摘要
未来战场上,重载无人机能够发挥重要作用,包括侦察、掩护、运输、打击等作战场景。面对复杂的作战环境,能对被打击目标快速识别显得尤为重要。在战场环境中地面目标比较复杂,需要对目标精准识别打击,本文通过粒子群算法对识别方法分配,提高重载无人机识别目标的可靠性,并根据危险等级提供打击策略,能达到真实战场要求。
In the future battlefield, heavy-duty UAVs can play an important role, including reconnaissance, cover, transportation, attack and other operational scenarios. In the face of a complex combat environment, it is particularly important to identify the target quickly. In the battlefield environment, the ground targets are complex and need to be accurately identified and attacked. This paper uses particle swarm optimization algorithms to allocate identification methods, improve the reliability of heavy-duty UAV target identification and provide attack strategies according to the danger level, which can meet the requirements of the real battlefield.
出处
《软件工程与应用》
2022年第6期1255-1263,共9页
Software Engineering and Applications
