Modeling and compensation of navigation error based on self-supervised LSTM network in complex environment
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NETL
NSTL
万方数据
针对复杂环境下惯导系统存在交互影响和导航误差难以辨识的问题,提出了一种基于自监督长短期记忆(LSTM)网络智能组合模型的导航误差补偿方法.模型中的自监督温变速率模块不受到温度传感器精度的限制,从而实时计算更精确的温变速率,进一步提升了模型导航误差辨识的能力.在实验部分,基于多种复杂环境下的实验数据,通过消融实验验证了自监督模块的有效性.以飞行数据的北向速度为例,补偿前后的最大速度绝对误差分别为 1.607 m/s 和 0.357 m/s.实验结果说明了所提方法可以减小复杂环境下的速度和位置误差,从而提升惯性导航精度.
Aiming at the problems that the inertial navigation system has interactive effects and navigation errors are difficult to identify in complex environment,a navigation error compensation method based on self-supervised long short term memory(LSTM)network intelligent combination model is proposed.The self-supervised temperature change rate module is proposed to overcome the limit of temperature sensor precision and provide temperature change rate in real time,which further improves the ability of the model to identify navigation error.In the experiment section,the effectiveness of the self-supervised module is verified through ablation experiments under various complex environment.Taking the northward velocity of flight test data as an example,the maximum absolute velocity error before compensation is 1.607 m/s,and 0.357 m/s after.Experimental results prove that the velocity and position error under complex physics environment could be effectively reduced,and the pure inertial navigation performance is therefore improved.
inertial navigationlong short term memory networknavigation error compensationself-supervised learning
NETL
NSTL
万方数据
