具有变化点检测的噪声适应状态估计器
Xiaolei Hou1, Shijie Zhao1, Jinjie Hu1
1College of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了用于跟踪机动目标的新型自适应估计器,通过结合变异推理和变化点检测来提高精度,以进行稳定状态和噪声参数估计.
科学领域:
- 信号处理 信号处理
- 控制系统 控制系统
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 由于状态空间模型中的突然参数变化,跟踪急剧机动的目标存在挑战.
- 现有的自适应卡尔曼过器在初始设置和非静止性方面遇到了困难.
研究的目的:
- 开发新的变异适应状态估计器,用于联合目标状态和过程噪声参数估计.
- 提高在动态环境中跟踪急剧机动目标的能力.
主要方法:
- 提出了两个递归估计器:基于变化点的自适应卡尔曼波器 (CPAKF) 和基于变化点的自适应卡尔曼平滑器 (CPAKS).
- 结合变化推理与在线贝叶斯变化点检测用于参数和状态估计.
- 运行长度的计算概率和近似的关节后部使用变异推理.
主要成果:
- 拟议的CPAKF和CPAKS方法证明了对初始代值设置的稳定性.
- 与现有方法相比,实现了针对急剧机动的目标的更好的追踪性能.
- 在CPAKS中,变化点检测促进了非静止序列的自适应滑动窗口长度.
结论:
- 新型变异自适应估计器有效地处理机动目标跟踪中的突然参数变化.
- 对于复杂的跟踪场景,CPAKF和CPAKS提供了一个强大的,适应性的解决方案.
- 使用合成和现实世界的机动目标数据集验证性能.
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