Complexity Evaluation of Aerial Infrared Countermeasure Scenes

Unmanned aerial vehicles (UAVs) are being increasingly deployed in modern battlefields owing to their low cost, stealth capabilities, and challenging interception. The recent Russian–Ukrainian conflict has further highlighted the widespread and far-reaching impact of UAVs. Therefore, the offensive and defensive countermeasures for UAVs have attracted considerable research attention. With the development of various optoelectronic interference methods, the complexity of the battlefield environments of UAVs has increased, posing significant challenges for infrared (IR) target identification and tracking. This study explored methodologies for evaluating IR countermeasure scenes involving UAVs. A detailed survey of the extensive literature on scene evaluation was conducted for the quantitative assessment of factors affecting the performance of target detection and tracking algorithms in IR scenarios. Based on the relevant literature, this study evaluated IR scenes in three distinct areas: the target area, the target/local background area, and the global background area. Furthermore, an IR scene complexity evaluation method was established to examine the effects of evaluation metrics for different regions on algorithm performance. The findings of this study can pave the way for future research and provide valuable insights for optimizing interference strategies for UAVs in IR countermeasures.