基于最大对应的坚固的平方根立方体卡尔曼波器用于车辆合作导航
Wei Sun1, Xiaotong Zhang2, Wei Ding1
1School of Geomatics, Liaoning Technical University, Fuxin, 12300, Liaoning, China.
Scientific reports
|December 27, 2023
概括
这项研究引入了一种新的最大纠正强大的平方根立方卡尔曼波器 (MCSCKF) 用于车辆合作本地化. 该MCSCKF方法显著提高定位准确性和稳定性,即使在高测量噪声污染.
科学领域:
- 机器人技术和自主系统
- 信号处理 信号处理
- 导航和控制 导航和控制
背景情况:
- 相对定位对于智能车辆导航和网络协作至关重要.
- 高度测量噪声污染严重降低了传统过性能.
研究的目的:
- 开发一种强大的车辆合作本地化方法,能够抵御噪音污染.
- 提高智能车辆相对位置估计的准确性和可靠性.
主要方法:
- 提出了一种新的最大校正强大的平方根立方体卡尔曼波器 (MCSCKF).
- 利用最大纠正的稳定性,同时保持平方根立方卡尔曼波器 (SCKF) 的优势.
- 在紧密集成的车辆合作导航场景中评估了该方法.
主要成果:
- 与扩展卡尔曼波器 (EKF) 和立体卡尔曼波器 (CKF) 相比,MCSCKF表现出更高的性能.
- 与EKF相比,定位精度提高了35.08%,与CKF相比提高了31.83%.
- 拟议的过器对非高斯噪声具有强大的稳定性.
结论:
- MCSCKF是一种有效的方法,可以提高车辆合作定位的准确性和稳定性.
- 这种方法解决了由测量噪声污染引起的性能退化问题.
- MCSCKF为可靠的智能车辆导航提供了一个有前途的解决方案.
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