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MF-IEKF:一种用于INS/GNSS的多倍化联合不变扩展卡尔曼波器

Lebin Zhao¹, Tao Chen¹, Peipei Yuan¹

¹School of Automobile, Chang'an University, Xi'an 710018, China.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

新的多重联合不变扩展卡尔曼波器 (MF-IEKF) 改进了惯性导航系统 (INS) 和全球导航卫星系统 (GNSS) 的集成. 它提高了准确性和稳定性,特别是在较大的初始失调角度下.

科学领域:

导航系统工程导航系统工程
机器人技术机器人技术机器人技术
地理学就是地质学.

背景情况:

惯性导航系统 (INS) 和全球导航卫星系统 (GNSS) 的整合对于准确的导航至关重要,主要是为了减轻INS错误漂移.
基于传统扩展卡尔曼波器 (EKF) 的融合方法表现出几何不一致性,导致偏差估计,特别是在初始错位角度较大时.
不变扩展卡尔曼波器 (IEKF) 使用Lie组SE2(3) 提供了一个更一致的框架,但在极端不对齐下面对错误信息的协同作用和缓慢的收方面存在局限性.

研究的目的:

在INS/GNSS集成中解决传统融合方法和标准IEKF的局限性.
为了提高估计准确度和改进收速度,特别是在具有较大的初始失调角度的场景中.
为了提高导航性能,提出一种新的多重联合IEKF (MF-IEKF).

主要方法:

开发了一个在SE2(3) 上使用乘法IMU预集成的几何一致状态传播模型.
引入了两个并行,相互逆向的左不变误差子过器 (ML1-IEKF和ML2-IEKF) 进行协同准确性增强.
整合了一个短期的乘法右不变子过器,以减轻大错位场景中的初始位置和速度错误.

主要成果:

与标准ML1-IEKF相比,拟议的MF-IEKF显示出更高的导航精度.
广泛的蒙特卡洛模拟验证了MF-IEKF的增强性能和稳定性.
使用KITTI数据集的实验证实了MF-IEKF的实际有效性和更高的准确性.

结论:

MF-IEKF有效地克服了INS/GNSS集成现有IEKF方法的局限性.
拟议的方法显著提高了导航准确性和稳定性,特别是在具有挑战性的大失调条件下.
MF-IEKF在实现可靠和精确的导航解决方案方面取得了重大进展.

关键词:

全球导航卫星系统 (GNSS) 是一个全球导航卫星系统.惯性导航系统 (INS) 是指乘法联合不变的EKF (MF-IEKF) 的情况.国家估计估计.

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