILKENT UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Dempster Shafer evidential reasoning; Fuzzy c means clustering; Kernel estimator; Linear discriminant analysis; Majority voting; Nearest neighbor classifier; Parameterized density estimation; Sonar sensing; Target classification; Target differentiation; Target localization

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; COLLISION AVOIDANCE; FOURIER TRANSFORMS; KNOWLEDGE BASED SYSTEMS; NAVIGATION; NEURAL NETWORKS; SONAR; WAVELET TRANSFORMS;

TARGET TRACKING;

PATTERN RECOGNITION;

SONAR;

EID: 0037404263     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(02)00167-X     Document Type: Article

References (59)

1. A. Elfes, Sonar based real-world mapping and navigation, IEEE Trans. Robotics Autom. RA-3 (3) (1987) 249-265.
2. R. Kuc, B.V. Viard, A physically-based navigation strategy for sonar-guided vehicles, Int. J. Robotics Res. 10 (2) (1991) 75-87.
3. R. Kuc, M.W. Siegel, Physically-based simulation model for acoustic sensor robot navigation, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9 (6) (1987) 766-778.
4. A. Kurz, Constructing maps for mobile robot navigation based on ultrasonic range data, IEEE Trans. Systems Man Cybernet. B 26 (2) (1996) 233-242.
5. J.J. Leonard, H.F. Durrant-Whyte, Directed Sonar Navigation, Kluwer Academic Press, London, 1992.
6. Ö. Bozma, R. Kuc, A physical model-based analysis of heterogeneous environments using sonar-ENDURA method, IEEE Trans. Pattern Anal. Mach. Intell. 16 (5) (1994) 497-506.
7. R. Kuc, Three-dimensional tracking using qualitative bionic sonar, Robotics Autonomous Systems 11 (2) (1993) 213-219.
8. J. Borenstein, Y. Koren, Obstacle avoidance with ultrasonic sensors, IEEE Trans. Robotics Autom. RA-4 (2) (1988) 213-218.
9. H. Peremans, K. Audenaert, J.M. Van Campenhout, A high-resolution sensor based on tri-aural perception, IEEE Trans. Robotics Autom. 9 (1) (1993) 36-48.
10. L. Kleeman, R. Kuc, Mobile robot sonar for target localization and classification, Int. J. Robotics Res. 14 (4) (1995) 295-318.
11. B. Barshan, B. Ayrulu, Performance comparison of four methods of time-of-flight estimation for sonar waveforms, Electron. Lett. 34 (16) (1998) 1616-1617.
12. B. Ayrulu, B. Barshan, Neural networks for improved target differentiation and localization with sonar, Neural Networks 14 (3) (2001) 355-373.
13. B. Ayrulu, B. Barshan, Reliability measure assignment to sonar for robust target differentiation, Pattern Recognition 35 (6) (2002) 1403-1419.
14. B. Ayrulu, B. Barshan, Identification of target primitives with multiple decision-making sonars using evidential reasoning, Int. J. Robotics Res. 17 (6) (1998) 598-623.
15. B. Barshan, B. Ayrulu, S.W. Utete, Neural network based target differentiation using sonar for robotics applications, IEEE Trans. Robotics Autom. 16 (4) (2000) 435-442.
16. B. Barshan, Fast processing techniques for accurate ultrasonic range measurements, Meas. Sci. Technol. 11 (1) (2000) 45-50.
17. B. Barshan, R. Kuc, Differentiating sonar reflections from corners and planes by employing an intelligent sensor, IEEE Trans. Pattern Anal. Mach. Intell. 12 (6) (1990) 560-569.
18. Panasonic Corp., Ultrasonic ceramic microphones, 12 Blanchard Rd., Burlington, MA, 1989.
19. B. Barshan, A.SW . Sekmen, Radius of curvature estimation and localization of targets using multiple sonar sensors, J. Acoust. Soc. Am. 105 (4) (1999) 2318-2331.
20. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.
21. K.J. Arrow, Social Choice and Individual Values, Wiley, New York, 1951.
22. B. Barshan, A sonar-based mobile robot for bat-like prey capture. Ph.D. Thesis, Yale University, Department of Electrical Engineering, New Haven, CT, December 1991.
23. M. Rosenblatt, Remarks on some non-parametric estimates of a density function, Ann. Math. Stat. 27 (3) (1956) 832-837.
24. E. Fix, J.L. Hodges, Discriminatory analysis, nonparametric discrimination, consistency properties, Technical Report 21-49-004 4, United States Air Force, School of Aviation Medicine, Randolph Field, TX, 1951.
25. M.P. Wand, M.C. Jones, Kernel Smoothing, Chapman and Hall, London, 1995.
26. S.J. Sheather, The performance of six popular bandwidth selection methods on some real data sets, Comput. Stat. 7 (1992) 225-250.
27. D.J. Hand, Discrimination and Classification, Wiley, New York, 1986.
28. B.W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York, 1986.
29. V.K. Rohatgi, An Introduction to Probability Theory and Mathematical Statistics, Wiley, New York, 1976.
30. G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, Wiley, New York, 1992.
31. J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybernet. 3 (1974) 32-57.
32. J.C. Bezdek, Fuzzy mathematics in pattern classification, Ph.D. Thesis, Cornell University, Department of Applied Mathematics, Ithaca, NY, 1973.
33. B.C. Bai, N.H. Farhat, Learning networks for extrapolation and radar target identification, Neural Networks 5 (3) (1992) 507-529.
34. M. Cohen, H. Franco, N. Morgan, D. Rumelhart, V. Abrash, Context-dependent multiple distribution phonetic modelling with MLPs, in: S.J. Hanson, J.D. Cowan, C.L. Giles (Eds.), Advances in Neural Information Processing Systems, Morgan Kaufmann, San Mateo, CA, 1993, pp. 649-657.
35. K.S. Narendra, K. Parthasarathy, Gradient methods for the optimization of dynamic systems containing neural networks, IEEE Trans. Neural Networks 2 (2) (1991) 252-262.
36. M.I. Jordan, R.A. Jacobs, Learning to control an unstable system with forward modeling, in: D.S. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufmann, San Mateo, CA, 1990, pp. 324-331.
37. M. Galicki, H. Witte, J. Dörschel, M. Eiselt, G. Griessbach, Common optimization of adaptive processing units and a neural network during the learning period: application in EEG pattern recognition, Neural Networks 10 (6) (1997) 1153-1163.
38. Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Handwritten digit recognition with a back-propagation network, in: D.S. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufmann, San Mateo, CA, 1990, pp. 396-404.
39. E. AlpaydYn, Multiple networks for function learning, in: Proceedings of IEEE International Conference on Neural Networks, San Francisco, March 1993, pp. 9-14.
40. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagation errors, Nature 323 (1986) 533-536.
41. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, New Jersey, 1994.
42. Y.Q. Chen, D.W. Thomas, M.S. Nixon, Generating-shrinking algorithm for learning arbitrary classification, Neural Networks 7 (9) (1994) 1477-1489.
43. H.M. Ozaktas, Z. Zalevsky, M.A. Kutay, The Fractional Fourier Transform with Applications in Optics and Signal Processing, Wiley, New York, 2001.
44. D. Mendlovic, H.M. Ozaktas, Fourier transforms and their optical implementation: I, J. Opt. Soc. Am. A 10 (9) (1993) 1875-1881.
45. H.M. Ozaktas, D. Mendlovic, Fourier transforms of fractional order and their optical interpretation, Opt. Commun. 101 (3,4) (1993) 163-169.
46. H.M. Ozaktas, D. Mendlovic, Fractional Fourier transforms and their optical implementation: II, J. Opt. Soc. Am. A 10 (12) (1993) 2522-2531.
47. H.M. Ozaktas, D. Mendlovic, Fractional Fourier optics, J. Opt. Soc. Am. A 12 (4) (1995) 743-751.
48. H.M. Ozaktas, B. Barshan, D. Mendlovic, Convolution and fitering in fractional Fourier domains, Opt. Rev. 1 (1) (1994) 15-16.
49. H.M. Ozaktas, B. Barshan, D. Mendlovic, L. Onural, Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms, J. Opt. Soc. Am. A 11 (2) (1994) 547-559.
50. B. Barshan, M.A. Kutay, H.M. Ozaktas, Optimal filtering with linear canonical transformations, Opt. Commun. 135 (1-3) (1997) 32-36.
51. I.SW . Yetik, H.M. Ozaktas, B. Barshan, L. Onural, Perspective projections in the space-frequency plane and fractional Fourier transforms, J. Opt. Soc. Am. A 17 (12) (2000) 2382-2390.
52. C. Candan, M.A. Kutay, H.M. Ozaktas, The discrete fractional Fourier transform, IEEE Trans. Signal Process. 48 (5) (2000) 1329-1337.
53. R.N. Bracewell, The Hartley Transform, Oxford University Press, New York, 1986.
54. R.C. Gonzalez, P. Wintz, Digital Image Processing, Addison-Wesley, Reading, MA, 1987.
55. D.F. Marshall, W.K. Jenkins, J.J. Murphy, The use of orthogonal transforms for improving performance of adaptive filters, IEEE Trans. Circuits Systems 36 (1989) 474-484.
56. C.K. Chui, An Introduction to Wavelets, Academic Press, San Diego, CA, 1992.
57. S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) (1989) 674-693.
58. T. Kohonen, Self-organized formation of topologically correct feature maps, Biol. Cybernet. 43 (1) (1982) 59-69.
59. B. Barshan, B. Ayrulu, Fractional Fourier transform pre-processing for neural networks and its application to object recognition, Neural Networks 15 (1) (2002) 131-140.