摘要
针对遥感卫星视轴指向微角度测量系统在在轨运行过程中受到复杂扰动环境影响和噪声干扰的问题,提出一种融合双向长短期记忆网络(BiLSTM)与卷积神经网络(CNN)的视轴指向在轨测量数据去噪方法,以提升测量数据的精度与可靠性。该方法首先结合微角度测量物理建模与蒙特卡洛仿真,对测量数据中噪声的分布特性与时空关联性进行系统性分析与验证。在此基础上,利用现有在轨测量数据构建高质量标注样本集,保障模型训练的准确性与泛化能力。所提出的BiLSTM-CNN网络架构中,BiLSTM用于捕捉测量序列中的双向时序依赖关系,CNN用于提取局部空间特征;同时引入梯度平衡机制以缓解训练过程中可能出现的梯度消失与过拟合问题,从而提升模型在复杂输入下的稳定性与鲁棒性。实验在多个典型神经网络模型上开展对比评估,结果表明:在a 1轴向测量数据中,所提模型在均方误差(MSE)、均方根误差(RMSE)及平均绝对误差(MAE)指标上分别较表现最好的BiLSTM模型降低7.9%、4.3%和16.4%;在b1轴向中,分别较表现最好的GRU模型分别降低4.6%、2.3%和6.4%。上述结果充分验证了本方法在多轴向测量数据处理中的稳健性与普适性,具备优异的噪声识别与抑制能力,为高精度遥感姿态测量任务提供了有效的数据处理手段,具有良好的工程实用价值和应用前景。
To address the challenges posed by complex perturbation environments and noise interference affecting line-of-sight(LOS)micro-angle measurement systems onboard remote sensing satellites during in-orbit operations,this study proposes a denoising method that integrates Bidirectional Long Short-Term Memory(BiLSTM)and Convolutional Neural Networks(CNN)to enhance the accuracy and reliability of measurement data.By combining physical modeling of micro-angle measurement with Monte Carlo simulations,the noise distribution and spatiotemporal correlations in the data are systematically analyzed and validated.A high-quality labeled dataset is then constructed from existing in-orbit measurements to ensure robust and generalizable model training.In the proposed BiLSTM-CNN architecture,BiLSTM captures bidirectional temporal dependencies,while CNN extracts local spatial features.A gradient balancing mechanism is incorporated to mitigate issues such as gradient vanishing and overfitting,thereby improving model stability under complex conditions.Experimental comparisons with typical neural network models show that,on the a 1-axis,the proposed model reduces MSE,RMSE,and MAE by 7.9%,4.3%,and 16.4%respectively compared to the best-performing BiLSTM,and on the b 1-axis,achieves respective reductions of 4.6%,2.3%,and 6.4%compared to the best-performing GRU.These results demonstrate the robustness,generalizability,and effective noise suppression capability of the proposed method,offering a practical and promising solution for high-precision satellite attitude measurement.
作者
高宇
张旭
李红
庄炜
祝连庆
Gao Yu;Zhang Xu;Li Hong;Zhuang Wei;Zhu Lianqing(Key laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument,Beijing Information Science&Technology University,Beijing 100192,China;Beijing Laboratory of Optical Fiber Sensing and System,Beijing Information Science&Technology University,Beijing 100016,China;Guangzhou Nansha Intelligent Photonic Sensing Research Institute,Guangzhou 511462,China)
出处
《仪器仪表学报》
北大核心
2025年第6期330-337,共8页
Chinese Journal of Scientific Instrument
基金
北京学者计划研究项目(BJXZ2021-012-00046)资助。
关键词
微角度测量系统
双向长短期记忆网络
卷积神经网络
去噪算法
蒙特卡罗分析
micro-angle measurement system
bidirectional long short-term memory network
convolutional neural network
denoising algorithm
monte carlo analysis
