基于双通道Residual-LSTM的SINS/GNSS组合导航算法

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中文摘要：针对全球导航卫星系统信号中断情况下SINS/GNSS组合导航系统无法持续进行误差校正的问题,提出一种基于双通道Residual-LSTM的SINS/GNSS组合导航算法.首先,考虑到SINS经度、纬度误差传播特性不同所导致的模型输入、输出信息之间的非线性相关性差异化,构建具有不同权重系数的双通道长短期记忆神经网络模型结构,并引入遗忘信息共享机制自适应地利用历史导航数据对经度、纬度信息进行拟合预测.其次,针对深层神经网络存在的模型退化和梯度消失问题,在多层双通道LSTM网络之间建立残差高速通道形成Residual-LSTM模型结构,以增加不同网络层次之间的信息传播路径.最后,通过实船数据验证本文所提算法的有效性.实验结果表明,与基于常规智能方法的SINS/GNSS组合导航算法相比,所提组合导航算法在GNSS信号中断期间经度误差降低了 51.97%,纬度误差降低了 31.45%.

外文标题：SINS/GNSS integrated navigation algorithm based on dual-channel Residual-LSTM

外文摘要：In response to the issue of the inability of SINS/GNSS integrated navigation system to continuously correct errors in the event of a global navigation satellite system signal interruption,a dual-channel Residual-LSTM based SINS/GNSS integrated navigation algorithm is proposed.First,considering the nonlinear correlation difference between the input and output information of the model caused by the different transmission characteristics of SINS longitude and latitude errors,a dual-channel long and short-term memory neural network model structure with different weight coefficients was constructed.A adaptive forgetting information sharing mechanism was introduced to effectively use historical navigation data to fit and predict the longitude and latitude information.Second,in view of the model degradation and gradient vanishing problems existing in deep neural networks,a Residual-LSTM model structure is formed by establishing a Residual-LSTM high-speed channel between multi-layer and dual-channel LSTM networks to increase the information propagation paths between different network layers.Finally,the effectiveness of the proposed algorithm is verified by the real ship data.The experimental results show that compared with the SINS/GNSS integrated navigation algorithm based on conventional intelligence method,the proposed integrated navigation algorithm reduces the longitude error by 51.97% and latitude error by 31.45% during the GNSS signal interruption period.

外文关键词：

SINS/GNSS integrated navigationGNSS interruptdual channel structureresidual long short term memory neural networkdeep neural network

作者：

奔粤阳、王奕霏、李倩、魏廷枭、周一帆

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作者单位：

哈尔滨工程大学智能科学与工程学院哈尔滨 150001

关键词：

SINS/GNSS组合导航 GNSS中断双通道结构残差长短期记忆神经网络深度神经网络

基金：

黑龙江省自然科学基金国家自然科学基金国家自然科学基金

项目编号：

YQ2021E0115237136851979047

出版年：

2024

DOI：

10.19650/j.cnki.cjsi.J2311607

仪器仪表学报

中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心

影响因子：2.372

ISSN：0254-3087

年,卷(期)：2024.45(4)