文章摘要
闫世霖,吴德伟,王伟,戴传金,朱浩男.循环神经网络辅助GNSS/SINS组合导航方法及性能分析[J].空军工程大学学报:自然科学版,2021,22(5):61-66
循环神经网络辅助GNSS/SINS组合导航方法及性能分析
An Analysis of Method and Performance for GNSS/SINS Integrated Navigation Assisted by Recurrent Neural Network
  
DOI:
中文关键词: GNSS/SINS组合导航  循环神经网络  GNSS信号中断
英文关键词: GNSS/SINS integrated navigation  recurrent neural network  GNSS signal interrupt
基金项目:国家自然科学基金(61973314)
作者单位
闫世霖,吴德伟,王伟,戴传金,朱浩男 1.空军工程大学信息与导航学院西安7100772.中国电子科技集团公司第二十研究所西安710068 
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中文摘要:
      针对GNSS信号中断时组合导航系统误差迅速发散的问题,提出了使用循环神经网络(RNN)来辅助组合导航系统的方法,RNN可以分别基于当前和过去的位置以及速度样本进行训练,使神经网络更好地处理系统中的时序信号,从而能够更加精确地预测SINS的位置和速度误差。采用无人机飞行试验数据验证了该算法在卫星信号中断时导航精度平均提升了77%,并且满足导航所需的实时性要求,与传统的径向基神经网络辅助的组合导航系统相比,其位置和速度的均方根误差平均降低了39%。
英文摘要:
      Aimed at the problems that integrated navigation system is rapid divergent in error when global navigation satellite system (GNSS) signal is interrupted, most of the current methods are to adopt fully connected neural network, but this can only deal with the mapping relationship between input and output at a single moment with being ignorant of the dependence of errors on the past values of strapdown inertial navigation system (SINS), a method of using recurrent neural network (RNN) to assist the integrated navigation system is proposed. RNN can train on the basis of the current and past position and speed samples respectively, so that the neural network can better process the timing signals in the system, and predict the position and speed errors of SINS more accurately. UAV flight test data are adopted to verify the method. The result shows that the navigation accuracy of this algorithm is increased by 77% on average when the satellite signal is interrupted, and this meets the real time requirements required by navigation. Compared with the traditional RBF neural network assisted integrated navigation system, the root mean square error of its position and velocity is reduced by 39% on average.
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