A novel effectiveness evaluation method of smokescreen jamming based on the fusion of directional gradients and local texture features

Abstract

Smokescreen jamming is one of the most common types of interference in optical imaging. However, there are few tools available to assess the effectiveness of optical imaging in the presence of smoke screen interference, and most of them are qualitative in nature. There is an urgent need for targeted quantitative assessment methods. Therefore, a novel effectiveness evaluation method of smoke screen jamming (EEMSSJ) based on the histogram of oriented gradient and local binary pattern feature is proposed. According to the influence on the tracking effect, the proposed method weights and fuses the directional gradient histogram feature, texture feature, cosine similarity, and luminance feature of the image and finally obtains the EEMSSJ index. The EEMSSJ index provides a quantitative assessment of the effectiveness of smoke screen interference from an image perspective, which can effectively improve the accuracy of the analysis and judgment of the smokescreen jamming situation and provide a strong reference basis for making timely adjustments to the interference deployment. The effectiveness of the proposed method and the EEMSSJ index are validated by experiments. Experimental results show that the EEMSSJ index can achieve a high level of agreement with the subjective assessment of the experimental data and possess high sensitivity and robustness. The EEMSSJ index provides a more accurate representation of the dynamic disturbance of the smoke screen than the correlation assessment method and the multi-scale structural similarity method.