一种新型的变异贝叶斯方法,用于水下INS/DVL/USBL定位的未知噪声
Haoqian Huang1, Chenhui Dong1, Yutong Zhang1
1College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一个基于Wishart的反向波动贝叶斯适应性立方卡尔曼波器 (IW-VACKF) 用于水下状态估计. 这种新方法通过在复杂的海洋环境中更好地描述不确定的系统噪声来提高精度.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 信号处理 信号处理
- 海洋工程 海洋工程
背景情况:
- 准确的状态估计对于水下系统至关重要,但由于不可预测的系统噪声,这具有挑战性.
- 传统方法与不确定的噪声模型作斗争,导致状态确定精度降低.
- 水下环境在获得有关系统噪声的可靠预先信息方面存在独特的困难.
研究的目的:
- 开发一种新的适应性立方卡尔曼波器,以改善复杂的水下环境中的状态估计.
- 通过使用反向-维沙特分布来解决不确定的系统噪声的挑战.
- 提高系统噪声动态和水下应用中的不确定性.
主要方法:
- 提出了一个基于逆Wishart (IW) 的变量贝叶斯适应立方卡尔曼波器 (IW-VACKF).
- 利用逆维沙特分布作为系统噪声协变矩阵的相对先验.
- 引入了一个混合概率向量来建模状态噪声的不确定性和动态.
- 导出状态过渡和测量过程作为层次的高斯模型.
- 采用变量贝叶斯方法来计算联合后置信息.
主要成果:
- 在模拟中,IW-VACKF表现出更好的状态估计精度.
- 现实世界的试验证实了过器在复杂的水下条件下的有效性.
- 提出的方法有效地处理不确定的系统噪声,优于传统方法.
结论:
- 在具有挑战性的水下场景中,IW-VACKF为精确状态估计提供了强大的解决方案.
- 使用逆维沙特分布和混合概率有效地描述系统噪声是提高准确性的关键.
- 开发的过器为水下导航和控制系统提供了重大进步.
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