Multi-sensor data fusion with MATLAB®

Multi-Sensor Data Fusion with MATLAB

Volumn , Issue , 2009, Pages 1-535

  Raol, Jitendra R ^a  

^a M S RAMAIAH INSTITUTE OF TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84888340168     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (210)

1. Wilfried, E. 2002. An Introduction to Sensor Fusion. Research Report 47/2001. Institute fur Technische Informatik, Vienna University of Technology, Austria.
2. Dasarathy, B. V. 2001. Information fusion—What, where, why, when, and how? (Editorial) Inf Fusion 2: 75–6.
3. Bar-Shalom, Y., and X.-R. Li. 1995. Multi-target Multi-sensor Tracking (Principles and Techniques), Storrs, CT: YBS.
4. Waltz E., and J. Llinas. 1990. Multisensor data fusion. Boston: Artech House Publishers.
5. Hall, D. L. 1992. Mathematical techniques in multisensor data fusion. Norwood, MA: Artech House.
6. Abidi, M. A., and R. C. Gonzalez, eds. 1992. Data fusion in robotics and machine intelligence. USA: Academic Press.
7. Hager, G. D. 1990. Task-directed sensor fusion and planning (A Computational Approach). Norwell, MA: Kluwer Academic Publishers.
8. Klein, L. A. 2004. Sensor and data fusion: A tool for information assessment and decision making. Bellingham, WA: SPIE Press.
9. Harris, C., X. Hong, and Q. Gan. 2002. Adaptive modelling, estimation and fusion from data: A neurofuzzy approach. Berlin: Springer.
10. Manyika, J., and H. Durrant-White. 1994. Data fusion and sensor management: A decentralized information—Theoretic approach. Ellis Horwood Series.
11. Dasarathy, B. V. 1994. Decision fusion. USA: Computer Society Press.
12. Mitchell, H. B. 2007. Multi-sensor data fusion. Berlin: Springer.
13. Varshney, P. K. 1997. Distributed detection and data fusion. Berlin: Springer.
14. Clark, J. J. 1990. Data fusion for sensory information processing systems. Norwell, MA: Kluwer.
15. Goodman, I. R., R. P. S. Mahler, and H. T. Nguyen. 1997. Mathematics of data fusion. Norwell, MA: Kluwer.
16. Wilfried, E. 2002. An introduction to sensor fusion. Research report 47/2001. Austria: Institute fur Technische Informatik, Vienna University of Technology.
17. Abidi, M. A., and R. C. Gonzalez, eds. 1992. Data fusion in robotics and machine intelligence. USA: Academic Press.
18. Dasarathy, B. V. 2001. Information fusion—what, where, why, when, and how? (Editorial) Inf Fusion 2:75−6.
19. Hall, D. L. 1992. Mathematical techniques in multi-sensor data fusion. Norwood, MA: Artech House.
20. Grossmann, P. 1998. Multisensor data fusion. GEC J Technol 15:27-37.
21. Nahin, P. J., and J. L. Pokoski. 1980. NCTR plus sensor fusion equals IFFN or can two plus two equal five? IEEE Trans Aerosp Electron Syst 16:320−37.
22. Dasarathy, B. V. 2000. More the merrier … or is it? Sensor suite augmentation benefits assessment. In: Proceedings of the 3rd International Conference on Information Fusion, 2: 20−25. Paris, France.
23. Mutambra, A. G. O. 1998. Decentralized estimation and control for multisensor systems. Florida, USA: CRC Press.
24. Dasarathy, B. V. 2000. Industrial applications of multi-sensor information fusion. Proceedings of IEEE International Conference on Industrial Technology, Goa, India (Invited Plenary Session Talk).
25. Markin, M., C. Harris, M. Bernhardt, J. Austin, M. Bedworth, P. Greenway, R. Johnston, et al. 1997. Technology foresight on data fusion and data processing. Publication of The Royal Aeronautical Society.
26. Shulsky, A. N. 1991. Silent warfare: Understanding the world of intelligence. New York: Brassey’s.
27. Boyd, J. R. 1987. A discourse on winning and losing. Maxwell AFB, Alabama: Unpublished set of briefing slides available at Air University Library.
28. Bedworth, M. D., and J. O’Brien. 1999. The omnibus model: A new architecture for data fusion? In Proceedings of the 2nd International Conference on Information Fusion (FUSION’99).
29. Raol, J. R., G. Girija, and J. Singh. 2004. Modelling and parameter estimation of dynamic systems (IEE control engg,) Vol. 65. London: IEE.
30. Rong Li, X., Y. M. Zhu, and C. Z. Han. Unified optimal linear estimation fusion— Part I: Unified models and fusion rules. In Proceedings of 2000 International Conference on Information Fusion, MoC2.10−MoC2.17, Paris, France.
31. Rong Li, X., and J. Wang. 2000. Unified optimal linear estimation fusion— Part II: Discussions and examples. In Proceedings of 2000 International conference on information fusion, MoC2.10−MoC2.17, Paris, France.
32. Rong Li, X., and P. Zhang. Optimal linear estimation fusion—Part III: Cross-correlation of local estimation errors. In Proceedings of 2000 International Conference on Information Fusion, WeB1.11–WeB1.18, Montreal, QC, Canada.
33. Rong Li, X., and K. Zhang. Optimal linear estimation fusion—Part IV: Optimality and efficiency of distributed fusion. In Proceedings of 2000 International Conference on Information Fusion, WeB1.19–WeB1.26, Montreal, QC, Canada.
34. Rong Li, X., K. Zhang, J. Zhao, and Y. M. Zhu. 2002. Optimal linear estimation fusion—Part V: Relationships. Proceedings of 5th International Conf. Information Fusion, 497−504, Annapolis, MD, USA.
35. Dempster, A. P. 1967. Upper and lower probabilities induced by a multivalued mapping. Annu Math Stat 38:325−39.
36. Shafer, G. 1976. A mathematical theory of evidence. Princeton, NJ: Princeton University Press.
37. Provan, G. M. 1992. The validity of Dempster–Shafer belief functions. Int J Approx Reason 6:389−99.
38. Crowley, J. L., and Y. Demazeu. 1993. Principles and techniques of sensor data fusion. Signal Processing, 32(2):5–27.
39. Challa, S., and D. Koks. 2004. Bayesian and Dempster–Shafer fusion. Sadhana 29(2):145−76.
40. Kadambe, S., and C. Daniell. Sensor/data fusion based on value of information. In Proceedings of the Sixth International Conference on Information Fusion, 1:25–32.
41. Leung, L. W., B. King, and V. Vohora. 2001. Comparison of image data fusion techniques using entropy and INI. In Proceedings of the 22nd Asian conference on remote sensing, Singapore, November 5–9. http://www.pubs.drdcgc.ca/inbasket/Laidman.071121_1403.toronto_CR_2007_066.pdf. Accessed December 2008.
42. Iyengar, S. S., and R. R. Brooks, eds. 2005. Distributed sensor networks. Boca Raton, FL: Chapman & Hall/CRC Press.
43. Aggarwal, J. K., ed. 1993. Multisensor fusion for computer vision, NATO ASI Series. Berlin: Springer-Verlag.
44. Hu, H., and J. Q. Gan. 2005. Sensors and data fusion algorithms in mobile robotics. TR CSM-422, Dept. of Comp. Science, University of Essex, UK.
45. Vaghi, A. 2003–2004. Sensor management by a graphical model approach. Thesis, Politecnico di Milano. http://www.fred.mems.duke.edu/posters/Alberto_GMA_thesis.pdf. Accessed December 2008.
46. Manyika, J., and H. Durrant-Whyte. 1994. Data fusion and sensor management— decentralized information theoretic approach. New York: Ellis Horwood Series.
47. Maybeck, P. S. 1982. Stochastic models, estimation and control, Vol. 2. New York: Academic Press.
48. Bierman, G. J. 1977. Factorization methods for discrete sequential estimation. New York: Academic Press.
49. Julier, S. J., and J. K. Uhlmann. 1997. A new extension of the Kalman filter to non-linear systems. In Proceedings of AeroSense, 11th International Symposium Aerospace/Defense Sensing, Simulation and Controls. Bellingham, WA: SPIE.
50. Julier, S. J., and J. K. Uhlmann. 2004. Unscented filtering and non-linear estimation. Proc IEEE 92(3):401−22.
51. Kashyap, S. K., and J. R. Raol. 2006. Evaluation of derivative free Kalman filter and fusion in non-linear estimation. Ottawa, ON, Canada: IEEE Canadian Conference on Electrical and Computer Engineering.
52. Kashyap, S. K., and J. R. Raol. 2008. Evaluation of derivative free Kalman filter for non-linear state-parameter estimation and fusion. J Aeronaut Soc India 60(2):101−14.
53. Bar Shalom, Y., and X. R. Li. 1995. Multitarget–multisensor tracking: Principles and techniques. Storrs, CT: Academic Press.
54. Blackman, S. S. 1986. Multiple-target tracking with radar applications. Norwood, MA: Artech House.
55. Naidu, V. P. S., G. Girija, and J. R. Raol. 2005. Data association and fusion algorithms for tracking in presence of measurement loss. J Inst Eng I AS 86:17−28.
56. Chang, K. C., and Y. Bar-Shalom. 1994. FUSEDAT: A software package for fusion and data association and tracking with multiple sensors. In Proceedings of the SPIE conference on signal and data processing of small targets, Orlando, FL.
57. Mori, S., W. H. Barker, C.-Y. Chong, and K.-C. Chang. 2002. Track association and track fusion with nondeterministic target dynamics. IEEE Trans Aerosp Electron Syst 38(2):659–668.
58. Bar-Shalom, Y., and X.-R. Li. 1993. Estimation and tracking: Principles, techniques, and software. Boston, MA: Artech House.
59. Haimovich, A. M., J. Yosko, R. J. Greenberg, and M. A. Parisi. Fusion of sensors with dissimilar measurements/tracking accuracies. IEEE Log No. T-AES/29/1/02258.
60. Shanthakumar, N., and G. Girija. 2007. Measurement level and state-vector data fusion implementations. Personal communications and personal notes. Flight Mechanics and Control Division, National Aerospace Laboratories, Bangalore.
61. Kashyap, S. K., N. Shanthakumar, G. Girija, and J. R. Raol. 2003. Sensor data characterization and fusion for target tracking applications. International Radar Symposium India (IRSI), 3–5 December. Bangalore.
62. Zhou, Y., H. Leung, and M. Blanchette. 1999. Sensor alignment with earthcentered earth-fixed co-ordinate system. IEE Trans AES 35(2):410−8.
63. Raol, J. R., and G. Girija. 2002. Sensor data fusion algorithms using squareroot information filtering. IEE Proc Radar Sonar Navig 149(2):89−96.
64. Bar-Shalom, Y. and X.-R. Li. 1993. Estimation and tracking: Principles, techniques and software. Boston: Artech House.
65. Blom, H. A. P., and Y. Bar-Shalom. 1988. The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans Autom Control 33:780−3.
66. Mazor, E., A. Averbuch, Y. Bar-Shalom, and J. Dayan. 1998. Interacting multiple model methods in target tracking: A survey. IEEE Trans Aerosp Electron Syst 34:103−23.
67. Naidu, V. P. S., G. Girija, and N. Santhakumar. 2007. Three model IMM-EKF for tracking targets executing evasive maneuvers. AIAA-66928, 45th AIAA conference on Aerospace Sciences, Reno, USA.
68. Simeonova, L., and T. Semerdjiev. 2002. Specific features of IMM tracking filter design. Inf Secur Int J 9:154−65.
69. Schulz, D., W. Burgard, D. Fox, and A. B. Cremers. People tracking with mobile robots using sample-based joint probabilistic data association filters. http://www.informatik.unibonn.de/~schulz/articles/people-tracking-ijrr-03.pdf.
70. Challa, S., R. J. Evans, and X. Wang. 2003. A Bayesian solution and its approximations to out-of-sequence measurement problems. Inf Fusion 4(3):185−99.
71. Paik, B. S., and J. H. Oh. 2000. Gain fusion algorithm for decentralized parallel Kalman filters. IEE Proc Control Theor App 147(1):97–103.
72. Shanthakumar, N., G. Girija, and J. R. Raol. 2001. Performance of Kalman and gain fusion algorithms for sensor data fusion with measurement loss. International Radar Symposium India (IRSI), Bangalore.
73. Green, M., and D. J. N. Limebeer. 1995. Linear robust control. Englewood Cliffs, NJ: Prentice Hall.
74. Hassibi, B., A. H. Sayad, and T. Kailath. 1996. Linear estimation in Krein spaces—part II: Applications. IEEE Trans Autom Control 41(1):34–49.
75. ∞ state estimation. IEE Proc Control Theor App 148(6):502−8.
76. Lee, T. H., W. S. Ra, T. S. Yoon, and J. B. Park. 2004. Robust Kalman filtering via Krein space estimation. IEE Proc Control Theor App 151(1):59−63.
77. Raol, J. R., and F. Ionescu. 2002. Performance of H-Infinity filter-based data fusion algorithm with outliers. 40th AIAA Aerospace Sciences Meeting & Exhibit, 14–17 January 2002, Reno, NV, USA, A02-13598.
78. Ananthasayanam, M. R., A. K. Sarkar, A. Bhattacharya, P. Tiwari, and P. Vorha. 2005. Nonlinear observer state estimation from seeker measurements and seeker-radar measurements fusion, Paper No AIAA-2005-6066-CP.
79. Zarchan, P. 1997. Tactical and strategic missile guidance, Vol. 176, 3rd ed. Progress in Aeronautics and Astronautics.
80. Bar-Shalom, Y., and K. C. Chang. 1989. Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm. IEEE Trans Aerosp Electron Syst 26(2):296–300.
81. Mehrotra, K., and P. Mahapatra. 1997. A jerk model for tracking highly maneuvering targets. IEEE Trans Aerosp Electron Syst 33(4):1094−105.
82. Vora, P., A. Bhattacharyya, M. Jyothi, P. K. Tiwari, and R. N. Bhattacharjee. 2004. RF Seeker modeling and Seeker filter design. Hyderabad, India: National Workshop on Tactical Missile Guidance.
83. Kashyap, S. K., N. Shanthakumar, V. P. S. Naidu, G. Girija, and J. R. Raol. 2007. State estimation for Pursuer guidance using interacting multiple model based augmented extended Kalman filter. International Radar Symposium, India, (IRSI) Bangalore.
84. Banerjee, P., and R. Appavu Raj. 2000. Multi-sensor data fusion strategies for real-time application in test and evaluation of rockets/missiles system. IEEE International Conference on Industrial Technology (ICIT), Goa, India.
85. Girija, G., J. R. Raol, R. Appavu Raj, and S. Kashyap. 2000. Tracking filter and multi-sensor data fusion. In Special issue: Advances in Modeling, System Identification, and Parameter Estimation, eds. J. R. Raol and N. K. Sinha, Sadhana 25(2):159−67.
86. Kashyap, S. K., V. P. S. Naidu, J. Singh, G. Girija, and J. R. Raol. 2006. Tracking of multiple targets using interactive multiple model and data association filter. J Aerosp Sci Technol Aeronaut Soc India 58(1):65−74.
87. Lerro, D., and Y. Bar-Shalom. 1993. Tracking with de-biased consistent converted measurements versus EKF. IEEE Trans Aerosp Electron Syst 29(3):1015–1022.
88. Park, S.-T., and J. Gyu Lee. 2001. Improved Kalman filter design for three-dimension radar tracking. IEEE Trans Aerosp Electron Syst 37(2):727–739.
89. Blackman, S. 1986. Multiple target tracking with radar applications. Dedham, MA: Artech House.
90. Kashyap, S. K., G. Girija, and J. R. Raol. 2003. Evaluation of converted measurement-and modified extended-Kalman filters for target tracking. AIAA Conference and Exhibit, Guidance, Navigation and Control, Texas, US, 11–17th August.
91. Kashyap, S. K., G. Girija, and J. R. Raol. 2006. Converted measurement-and modified extended-Kalman filter for target tracking. Def Sci J 56(5):679−92.
92. Peter, S. 1979. Maybeck, stochastic models, estimation and control, Vol. 1. New York: Academic Press.
93. Shanthakumar, N., and T. Jann. 2004. Estimation of attitudes from a low cost miniaturized inertial platform using Kalman filter based sensor fusion algorithm. In Sadhana, special issue on multi-sensor data fusion, ed. J. R. Raol, IAS, Bangalore.
94. Doherr, K. F., and T. Jann. 1997. Test vehicle ALEX for low cost autonomous parafoil landing experiments. 14th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar, USA, AIAA-97-1543.
95. Jann, T., K. F. Doherr, and W. Gockel. Parafoil test vehicle ALEX-further development and flight test results, AIAA-99-1751.
96. Jann, T. 2001. Aerodynamic model identification and GNC design for the Parafoil-Load system ALEX. Proccedings of AIAA Aerodynamic Systems Decelerator Conference, USA, AIAA-2001-2015.
97. Valvano, J. W. 2000. Embedded microcomputer systems. USA: Brooks/Cole.
98. Collinson, R. P. G. 1998. Introduction to avionics. UK: Chapman and Hall.
99. Anonymous. 2002. ImageCraft C compiler and development environment for motorola HC12 manual, version 6.
100. Anonymous. 1999. HC12Compact hardware manual, Version 1.0.
101. Saha, R. K. 1996. Effect of common process noise on two-sensor track fusion. J Guid Control Dyn 19(4):829–835.
102. Goodman, I. R., R. P. S. Mahler, and T. Hung. 1997. Mathematics of data fusion. Norwell, MA: Kluwer.
103. Naidu, V. P. S., G. Girija, and J. R. Raol. 2003. Evaluation of data association and fusion algorithms for tracking in the presence of measurement loss. AIAA Paper No. 5733, Austin (USA).
104. Girija, G., C. Zorn, and A. Koch. 2005. Multi sensor data fusion for sensor failure detection and health monitoring. AIAA Guidance, Navigation and Control Conference and Exhibit, 15–18 Aug, AIAA-2005-5843, San Francisco, CA.
105. Gilula, Z., R. E. Mcculloch, and P. E. Rossi. 2006. A direct approach to data fusion. J Mark Res 43(1):73–83.
106. Girija, G., and J. R. Raol. 2001. Comparison of methods for association and fusion of multi-sensor data for tracking applications. AIAA Guidance, Navigation, and Control Conference and Exhibit. Montreal, Quebec, Canada.
107. Zadeh, L. A. 1965. Fuzzy sets. Inf Control 8:338–53.
108. King, R. E. 1999. Computational intelligence in control engineering. New York: Marcel Dekker.
109. Passino, K. M., and S. Yurkovich. 1998. Fuzzy control. Menlo Park, CA: Addison-Wesley Longman.
110. Kosko, B. 1997. Fuzzy engineering. Upper Saddle River, NJ: Prentice Hall.
111. Kashyap, S. K., J. R. Raol, and A. V. Patel. 2008. Evaluation of fuzzy implications and intuitive criteria of GMP and GMT. In Foundations of generic optimization, Vol. 2: Applications of fuzzy control, genetic algorithms and neural networks (Mathematical modelling: Theory and applications), pp. 313–386, ed. R. Lowen and A. Verschoren. New York: Springer.
112. Kashyap, S. K., and J. R. Raol. 2007. Interpretation and unification of fuzzy set operations and implications. J Syst Soc India 16(1):26–33.
113. Kashyap, S. K. 2008. Decision fusion using fuzzy logic. Doctoral thesis, University of Mysore, Mysore, India.
114. Li-Xin, W. 1994. Adaptive fuzzy systems and control, design and stability analysis. Englewood Cliffs, NJ: Prentice Hall.
115. Raol, J. R., and J. Singh. 2009. Flight mechanics modeling and analysis. Boca Raton, FL: CRC Press.
116. Cho, S. B., and J. H. Kim. 1995. Multiple network fusion using fuzzy logic. IEEE Trans Neural Netw 6(2):497–501.
117. Lau, C. W., B. Ma, H. M. Meng, Y. S. Moon, and Y. Yam. 2004. Fuzzy logic decision fusion in a multimodal biometric system. http://www.se.cuhk.eduhk/hccl/publications/pub/lau_icslp2004.pdf (accessed December 2008).
118. Wik, M. W. 2006. A three processes effects model based on the meaning of information. 11th ICCRTS Coalition Command and Control in the Networked Era, September 26–28, 2006. http://www.dodccrp.org/events/11th_ICCRTS/html/presentations/018.pdf (accessed December 2008).
119. Crowley, J. L., and Y. Demazeu. 1993. Principles and techniques of sensor data fusion. LIFIA (IMAG), France. http://www-prima.imag.fr/Prima/Homepages/jlc/papers/SigProc-Fusion.pdf (accessed July 2008).
120. Kant, E. 1958. Critique of pure reason, translated by N. Kemp Smith. New York: Random House (original work published in 1781).
121. Abidi, M. A., and R. C. Gonzalez, eds. 1992. Data fusion in robotics and machine intelligence. USA: Academic Press.
122. Hall, D. L. 1992. Mathematical techniques in multi-sensor data fusion. Norwood, MA: Artech House.
123. Chao, W., Q. Jishuang, and L. Zhi. Data fusion, the core technology for future on-board data processing system. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China.
124. Klein, L. A. 2004. Sensor and data fusion: A tool for information assessment and decision making. Washington, DC: SPIE Press.
125. Egfin Nirmala, D., V. Vaidehi, S. Indira Gandhi. 2005. Data fusion using fuzzy logic for multi target tracking. In Proceedings of International Radar Symposium India, pp. 75–80.
126. Kashyap, S. K., and J. R. Raol. 2008. Fuzzy logic applications for filtering and fusion for target tracking. Def Sci J 58:120–35.
127. Ivansson, J. 2002. Situation assessment in a stochastic environment using Bayesian networks. Master’s thesis, Division of Automatic Control, Department of Electrical Engineering, Linkoping University.
128. Narayana Rao, P., S. K. Kashyap, and G. Girija. 2008. Situation assessment in air combat: A fuzzy-Bayesian hybrid approach. In Proceedings of the International Conference on Aerospace Science and Technology, NAL, Bangalore, India.
129. Endsley, M. R. 1993. A survey of situation awareness requirements in air-to-air combat fighters. Int J Aviat Psychol 3:157–68.
130. Neapolitan, R. E. 1990. Probabilistic reasoning in expert systems. New York: John Wiley & Sons.
131. Pearl, J. 1988. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Francisco, CA: Morgan Kaufmann Publishers, Inc.
132. HUGIN Expert, http//: www.HUGIN.dk (accessed December 2008).
133. Hajiyev, Ch. M., and F. Caliskan. 2001. Integrated sensor/actuator FDI and reconfigurable control for fault tolerant flight control system design. Aeronaut J 105:525–33.
134. Hajiyev, C., and F. Caliskan. 2003. Fault diagnosis and reconfiguration in flight control systems. Boston: Kluwer Academic Publishers.
135. Eva Wu, N., Y. Zhang, and K. Zhou. 2000. Detection, estimation, and accommodation of loss of control effectiveness. Int J Adapt Control Process 14:775–95.
136. Savanur, S. R., S. K. Kashyap, and J. R. Raol. 2008. Adaptive Neuro-Fuzzy based control surface fault detection and reconfiguration. In Proceedings of the International Conference on Aerospace Science and Technology, Bangalore, India.
137. McLean, D. 1990. Automatic flight control systems. London: Prentice Hall International.
138. Raol, J. R., G. Girija, and J. Singh. 2004. Modelling and parameter estimation of dynamic systems, IEE Control Engineering. Series Book. Vol. 65. London: IEE.
139. Hewer, G. A., R. D. Martin, and J. Zeh. 1987. Robust preprocessing for Kalman filtering of glint noise. IEEE Trans Aerosp Electron Syst 23:120–8.
140. Wu, W. 1993. Target tracking with glint noise. IEEE Trans Aerosp Electron Syst 29:174–85.
141. Sundararajan, N., P. Saratchandran, and Y. W. Lu. 2001. Radial basis function neural networks with sequential learning—MRAN and its applications, Progress in neural processing 11. Singapore: World Scientific Publishing.
142. Lu, Y., N. Sundararajan, and P. Saratchandran. 1997. A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Comput 9:461–78.
143. Hong, G. 2007. Image fusion, image registration, and radiometric normalization for high resolution image processing. PhD Thesis, TR No. 247, Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada, p. 198.
144. Goshtasby, A. A. and S. Nikolov, eds. 2007. Image fusion: Advances in the state of the art. Inf Fusion 8:114–8.
145. Zitova, B., and J. Flusser. 2003. Image registration methods: A survey. Image Vis Comput 21:977–1000.
146. Gonzalez, R. C., and R. E. Woods. 1993. Digital image Processing. New York: Addison-Wesley Inc.
147. Hill, P., N. Canagarajah, and D. Bull. 2002. Image fusion using complex wavelets. BMVC. http://www.bmva.ac.uk/bmvc/2002/papers/88/full_88.pdf (accessed December 2008).
148. nd Asian conference on remote sensing, November 2001, Singapore.
149. Kumar, A., Y. Bar Shalom, and E. Oron. 1995. Precision tracking based on segmentation with optimal layering for imaging sensor. IEEE Trans Pattern Anal Mach Intell 17:182–8.
150. Oron, E., A. Kumar, and Y. Bar Shalom. 1993. Precision tracking with segmentation for imaging sensor. IEEE Trans Aerosp Electron Syst 29:977–87.
151. Waltz, E. and J. L. Llinas. 1990. Multi-sensor data fusion. Boston: Artech House.
152. Naidu, V. P. S., and J. R. Raol. 2008. Pixel-level image fusion using wavelets and principal component analysis—A comparative analysis. Def Sci J 58:338–52.
153. nd Asian conference on remote sensing, November 2001, Singapore.
154. Naidu, V. P. S., G. Girija, and J. R. Raol. 2007. Data fusion for identity estimation and tracking of centroid using imaging sensor data. Def Sci J 57:639–52.
155. Pajares, G., and J. M. de la Cruz. 2004. A wavelet-based image fusion tutorial. Pattern Recognit 37:1855–72.
156. Li, S., J. T. Kwok, and Y. Wang. 2001. Combination of images with diverse focuses using the spatial frequency. Inf Fusion 2:169–76.
157. Eskicioglu, A. M., and P. S. Fisher. 1995. Image quantity measures and their performance. IEEE Trans Commun 43:2959–65.
158. Cover, T. M., and J. A. Thomas. 1991. Elements of information theory. New York: Wiley.
159. Wang, Z., and A. C. Bovik. 2002. A universal image quality index. IEEE Signal Process Lett 9:81–4.
160. Cvejic, N., A. Loza, D. Bull, and N. Cangarajah. 2005. A similarity metric for assessment of image fusion algorithms. Int J Signal Process 2:178–82.
161. Piella, G., and H. Heijmans. 2003. A new quality metric for image fusion. Proceedings of the IEEE International Conference on Image Processing, Barcelona, Spain, pp. 173–6.
162. Jalili-Moghaddam, M. 2005. Real-time multi focus image fusion using discrete wavelet transform and Laplasican pyramid transform. Master’s thesis, Chalmess University of Technology, Goteborg, Sweden.
163. Daubechies, I. 1992. Ten lectures on wavelets. Regional conference series in applied maths, Volume 91. Philadelphia: SIAM.
164. Baltzakis, H., A. Argyros, and P. Trahanias. 2003. Fusion of laser and visual data for robot motion planning and collision avoidance. Mach Vis Appl 15:92–100.
165. Wang, J. G., K. A. Toh, E. Sung, and W. Y. Yau. 2007. A feature-level fusion of appearance and passive depth information for face recognition. In Face recognition, ed. K. Delac, and M. Grgic. Vienna, pp. 537–58, Austria: I-Tech.
166. Ross, A., and G. Rohin. 2005. Feature level fusion using hand and face biometircs. In Proceedings of SPIE conference on biometric technology for human identification II, Vol. 5779, 196–204, Orlando, FL.
167. Chang, K. C., R. K. Saha, and Y. Bar-Shalom. 1997. On optimal track-to-track fusion. IEEE Trans Aerosp Electron Syst 33:1271–6.
168. Cheezum, M. K., W. F. Walker, and W. H. Guilford. 2001. Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys J 81:2378–88.
169. Pratt, W. K. 1974. Correlation techniques of image registration. IEEE Trans Aerosp Electron Syst 10:353–8.
170. Chang, S., and C. P. Grover. 2002. Centroid detection based on optical correlation. Opt Eng 41:2479–86.
171. Romine, J. B., and E. W. Kamen. 1996. Modeling and fusion of radar and imaging sensor data for target tracking. Opt Eng 35:659–72.
172. Naidu, V. P. S., G. Girija, and J. R. Raol. 2005. Target tracking and fusion using imaging sensor and ground based radar data, AIAA guidance, navigation and control conference and exhibit, Paper No. AIAA-2005-5842, San Francisco, CA.
173. Dogancay, K., and A. Hashemi-Sakhtsari. 2005. Target tracking by time difference of arrival using recursive smoothing. Signal Process 85:667–79.
174. Stoica, P., and A. Nehorai. 1989. MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans Acoust Speech Signal Process 37:720–41.
175. Johnson, D. H., and D. E. Dudgeon. 1993. Array signal processing: Concepts and techniques. Prentice Hall Signal Processing Series. Englewood Cliffs, NJ: Prentice Hall.
176. Wijk, O., and H. I. Christensen. 2000. Triangulation-based fusion of sonar data with application in robot pose tracking. IEEE Trans Rob Autom 16:740–52.
177. Dufour, F., and M. Mariton. 1991. Tracking a 3D maneuvering target with passive sensors. IEEE Trans Aerosp Electron Syst 27:725–38.
178. Naidu, V. P. S., and J. R. Raol. 2007. Target tracking with multiacoustic array sensors data. Def Sci J 57(3):289–303.
179. Strobel, N., S. Spors, and R. Rabenstein. 2001. Joint audio-video object localization and tracking. IEEE Signal Process Mag 18:22–31.
180. Naidu, V. P. S., and J. R. Raol. 2008. Fusion of out of focus images using principal component analysis and spatial frequency. J Aerosp Sci Technol 60:216–225.
181. Blum, R. S., and Z. Liu. 2006. Multi-sensor image fusion and its applications. Boca Raton, FL: CRC Press.
182. Harris, C., A. Bailey, and T. Dodd. 1998. Multisensor data fusion in defense and aerospace. J Aerosp Soc 162:229–44.
183. Naidu, V. P. S., M. Mudassar, G. Girija, and J. R. Raol. 2005. Target location and identity estimation and fusion using disparate sensor data, AIAA guidance, navigation and control conference and exhibit, Paper No. AIAA-2005-5841, San Francisco, CA.
184. Sam Ge, S., and F. L. Lewis, eds. 2006. Autonomous mobile robots—Sensing, control, decision making and applications. Control engineering series. Boca Raton, FL: CRC Press.
185. Sam Ge, S., and F. L. Lewis, eds. 2006. Autonomous mobile robots—Sensing, control, decision making and applications (Control Engineering Series). Boca Raton, FL: CRC Press.
186. Vitko, A. 2008. Data fusion, context awareness and fault detection in autonomous robotics, Faculty of Electrical Engineering and Information Technology, Institute of Control and Industrial Informatics, Slovakia.
187. Hu, H., and J. Q. Gan. 2005. Sensors and data fusion algorithms in mobile robotics, Technical report: CSM-422, Department of Computer Science, University of Essex, United Kingdom.
188. Durrant-Whyte, H. 2000. A beginners guide to decentralised data fusion, Australian Centre for Field Robotics, The University of Sydney, Australia.
189. Carvalho, H. S., W. B. Heinzelman, A. L. Murphy, and C. J. N. Coelho. 2003. A general data fusion architecture. In Proceedings of the Sixth International Conference of Information Fusion, 2:1465–72.
190. Neira, J., J. D. Tardos, J. Horn, and G. Schmidt. 1999. Fusing range and intensity images for mobile robot localisation. IEEE Trans Rob Autom 15:76–83.
191. Grandjean, P., and A. R. Robert de Saint Vincent. 1989. 3-D modeling of indoor scenes by fusion of noisy range and stereo data. In Proceedings of the IEEE International Conference in Robotics and Automation, 681–687, Scottsdale, AZ.
192. Julier, S. J., T. Bailey, and J. K. Uhlmann. 2006. Using exponential mixture models for suboptimal distributed data fusion. In Proceedings of the Nonlinear Statistical Signal Processing Workshop, 160–163. UK: IEEE.
193. Conner, D.C., P. R. Kedrowski, and C. F. Reinholtz. 2001. Multiple camera, laser rangefinder, and encoder data fusion for navigation of a differentially steered 3-wheeled autonomous vehicle. In Proceedings of SPIE, 4195:76–83, Bellingham, WA.
194. Raheja, D., J. Llinas, R. Nagi, and C. Romanowski. 2006. Data fusion/data mining-based architecture for condition based maintenance. Int J Prod Res 44:2869–87.
195. Nairac, A., N. Townsend, R. Carr, S. King, P. Cowley, and L. Tarassenko. 1999. A system for the analysis of jet engine vibration data. Integr Comput Aided Eng 6:53–66.
196. Hayton, P., S. Utete, D. King, S. King, P. Anuzis, and L. Tarassenko. 2007. Static and dynamic novelty detection methods for jet engine condition monitoring. In Philosophical Transactions of the Royal Society Part A. Special Issue on Structural Health Monitoring, ed. K. Worden and C. Farrar, 365:493–514. London: The Royal Society.
197. Vogt, D., and V. Z. Brink. 2007. The Azisa standard for mine sensing and control. Presentation at Automation in Mining Conference.
198. Hayes, B. 2008. The Britney Spears problem. Am Sci 96:274–9.
199. Ho, Y.-C. 1994. Heuristics, rules of thumb, and the 80/20 proposition. IEEE Trans Automat Contr 39:1025–7.
200. Utete, S. W., B. Barshan, and B. Ayrulu. 1999. Voting as validation in robot
201. programming. Int J Robot Res 18:401–13.
202. Krause, P., and D. Clark. 1993. Representing uncertain knowledge: An artificial intelligence approach. Oxford: Intellect Books.
203. Athans, M. 1987. Command and control (C2) theory: A challenge to control science. IEEE Trans Automat Control 32:286–93.
204. Utete, S. 1994. Network Management in Decentralised Sensing Systems, PhD Thesis, Department of Engineering Science, University of Oxford, UK.
205. Grime, S. 1992. Communication in Decentralised Sensing Architectures. PhD Thesis, Department of Engineering Science, University of Oxford, UK.
206. Grime, S., and H. F. Durrant-Whyte. 1994. Data fusion in decentralised sensor networks. Control Eng Pract 2:849–63.
207. P. Rosenstiehl, J. R. Fiksel, and A. Holliger. 1972. Intelligent graphs: networks of finite automata capable of solving graph problems. In Graph theory and computing, ed. R. Read. London: Academic Press.
208. Bollobás, B. 1978. Extremal graph theory. London: Academic Press.
209. AcuMine products. 2008. http://www.acumine.com/_Products/products.php (accessed January 2009).
210. Ehrich Leonard, N., D. A. Paley, F. Lekien, R. Sepulchre, D. M. Fratantoni, and R. E. Davis. 2007. Collective motion, sensor networks, and ocean sampling. Proc IEEE 95.