Concurrent multi-target localization, data association, and navigation for a swarm of flying sensors

Information Fusion

Volumn 8, Issue 3, 2007, Pages 316-330

  Deming, Ross W ^a   Perlovsky, Leonid I ^a  

^a AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

Concurrent data association and tracking; Dynamic logic; Expectation maximization; Maximum likelihood; Mixture model; Photogrammetry; Sensor fusion; Unmanned aerial vehicles

Indexed keywords

CORRELATION THEORY; MAXIMUM LIKELIHOOD; PHOTOGRAMMETRY; SENSOR DATA FUSION; TARGET TRACKING; UNMANNED AERIAL VEHICLES (UAV);

CONCURRENT DATA ASSOCIATION AND TRACKING; DYNAMIC LOGIC; EXPECTATION MAXIMIZATION; MIXTURE MODEL; SENSOR FUSION;

AUTOMATIC TARGET RECOGNITION;

EID: 34047184709     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2005.11.001     Document Type: Article

References (44)

1. Perlovsky L.I. Neural Networks and Intellect: Using Model-based Concepts (2001), Oxford University Press, New York, NY
2. Perlovsky L.I., Schoendorf W.H., Garvin L.C., Chang W., and Monti J. Development of concurrent classification and tracking. J. Underwater Acoust. 47 1 (1997) 202-210
3. Perlovsky L.I. Cramer-Rao bound for tracking in clutter and tracking multiple objects. Pattern Recogn. Lett. 18 3 (1997) 283-288
4. L.I. Perlovsky, Tracking multiple objects in visual imagery, Conference on Neural Networks for Vision and Image Processing, Tyngsboro, MA, 1991.
5. Perlovsky L.I., Webb V.H., Bradley S.R., and Hansen C.A. Improved ROTHR detection and tracking using MLANS. AGU Radio Sci. 33 4 (1998) 1034-1044
6. R. Deming, L. Perlovsky, R. Brocket, Sensor fusion for swarms of unmanned aerial vehicles using modeling field theory, 2005 IEEE Int'l Conf. on Integration of Knowledge Intensive Multi-Agent Systems: Modeling, Evolution, and Engineering (KIMAS 2005), Waltham, MA, April 18-21, 2005.
7. Office of the Secretary of Defense, Unmanned Aerial Vehicles Roadmap 2002-2027, 2002.
8. Wall R. The latest leap. Aviat. Week Space Technol. 6 September (2004) 46-47
9. Wall R. On the offensive. Aviat. Week Space Technol. 6 September (2004) 49-50
10. Winkler S., and Vorsmann P. Bird's-eye view: GPS and micro aerial vehicles. GPS World October (2004) 14-22
11. McLachlan G.J., and Baskford K.E. Mixture Models (1988), Marcel Dekker, Inc., New York, NY
12. McKenna S.J., Raja Y., and Gong S. Tracking colour objects using adaptive mixture models. Image Vision Comput. 17 (1999) 225-231
13. Bar-Shalom Y., and Fortmann T.E. Tracking and Data Association (1988), Academic Press, New York
14. Castellanos J.A., Montiel J.M.M., Neira J., and Tardos J.D. The SPmap: a probabilistic framework for simultaneous localization and map building. IEEE Trans. Robotic. Autom. 15 Oct. (1999) 948-953
15. M. Csorba, H.F. Durrant-White, New approach to map building using relative position estimates, in: SPIE, Orlando, FL, April 1997, pp. 115-125.
16. A. Davison, Y.G. Cid, N. Kita, Real time 3D SLAM with wide-angle vision, in: Intelligent Autonomous Vehicles, Lisbon Portugal, July 2003, IEEE.
17. Dissanayaka M., Newman P., Clark S., Durrant-White H., and Csorba M. A solution to the simultaneous localisation and map building problem. IEEE Trans. Robotic. Autom. 17 3 (2001) 229-241
18. H.F. Durrant-White, M. Dissanayake, Toward deployment of large scale simultaneous localisation and map building, Control Theory and Applications, IEEE Proceedings, vol. 142, pp. 385-400.
19. Feder H.J.S., Leonard J.J., and Smith C.M. Adaptive mobile robot navigation and mapping. Int. J. Robot. Res. 18 7 (1999) 650-668
20. J. Fenwick, P. Newman, J. Leonard, Collaborative concurrent mapping and localization, in: IEEE Conf. on Robotics and Automation, Washington, DC, May 2002.
21. J. Guivant, E. Nebot, Improving computational and memory requirements of simultaneous localization and map building algorithms, in: IEEE Conf. on Robotics and Automation, Washington, DC, May 2002, pp. 2731-2736.
22. Jensfelt P., and Kristensen S. Active global localisation for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robotic. Autom. 17 5 (2001) 748-760
23. I.K. Jung, S. Lacroix, High resolution terrain mapping using low altitude aerial sterio imagery, in: ICCV, Nice, France, July 2003, IEEE.
24. J.J. Leonard, H.J.S. Feder, Decoupled stochastic mapping, MIT Marine Robotics Technical Memorandum, December 1999.
25. Neira J., and Tardos J.D. Data association in stochastic mapping using the joint compatibility test. IEEE Trans. Robotic. Autom. 17 6 (2001) 890-897
26. E. Nettleton, H. Durrant-White, P. Gibbens, A. Goktogan, Multiple platform localisation and map building, in: G.T. McKee, P.S. Schenker (Eds.), Sensor Fusion and Decentralised Control in Robotic Systems III, Bellingham, vol. 4196, 2000, pp. 337-347.
27. J. Nieto, J.E. Guivant, E.M. Nebot, S. Thrun, Real time data association for FastSLAM, in: ICRA, Taiwan, September 2003, IEEE.
28. D. Schultz, W. Burgard, D. Fox, A.B. Cremers, Tracking multiple moving objects with a mobile robot, in: CVAP-01, Kauai, HW, December 2001, IEEE.
29. Smith R., Self M., and Cheeseman P. Estimating uncertain spatial relationships in robotics. In: Cox I.J., and Wilfon G.T. (Eds). Autonomous Robot Vehicles (1990), Springer Verlag, New York 167-193
30. Thrun S., Burgrad W., and Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots. Mach. Learning 31 (1998) 29-53
31. Thrun S. A probabilistic online mapping algorithm for teams of mobile robots. Int. J. Robot. Res. 20 5 (2001) 335-363
32. J.K. Uhlmann, S.J. Julier, M. Csorba, Nondivergent simultaneous map building and localization using covariance intersection, in: SPIE, Orlando, FL, April 1997, pp. 2-11.
33. S. Williams, Efficient solutions to autonomous mapping and navigation problems, PhD Thesis, University of Sydney, 2001.
34. Zhang Z., and Faugeras O.D. A 3D world model builder with a mobile robot. Int. J. Robot. Res. 11 4 (1992) 269-285
35. Reid D. An algorithm for tracking multiple targets. IEEE Trans. Automat. Contr. 24 AC-6 (1979) 84-90
36. Hue C., Le Cadre J.P., and Perez P. Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Trans. Signal Process. 50 2 (2002) 309-326
37. Arulampalam M.S., Maskell S., Gordon N., and Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50 2 (2002) 174-188
38. Sukkarieh S., Nettleton E., Kim J.H., Ridley M., Goktogan A., and Durrante-White H. The ANSER project: data fusion across multiple uninhabited air vehicles. Int. J. Robot. Res. 22 7-8 (2003) 505-539
39. Crespi V., Chung W., and Jordan A.B. Decentralized sensing and tracking for UAV scheduling. Proc. SPIE (2004)
40. In: Thompson M.M. (Ed). Manual of Photogrammetry vol. 1 (1966), American Society of Photogrammetry, Falls Church, VA Chapter II
41. Linder W. Digital Photogrammetry: Theory and Application (2003), Springer
42. Gulch E. Information extraction from digital images: a KTH approach. In: Gruen A., Kuebler O., and Agouris P. (Eds). Automatic Extraction of Man-Made Objects from Aerial and Space Images (1995), Birkhauser Verlag, Basel 73-82
43. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York, NY
44. Jain A.K. Fundamentals of Digital Image Processing (1986), Prentice-Hall, Englewood Cliffs, NJ (Chapter 9)