M. Kumar∗ and D.P. Garg∗∗


  1. [1] D.L. Hall & J. Llinas, Handbook of multisensor data fusion(Boca Raton, FL: CRC Press, 2001).
  2. [2] R.R. Brooks & S.S. Iyengar, Multi-sensor fusion: Fundamen-tals and applications with software (Upper Saddle River, NJ:Prentice-Hall, Inc., 1998).
  3. [3] R. Luo & K. Su, A review of high-level multisensor fusion:Approaches and applications, Proc. IEEE Int. Conf. on Multi-sensor Fusion and Integration for Intelligent Systems, Taipei,Taiwan, 1999, 25–31.
  4. [4] S.J. Press, Bayesian statistics: Principles, models and appli-cations (John Wiley and Sons, 1989).
  5. [5] J.J. Clark & A.L. Yuille, Data fusion for sensory informationprocessing systems (Boston, MA: Kluwer Academic Publica-tions, 1990).
  6. [6] M. Kumar, D. Garg, & R. Zachery, A method for judiciousfusion of inconsistent multiple sensor data, IEEE SensorsJournal, 7(5), 2007, 723–733.
  7. [7] G. Shafer, A mathematical theory of evidence (Princeton, NJ:Princeton University Press, 1976).
  8. [8] A.P. Demspter, A generalization of bayesian inference, Journalof Royal Statistical Society, B, 30(2), 1968, 205–247.
  9. [9] R. McKendall & M. Mintz, Data fusion techniques using robuststatistics, in M.A. Abidi & R.A. Gonzalez (Eds.), Data fusionin robotics and machine intelligence (New York, NY: AcademicPress, 1992), 211–244.
  10. [10] P.S. Maybeck, Stochastic models, estimation and control, Vol-ume 1 (New York, NY: Academic Press, 1979).
  11. [11] R.E. Kalman, A new approach to linear filtering and predictionproblems, Transactions of the ASME – Journal of BasicEngineering, 82, Ser. D, 1960, 35–45.
  12. [12] S.L. Sun, Y.L. Shen, & J. Ma, Optimal fusion reduced-orderkalman estimators for discrete-time stochastic singular systems,Control and Intelligent Systems, 36(1), 2008, 1–9.
  13. [13] J.Z. Sasiadek, Sensor fusion, Annual Reviews in Control, 26,2002, 203–228.
  14. [14] S.L. Sun, Optimal fusion distributed filter for discrete multi-channel ARMA signals, Control and Intelligent Systems, 34(1),2006, 80–86.
  15. [15] R.R. Yager & L.A. Zadeh (Eds.), An introduction to fuzzylogic applications in intelligent systems (Boston, MA: KluwerAcademic Publishers, 1991).
  16. [16] J.K. Klir & B. Yuan, Fuzzy sets and fuzzy logic: Theory andapplications (Upper Saddle River, NJ: Prentice-Hall Press,1995).75
  17. [17] D. Garg, S. Ananthraman, & S. Prabhu, Neural networkapplications, in J.G. Webster (Ed.), Wiley Encyclopedia ofElectrical and Electronic Engineering, 14 (New York: JohnWiley, 1999), 255–265.
  18. [18] D. Garg & M. Kumar, Neural controllers, in B. Wah(Ed.), Wiley Encyclopedia of Computer Science and En-gineering (Hoboken: John Wiley & Sons, Inc., 2008)
  19. [19] L. Chin, Application of neural networks in target trackingdata fusion, IEEE Transactions on Aerospace and ElectronicSystems, 30(1), 1994, 281–287.
  20. [20] A. Jain, C.W. de Silva, & Q.M.J. Wu, Intelligent fusion ofsensor data for product quality assessment in a fishcuttingmachine, Control and Intelligent Systems, 32(2), 2004, 89–98.
  21. [21] A. Mahajan, K. Wang, & P.K. Ray, Multisensor integration andfusion model that uses a fuzzy inference system, IEEE/ASMETransactions on Mechatronics, 6(2), 2001, 188–196.
  22. [22] R.N.P. Singh & W.H. Bailey, Fuzzy logic applications tomultisensor-multitarget correlation, IEEE Transactions onAerospace and Electronic Systems, 33(3), 1997, 752–769.
  23. [23] J.A. Stover, D.L. Hall, & R.E. Gibson, A fuzzy-logic architec-ture for autonomous multisensor data fusion, IEEE Transac-tions on Industrial Electronics, 43(3), 1996, 403–410.
  24. [24] H.L. Larsen & R.R. Yager, A framework for fuzzy recogni-tion technology, IEEE Transactions on Systems, Man, andCybernetics, Part C Applications and Reviews, 30(1), 2000,65–76.
  25. [25] T. Bayes, An essay towards solving a problem in doctrine ofchances, Philosophical Transactions, 53, 1763, 370–418.
  26. [26] J. Manyika & H. Durrant-Whyte, Data fusion and sensormanagement: A decentralized information-theoretic approach(New York, NY: Ellis Howard Limited, 1994).
  27. [27] M. Kumar, D. Garg, & R. Zachery, Stochastic adaptive sensormodeling and data fusion, Proc. of SPIE Conference on Sensorsand Smart Structures Technologies for Civil, Mechanical, andAerospace Systems, San Diego, CA, February–March 2006,61740C-1 to 61740C-11.
  28. [28] M. Kumar, D. Garg, & R. Zachery, Intelligent sensor modelingand data fusion via neural network and maximum likelihoodestimation, IMECE2005-80972, Proceedings of the ASME In-ternational Mechanical Engineering Congress and Exposition,Orlando, FL, November 2005.
  29. [29] H.F. Durrant-Whyte, Integration, coordination and control ofmulti-sensor robot systems (Norwell, MA: Kluwer AcademicPublishers, 1988).
  30. [30] J. Porrill, Optimal combination and constraints for geometricalsensor data, The International Journal of Robotics Research,1988, 66–77.
  31. [31] D.E. Rumelhart & J.L. McClelland, Explorations in paralleldistributed processing: A handbook of models, programs, andexercises (Cambridge, MA: MIT Press, 1988).
  32. [32] S.S. Haykin, Neural networks: A comprehensive foundation(Upper Saddle River, NJ: Prentice Hall Press, 1998).
  33. [33] M. Kumar & D. Garg, Neuro-fuzzy controller applied tomultiple robot cooperative control, International Journal ofIndustrial Robot, 32(3), 2005, 234–239.
  34. [34] K. Hornik, M. Stinchombe, & H. White, Multilayer feedforwardnetwork are universal approximators, Neural Networks, 2, 1989,359–366.
  35. [35] B. Irie & S. Miyake, Capabilities of three-layered perceptrons,Proc. of IEEE Int. Conf. on Neural Networks, California, USA,24–27 July 1988, 641–648.
  36. [36] D. Garg & M. Kumar, Optimization techniques applied to mul-tiple manipulators for path planning and torque minimization,Journal for Engineering Applications of Artificial Intelligence,15, 2002, 241–252.
  37. [37] A. Elfes, Multi-source spatial data fusion using bayesian rea-soning, in M.A. Abidi & R.A. Gonzalez (Eds.), Data fusion inrobotics and machine intelligence (New York, NY: AcademicPress, 1992).
  38. [38] M. Kumar & D. Garg, Intelligent multi sensor fusion techniquesin flexible manufacturing workcells, Proc. of American ControlConf., Boston, MA, 2004, 5375–5380.
  39. [39] R. HoseinNezhad, B. Moshiri, & M.R. Asharif, Sensor fusion forultrasonic and laser arrays in mobile robotics: A comparativestudy of fuzzy, dempster and bayesian approaches, Proc. ofIEEE Conf. on Sensors, 2, 12–14 June 2002, 1682–1689.
  40. [40] M. Kumar & D. Garg, Three-dimensional occupancy gridwith the use of vision and proximity sensors in a roboticworkcell, IMECE2004-59593, Proc. of the ASME InternationalMechanical Engineering Congress and Exposition, Anaheim,CA, 14–19 November 2004.
  41. [41] M. Kumar, D. Garg, & R. Zachery, Multi-sensor fusion strategyto obtain 3-D occupancy profile, Proc. of the 31st Annual Conf.of the IEEE Industrial Electronics Society (IECON), Raleigh,NC, November 2005, 2083–2088.
  42. [42] J.O. Berger, Statistical decision theory and Bayesian analysis(Berlin: Springer-Verlag, 1985).
  43. [43] D. Garg & M. Kumar, Object classification via stereo vision in aflexible manufacturing work cell, Proc. of the 10th InternationalConference on Mechatronics and Machine Vision in Practice,Perth, Western Australia, 9–11 December 2003.
  44. [44] Z. Zhang, R. Deriche, O. Faugeras, & Q.T. Luong, A robusttechnique for matching two uncalibrated images through therecovery of the unknown epipolar geometry, Artificial Intelli-gence Journal, 78, 1995, 87–119.

Important Links:

Go Back