国防科技大学学报2025，Vol.47Issue(3)：141-150,10.DOI:10.11887/j.cn.202503015
无人机非线性状态估计:扩展精确高斯变分推理学习方法
Nonlinear state estimation for unmanned aerial vehicles:extended exactly Gaussian variational inference learning method
摘要
Abstract
Aiming at the problems of large estimation error and poor anti-interference ability in state estimation and parameter learning of time-varying nonlinear systems,a batch state estimation and parameter learning method for accurate sparse Gaussian variational inference for nonlinear systems was proposed.A loss function was proposed based on Gaussian variational reasoning,and the state estimation problem was transformed into an approximation problem to the true posterior,and parameters that need to be learned were introduced.The parameters of the state probability distribution were iteratively updated using the Gauss-Newton optimizer method,and a complete state estimation iterative scheme was obtained by using Stein's lemma,the sparsity of the covariance matrix and the Gaussian volume method.The noise parameters of the measurement model were learned through expectation maximization,and the inverse Wishart prior was introduced to reduce the influence of measurement noise and outliers on parameter learning and state estimation results.The simulation experiment was carried out on the UAV simulation model,and the UAV trajectory can be accurately estimated without adding the UAV movement and the real value of the measurement noise,and the impact of measurement noise and measurement outliers on trajectory estimation accuracy is effectively suppressed.关键词
精确稀疏高斯变分推理/非线性系统批量状态估计/参数学习/期望最大化方法/轨迹估计Key words
exactly sparse Gaussian variational inference/batch state estimation for nonlinear systems/parameter learning/expectation maximization method/trajectory estimation分类
信息技术与安全科学引用本文复制引用
刘久富,Elishahidi S.B.Mvungi,汪恒宇,解晖,刘向武,王志胜..无人机非线性状态估计:扩展精确高斯变分推理学习方法[J].国防科技大学学报,2025,47(3):141-150,10.基金项目
国家自然科学基金资助项目(61473144) （61473144）
