Background-independent evaluation model for infrared jamming effectiveness of false targets

Given the lack of a standardized evaluation system for the infrared jamming effectiveness of false targets, this paper first uses a co-saliency detection model to extract the main parts of the true and false targets. Then the perceptual similarity algorithm is improved by combining the operational requirements of false targets in the infrared band. Finally, a background-independent evaluation model for infrared jamming effectiveness of false targets is constructed. The experimental results show that the model can quantitatively reflect the infrared jamming effectiveness of a single false target and distinguish the infrared jamming effectiveness of different types of false targets. In addition, the model has stronger robustness than traditional evaluation models.

Automated summary

This paper constructs a background-independent evaluation model for the infrared jamming effectiveness of false targets using a co-saliency detection model and an improved perceptual similarity algorithm. The model can quantitatively reflect the infrared jamming effectiveness of a single false target, distinguish the infrared jamming effectiveness of different types of false targets, and exhibit stronger robustness than traditional evaluation models.