By A. Bertossi
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All Rights Reserved Passive Infrared Automatic Target Discrimination 45 Figure 4 A sample of IR imagery showing two tanks. The parameters n and k were varied by three different values. The parameter p varied by four different values because past experience had suggested that it was the most sensitive of the three parameters. Thus, a 3 Â 3 Â 4 factorial design was performed and a total of 36 runs on each frame were made. Figure 9 shows the variations of mean segmentation accuracy versus the percent parameter, p, for three different values of n and k for the two frames.
It should be noted that this conclusion may apply only to the speciﬁc sensors used for this study. If we compare setup a with setup c, we note signiﬁcant improvement achieved by the stacked dual-band input in both training and testing sets, which ranges from 5% to 8% again. In other words, processing the MW and LW jointly is better than using either one of them alone. The way we merge the two bands also affects the clutter rejection performance. Although the performances of setups c and d are similar, setup c is the clear winner when it comes to the peak performance and in the cases where 20 or more MLP inputs were used.
08 SD Passive Infrared Automatic Target Discrimination Table 3 51 Variation of SA as a Function of ESR Bin range Database 29-Palms SA Bin Min. Max. 56 T3003 (expanding the ﬁrst bin) T3004 Combined databases Combined (expanding range to between 0 and 1500) a Standard deviation Copyright © 2002 by Marcel Dekker, Decker, Inc. 19 SD 52 Sadjadi T3002 Set Results Table 1 shows the variations of SA as function of TIR2 . 33. 47. 49. Finally, in the fourth bin, the average SA value was zero due to the one target that was not detected.