GNSS拒止时基于并行CNN-BiLSTM回归和残差补偿的UAV导航误差校正方法  

Method Based on Parallel CNN-BiLSTM Regression and Residual Compensation for Correcting UAV Navigation Error in GNSS Denied Environment

作　　者：韩宾[1] 邵一涵 罗颖[1] 田杰 曾闵[1] 江虹[1] HAN Bin;SHAO Yihan;LUO Ying;TIAN Jie;ZENG Min;JIANG Hong(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621000,China;Institute of Electronic Engineering,China Academy of Engineering Physics,Mianyang 621900,China)

机构地区：[1]西南科技大学信息工程学院,四川绵阳621000 [2]中国工程物理研究院电子工程研究所,四川绵阳621900

出　　处：《湖南大学学报（自然科学版）》2024年第8期57-69,共13页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(61771410);四川省科技计划资助项目(2023NSFSC1373,2024NSFSC0476);西南科技大学博士基金项目(20zx7122,23zx7101)。

摘　　要：全球导航卫星系统(GNSS)拒止时,GNSS/惯性导航系统(INS)组合导航系统的性能严重下降,导致无人机集群导航误差快速发散.目前,利用神经网络预测位置与速度代替GNSS导航信息可校正无人机INS误差,但该方法仍存在定位误差较高且在轨迹突变时预测精度急剧下降的问题.因此,提出了一种基于卷积-双向长短时记忆网络联合残差补偿的位置与速度预测方法,用于提高位置与速度预测精度.首先,针对GNSS拒止后GNSS/INS组合导航系统定位误差较高的问题,提出卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的融合模型,该模型可建立惯性测量单元(IMU)动力学测量数据与GNSS导航信息之间的关系,实现较准确的位置和速度预测.其次,针对轨迹突变时预测效果急剧下降的问题,提出并行CNNBiLSTM回归架构,在预测位置与速度的同时,挖掘IMU动力学测量数据、预测值与预测残差之间的关系,预测并补偿预测残差,增强模型在轨迹突变时的预测精度.仿真结果表明,所提模型在预测准确性、有效性和稳定性方面都优于CNN-LSTM、LSTM网络模型.When the global navigation satellite system(GNSS) signal is unavailable,the performance of GNSS/inertial navigation system(INS) integrated navigation system significantly degrades,which leads to the rapid divergence of INS errors of UAV swarms.At present,the neural network model is used to predict the position and speed instead of GNSS navigation information to correct the positioning error of the INS.However,this method suffers from high positioning errors and a sharp decline in prediction accuracy when the trajectory changes suddenly.Therefore,a position and velocity prediction method based on convolution neural networks(CNN)-bidirectional long short-term memory network(BiLSTM) joint residual compensation model is proposed to compensate for inertial navigation errors and improve position and velocity positioning accuracy.Firstly,aiming at the problem of high positioning error of GNSS/INS integrated navigation system after GNSS denial,a time series prediction network is formed by fusing CNN and BiLSTM to train and establish the relationship between inertial measurement unit(IMU) dynamics measurement and GNSS information,so as to realize position and speed prediction.Secondly,aiming at the problem that the prediction effect drops sharply when the trajectory changes abruptly,CNN-BiLSTM is used again to mine the relationship between the IMU dynamics measurement,prediction value and prediction residual,and to predict and compensate the prediction residual.Simulation results show that the proposed model outperforms traditional CNN-LSTM and LSTM network models in terms of prediction accuracy,effectiveness,and stability.

关 键 词：全球导航卫星系统拒止 卷积神经网络 双向长短时记忆网络 残差补偿 自适应卡尔曼滤波 

分 类 号：TN96[电子电信—信号与信息处理]