Comparing behavioral receiver operating characteristic curves to multidimensional matched filters

Human factors experiments can be used to test whether a sensor can improve operator performance for detecting or recognizing a target.1 Although human factors experiments are of tremendous value, these tests are time consuming and resource intensive. To reduce costs associated with collecting behavioral data, a two-dimensional matched filter is proposed. The objective is to compare and contrast behavioral and matched filter receiver operating characteristic (ROC) plots to determine whether the matched filter technique is a good predictor of human performance. Five different background images (three infrared band images, a chromatic-fused image, and monochromatic-fused image) were used with, and without, a target (airplane) present. False alarm and target detection probabilities were computed and results were plotted on an ROC curve. The matched filter ROC curves were then compared to behavioral ROC curves. Results showed that the matched filter ROC curves were similar to behavioral ROC curves with color fusion, longwave infrared showing the highest sensitivity, and mid-wave and shortwave infrared scenes were significantly less sensitive (near chance). These results indicate that the matched filter analysis may be used to model human behavior.