A multiple hypothesis rule-based automatic target recognizer
The majority of automatic target recognizers undertaking field evaluation today owe their internal structure to a classical statistical approach. Although the dimensionality of the variable parameters that each system is subject to is large, little use is made of context and ancillary information such as time of day, sensor, weather conditions and intelligence data. Such ancillary data can be profitably used to alleviate the algorithmic burden of accommodating the extreme ranges of conditions.
Presented here is a novel approach to include ancillary knowledge into the control structure of an automatic target recognizer (ATR).
KeywordsFalse Alarm Belief Function Basic Probability Assignment Ancillary Information Mission Objective
Unable to display preview. Download preview PDF.
- 1).R. Crownover and J. Keller, "Fast dimension reduction that preserves undetermined data clusters," Proceedings, SPIE Conference on Advanced Signal Processing Algorithms and Architectures, San Diego, California, August 1986.Google Scholar
- 2).J. Keller, R. Crownover, J. Wootton and G. Hobson, "Target recognition using the Karhunen-Loeve transform," Proceedings, IEEE International Conference on Systems, Man and Cybernetics, Tucson, Arizona, November 1985, pp. 310–314.Google Scholar
- 3).J. Keller, R. Crownover and R. Chen, "Characteristics of natural scenes related to fractal dimension," IEEE Transactions, Pattern Analysis and Machine Intell, Vol. PAMI-9, No. 5, Sept. 1987, pp. 621–627.Google Scholar
- 4).J. Keller and D. Hunt, "Incorporating fuzzy membership functions into the perceptron algorithm," IEEE Transactions, Pattern Anal and Machine Intell, Vol. PAMI-7, No. 6, November 1985, pp. 693–699.Google Scholar
- 5).J. Keller, G. Hobson, J. Wootton, A. Nafarieh and K. Luetkemeyer, "Fuzzy confidence measures in midlevel vision," IEEE Transactions, Systems, Man and Cybernetics, Vol. SMC-17, No. 4, 1987.Google Scholar
- 6).P.A. Nagin, A.R. Hanson, and E.M. Riseman. "Region extraction and description through planning," COINS Tech Rep 77-8, Computer and Information Sciences Dept., University of Massachusetts, Amherst.Google Scholar
- 7).R.A. Brooks, R. Greiner, and T. Binford. "Progress report on a model-based vision system," Proceedings of the Image Understanding Workshop, 1978, pp. 145–151 (L.S. Baumann, ed.).Google Scholar
- 8).D.P. McKeown. "MAPS: The organization of a spatial database system using imagery, terrain and map data," Proceedings: DARPA Image Understanding Workshop, June 1983, pp. 105–127.Google Scholar
- 9).D.M. McKeown, and J. McDermott, "Toward expert systems for photo interpretation," IEEE Trends and Applications '83, May 1983, pp. 33–39.Google Scholar
- 10).A. Rosenfeld and A. Kak. Digital Picture Processing, 2nd edition, Orlando: Academic Press, 1982.Google Scholar
- 11).R. Duda, and P. Hart, Pattern Classification and Scene Analysis, New York: Wiley & Sons, 1978.Google Scholar
- 12).K. Luetkemeyer, G. Hobson, and C. Carpenter. "Evaluation of segmentation techniques applied to prescreened areas of multi-sensor imagery," MAECON, Dayton, 1986.Google Scholar
- 13).G. Waldman, J. Wootton, G. Hobson, and K. Luetkemeyer. "A normalized clutter measure for images," Computer Vision, Graphics & Image Processing (to be published).Google Scholar
- 14).G. Hobson, and J. Wootton. "Electro optical/infrared automatic feature recognition," IRAD Tech Report, F784, Emerson Electric.Google Scholar
- 15).G. Hobson, and J. Wootton. "Electro optical/infrared automatic feature recognition," IRAD Tech Report, F785, Emerson Electric.Google Scholar
- 16).G. Hobson, and J. Wootton. "Electro optical/infrared automatic feature recognition," IRAD Tech Report, F786, Emerson Electric.Google Scholar
- 17).J. Keller, M. Gray, J. Givens. "A fuzzy k-nearest neighbor algorithm," IEEE Trans System, Man, Cybern, Vol. SMC-15, No. 4, July/August 1985, pp. 580–585.Google Scholar
- 18).J. Wootton, G. Hobson, K. Luetkemeyer and J. Keller. "The use of fuzzy set theory to build confidence measures in multisensor imagery," IEEE Applied Imagery Pattern Recognition Workshop.Google Scholar
- 19).G. Shafer. A Mathematical Theory of Evidence, Princeton: Princeton University Press, 1976.Google Scholar