基于数据融合的算法,用于评估低海拔和低速度小目标的威胁水平
Wei Wu1, Wenjie Jie1, Angang Luo2
1School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究引入了一种新的威胁评估算法,用于使用多源数据融合的低高度,低速度 (LSS) 目标. 该算法通过准确评估LSS目标威胁以更好地进行拦截决策来增强防空能力.
科学领域:
- 航空航天工程 航空航天工程
- 防务技术 防务技术 技术
- 人工智能的人工智能
背景情况:
- 低高度和低速度 (LSS) 目标给防空系统带来了重大检测和机动性挑战.
- 有效的威胁评估对于对这些难以捉摸的目标进行及时拦截决定至关重要.
- 可见光检测条件需要强大的算法来克服目标表征的局限性.
研究的目的:
- 通过在可见光条件下融合多源数据,为LSS目标开发一种新的威胁评估算法.
- 建立一个全面的评估系统LSS目标使用定义的威胁指标和会员功能.
- 提高对防空指挥控制的威胁评估的可靠性和准确性.
主要方法:
- 一种结合权衡方法,集成分析层次过程 (AHP),权衡和CRITIC方法,以优化主观和客观的重量融合.
- 通过类似于理想解决方案 (TOPSIS) 进行订单偏好技术的应用,以快速进行威胁歧视.
- 利用Dempster-Shafer (D-S) 证据理论进行强有力的威胁评估,并具有反干扰能力.
主要成果:
- 托普西斯方法有效地区分威胁值,适合快速区分高和低威胁.
- D-S证据理论显示出强大的反干扰能力,平衡主观和客观的不确定性.
- 这两种方法在复杂环境中显著提高了威胁评估的可靠性.
结论:
- 拟议的多源数据融合算法增强了对LSS目标的防空能力.
- TOPSIS和DS证据理论为不同的威胁评估场景提供了互补的优势.
- 该研究通过改进威胁评估,为防空指挥和控制系统提供了宝贵的支持.
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