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2 Aberdeen Proving Ground Database Results In this experiment, a total of 136 frames containing 372 targets were used. There were three different sets of data in the this database that we refer to as T3002 and T3004. In the following, the results for each set is reported separately. Copyright © 2002 by Marcel Dekker, Decker, Inc. All Rights Reserved Passive Infrared Automatic Target Discrimination Table 1 49 Variation of SA as a Function of TIR2 Bin range Database Bin 29-Palms 1 2 3 4 1 2 3 4 T3002 (expanding the ﬁrst bin) T3003 (expanding the ﬁrst bin) T3004 T3004 (expanding ﬁrst two bins) Combined databases Combined databases (for 0–150 only) a Min.
Automatic target recognition: State of the art survey. IEEE Trans Aerospace electron Syst 22(4):364–379, 1986. 2. B Dasarathy. Information processing for target recognition from autonomous vehicles. Proc SPIE Electro-Opt Tech Autonomous Vehicles 219:86–93, 1980. 3. B Bhanu, T Jones. Image understanding research for automatic target recognition. IEEE Aerospace Electron Syst Mag 15–22, Oct. 1993. 4. L Clark, L Perlovsky, W. Schoendorf, C. Plum, T Keller. Evaluation of forward-looking infrared sensors for automatic target recognition using an information-theoretic approach.
S Fahlman. Faster learning variations on back-propagation: An empirical study. Proceedings of the 1988 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann, pp 38–51, 1988. 13. S Haykin. Neural Networks: A Comprehensive Foundation. New York: Macmillan College, 1994. 14. TD Sanger. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks 2:459–473, 1989. Copyright © 2002 by Marcel Dekker, Decker, Inc. 1 INTRODUCTION Automatic target recognition (ATR) technology is entering a critical phase of the technology cycle.