飞控与探测2025，Vol.8Issue(5)：1-10,10.DOI:10.20249/j.cnki.2096-5974.2025.05.001
基于概率时间序列注意力模型的概率图优化组合导航算法
Probabilistic Graph Optimization Integrated Navigation Based on Probabilistic Time Series Transformer
摘要
Abstract
To satisfy the modern track traffic's demand for maintaining high precision and continui-ty of navigation under complex environmental conditions,and to address the issue of positioning drift caused by data outages in nonlinear dynamic integrated navigation systems,this paper propo-ses a novel adaptive estimation method for observation errors in nonlinear dynamic integrated navi-gation systems.This method is based on a Probabilistic Time Series Transformer model,aiming to resolve the aforementioned issues.By introducing self-learning capabilities through the Probabi-listic Time Series Transformer,the method adaptively adjusts the impact of state prediction and observation information outages on the dynamic navigation system.The Probabilistic Time Series Transformer is composed of a dual-loop system of a generative model and an inference model,combined with LSTM network to tackle the challenges of multivariate time series modeling.The integrated navigation system based on the Probabilistic Time Series Transformer optimizes the er-ror compensation mechanism by establishing a relationship between the current Kalman filter gain and the optimal estimation error,thereby improving the accuracy and stability of the nonlinear navigation system.Experimental results demonstrate that the proposed method not only effectively controls the impact of GNSS outages on the nonlinear navigation system but also accu-rately estimates and compensates for observation model system errors.The average positioning er-ror in various complex scenarios is less than 10 m.The suppression of positioning drift in the ob-servation model is better than that of other filtering methods.关键词
组合导航/注意力模型/时间序列/轨道交通/卡尔曼滤波Key words
integrated navigation/attention model/time series/track traffic/Kalman filtering分类
电子信息工程引用本文复制引用
张雷,徐钦,赵万良,成宇翔,孙研..基于概率时间序列注意力模型的概率图优化组合导航算法[J].飞控与探测,2025,8(5):1-10,10.基金项目
国家自然科学基金(重点项目)(52432012) （重点项目）
