一个新的高斯和四次数受约束的立方体卡尔曼波算法用于GNSS/SINS集成态度确定和定位系统
Qing Dai1,2, Guo-Rui Xiao2, Guo-Hua Zhou3
1College of Urban Construction, Luoyang Polytechnic, Luoyang, China.
Frontiers in neurorobotics
|June 24, 2024
概括
一个新的高斯总量四次数受约束立方卡尔曼波器 (GSQCCKF) 改善了无人驾驶飞行器的导航. 该算法通过近似噪声和限制全球导航卫星系统/惯性导航系统集成系统的四次数来提高非高斯噪声的准确性.
科学领域:
- * 航空航天工程 航空航天工程
- * 导航系统 * 导航系统
- * * 信号处理 信号处理
背景情况:
- * 四边子立方卡尔曼波器 (QCKF) 广泛用于无人机导航,但假定高斯噪声,这往往是不准确的.
- * 现实世界的全球导航卫星系统/惯性导航系统集成态度确定和定位系统 (GNSS/SINS-IADPS) 数据处理涉及非高斯噪声和四边形规范化约束.
- *在非高斯环境中直接应用QCKF,而不考虑这些约束因素,可能会降低估计准确性.
研究的目的:
- * 开发一种新的算法,以解决QCKF在非高斯噪声环境中的局限性,用于无人机导航.
- *为了提高GNSS/SINS-IADPS数据处理在非高斯噪声条件下的准确性和适应性.
- * 在过过程中纳入四规范化约束.
主要方法:
- * 提出了高斯和四次数受约束的立方体卡尔曼波器 (GSQCCKF) 算法.
- *使用高斯混合模型来改进随机模型的近似非高斯噪声.
- * 采用两步投影方法来强制执行四规范化约束.
主要成果:
- *与标准QCKF相比,GSQCCKF算法在估计准确度方面取得了显著的改进.
- * 在GNSS/SINS-IADPS数据处理中提高了对非高斯噪声条件的适应性.
- *通过对无人机数据的模拟和实验分析进行验证.
结论:
- *GSQCCKF算法有效地处理无人机导航中的非高斯噪声和四规范化.
- * 这种新的方法为GNSS/SINS-IADPS数据处理提供了更强大,更准确的解决方案.
- * 结果表明,在需要精确的态度和位置确定的自主系统中,其应用范围更广.
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