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一种通过混合分解和状态预测框架预测隐藏目标轨迹的方法
Zhengpeng Yang1, Jiyan Yu1, Miao Liu1
1College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一个新的框架,用于预测隐藏的目标轨迹,使用改进的顺序变化模式分解 (ISVMD) 和极端学习机器 (ELM),优化由红蓝优化器 (RBMO). 该方法在具有挑战性的环境中显著提高了准确性和稳定性.
科学领域:
- 毫米波传感器是一种毫米波传感器.
- 信号处理 信号处理
- 机器学习用于目标跟踪跟踪.
背景情况:
- 对于毫米波 (mmWave) 传感器来说,在复杂,杂的环境中,难以准确地预测隐藏目标的轨迹.
- 现有的追踪系统与环境干扰和故意隐藏目标作斗争.
研究的目的:
- 开发一个先进的自主预测框架,用于隐藏的目标,使用毫米波传感器数据.
- 在具有挑战性的条件下提高轨迹预测的准确性和稳定性.
主要方法:
- 集成改进的顺序变化模式分解 (ISVMD) 用于信号重建和特征提取.
- 极端学习机器 (ELM) 的应用用于轨迹预测.
- 优化ISVMD-ELM框架使用新的红蓝优化器 (RBMO).
主要成果:
- 拟议的RBMO-ISVMD-ELM方法在与最先进的方法相比显示出更高的性能.
- 该框架有效地将杂的目标回声信号重建为强大的特征序列.
- 在苛刻的条件下,在预测隐藏的地面目标轨迹方面取得了高精度.
结论:
- RBMO-ISVMD-ELM框架为毫米波传感中隐藏目标轨迹预测提供了强大而准确的解决方案.
- ISVMD,ELM和RBMO的协同组合提高了适应性和计算效率.
- 这种方法大大克服了环境干扰和隐藏所带来的局限性.
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