复合加权聚变评估和优化雷达搜索器中的智能跟踪算法
Kaiyu Hu1,2, Chunxia Yang1, Zhaoyang Wang1
1304 Institute, China Aerospace Science and Industry Corporation, Beijing 100074, China.
iScience
|January 1, 2024
概括
本研究引入了一种新的加权融合方案,用于评估和优化雷达搜索神经网络 (NN) 追踪算法. 该方法通过系统地评分和融合跟踪数据来提高性能,从而提高了算法效率.
科学领域:
- 人工智能的人工智能
- 控制系统工程 控制系统工程
- 雷达信号处理 雷达信号处理
背景情况:
- 评估和优化雷达搜索神经网络 (NN) 追踪算法对于提高引导和控制系统性能至关重要.
- 现有的方法可能会受到硬件变化的影响,使客观算法评估复杂化.
- 动态跟踪场景需要适应性评估指标,以考虑随时间的性能波动.
研究的目的:
- 为雷达搜索器NN跟踪算法设计一个分层加权的核聚变评估和优化方案.
- 为了减轻硬件对算法性能评估的影响.
- 基于全面的绩效评分,开发一种可靠的方法来优化NN跟踪算法.
主要方法:
- 为了全面的算法评估,采用了三阶段加权聚变过程.
- 闭环性能指数最初被合并以消除硬件效应.
- 追踪指数使用混合线性-非线性机制在不同时间段进行得分,并按层次融合.
主要成果:
- 使用拟议的融合方案设计和分析了一个参数评估案例集.
- 确定了获得最高综合分数的案例.
- 雷达搜索器NN跟踪算法根据得分最高的情况进行了优化,在实验验证中表现出更好的有效性.
结论:
- 拟议的等级加权融合方案为评估和优化雷达搜索器NN跟踪算法提供了一种有效的方法.
- 这种方法成功地将算法性能与硬件依赖性隔离开来.
- 实验结果证实了该方法在提高跟踪算法性能方面的有效性.
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