Predicting the Probability of Target Detection in Static Infrared and Visual Scenes Using the Fuzzy Logic Approach.

摘要

The probability of detection (PD) of targets in static infrared and visually cluttered scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a robust method for the computation and prediction of the PD targets in cluttered scenes. The Mamdani/Assilian, and Sugeno Neurofuzzy- based models have been investigated. A large set of infrared (IR) imagery and a limited set of visual imagery has been used to model the relationships between several input parameters; the contrast, camouflage condition,range, aspect, width and experimental PD. The fuzzy and neuro-fuzzy models gave predicted PD values that had 0.98 correlation to the experimental PD's. The results obtained indicate the robustness of the fuzzy-based modeling techniques and the applicability of the FLA to those types of problems having to do with the modeling of human-in-the-loop target detection in any spectral regime.