一个强大的GPS导航过器基于最大电流的标准与自适应内核带宽
Dah-Jing Jwo1, Yi-Ling Chen1, Ta-Shun Cho2
1Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung 202301, Taiwan.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一种自适应的内核带宽方法,以提高全球定位系统 (GPS) 在非高斯噪声中的导航精度. 新技术提高了过器的性能和强大的状态估计,以实现精确的定位.
科学领域:
- 信号处理 信号处理
- 导航系统 导航系统
- 一个稳健的估计.
背景情况:
- 全球定位系统 (GPS) 导航易受干扰和非高斯噪声的影响,降低了定位准确度.
- 传统的最小平均平方误差 (MMSE) 算法在非高斯噪声下表现不佳.
- 适应性过显示出处理重尾噪声的前景.
研究的目的:
- 为了提高在非高斯噪声的情况下GPS导航接收器的性能.
- 解决传统的基于最大电流标准的扩展卡尔曼波器 (MCCEKF) 算法中固定内核宽度的局限性.
- 开发一个强大的状态估计方法,用于GPS信号与异常噪声值.
主要方法:
- 在MCC框架内实施自适应内核带宽 (AKB) 技术.
- 使用自适应变量来动态调整内核宽度生成内核函数矩阵.
- 应用了新的MCCEKF与自适应内核带宽 (MCCEKF-AKB) 算法.
主要成果:
- 通过调整内核宽度,MCCEKF-AKB算法显示了比传统MCCEKF更好的性能.
- 有效地减少脉冲噪声,在重尾噪声条件下提高性能.
- 实现了对异常值的稳健状态估计,同时保持了过准确度.
结论:
- 拟议的MCCEKF-AKB在通用噪音条件下为GPS导航提供了一个简单而有效的计算解决方案.
- 适应性内核带宽对于在非高斯噪声环境中优化MCC过器性能至关重要.
- 这种方法提供了一种定性解决方案,用于研究噪音测量数据中的随机结构,以改进本地化.
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