一个分层的自适应时刻匹配多个模型跟踪方法超音速滑动目标在测量不确定性下的测量不确定性
Hanxing Shao1, Jibin Zheng1, Yanwen Bai1
1National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一种新的等级自适应时刻匹配 (HAMM) 方法,用于跟踪高超音速滑翔目标 (HGT). 哈姆方法提高了雷达跟踪准确度和收速度,即使是复杂的机动和非高斯噪声.
科学领域:
- 航空航天工程 航空航天工程
- 雷达系统 雷达系统
- 信号处理 信号处理
背景情况:
- 超音速滑翔目标 (HGTs) 由于复杂的机动和非高斯测量噪声,存在重大雷达跟踪挑战.
- 现有的单模型和多模型跟踪方法在机动模式捕获,模型集完整性,计算效率和非高斯噪声抑制方面扎.
研究的目的:
- 为高超音速滑翔目标 (HGTs) 开发一种先进的多模型跟踪方法.
- 解决现有方法在机动复杂性,噪声特征和计算效率方面的局限性.
主要方法:
- 提出了一种分层自适应时刻匹配 (HAMM) 多模型方法.
- 构建了一个全面的机动模式模型集.
- 一个等级框架动态地适应一个粗的模型集,使用后面的概率演化和Rényi分歧.
- 通过时刻匹配生成一个精细的模型集.
- 引入了一个最小误差立方卡尔曼波器 (MEECKF) 来处理非高斯噪声.
主要成果:
- 拟议的HAMM方法证明了HGTs的位置准确度得到了改进.
- 与传统方法相比,该方法表现出更快的趋同.
- MEECKF有效地抑制非高斯测量噪声,提高了跟踪稳定性.
结论:
- HAMM多模型方法为HGT的雷达跟踪提供了一个强大的解决方案.
- 层次模型适应和非高斯噪声抑制的整合提供了卓越的性能.
- 拟议的方法推进了超音速目标跟踪的最新技术.
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