基于多元拉普拉斯分布的鲁棒非线性平滑器
Robust Nonlinear Smoother Based on Multivariate Laplace Distribution
何嘉诚 1柏明明 2王刚 3彭倍1
作者信息
- 1. 电子科技大学机械与电气工程学院,成都 611731
- 2. 浙江大学控制科学与工程学院,杭州 310027
- 3. 电子科技大学信息与通信工程学院,成都 611731
- 折叠
摘要
野值干扰具有不可预估性,将对导航与控制系统的平稳运行产生严重威胁.针对厚尾分布的测量噪声会诱导传统基于高斯假设的平滑器性能下降问题,提出了一种基于多元拉普拉斯分布的非线性平滑器.方法采用拉普拉斯分布建模含有随机野值干扰的量测噪声,并基于变分推断技术对多元拉普拉斯分布的模型参数进行在线辨识,从而提高平滑器在厚尾噪声下的鲁棒性和适应性.仿真结果表明,所提出的平滑器能够有效提升在厚尾噪声环境中对目标跟踪的精度,优于传统高斯假设下的平滑器.
Abstract
Outliers pose a significant threat to the stable operation of navigation and control systems with their unpre-dictability.To address the performance degradation of the traditional Gaussian-based smoothers caused by heavy-tailed measurement noise,a nonlinear smoother based on multivariate Laplace distribution is proposed.Laplace distribution is used to model the measurement noise containing random outlier disturbances,and the model parameters of the multivariate Laplace distribution are identified online based on the variational inference,so as to improve the robustness and adaptability of the smoother under heavy-tailed noise.Simulation results demonstrate that the proposed smoother effectively improves the target-tracking accuracy in heavy-tailed noise environments,which significantly outperforms the traditional Gaussian-based smoothers.
关键词
厚尾分布噪声/多元拉普拉斯分布/非线性/平滑器Key words
heavy-tailed distribution noise/multivariate Laplace distribution/nonlinear/smoother引用本文复制引用
出版年
2024
NETL
NSTL
万方数据