计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 404-409.doi: 10.11896/jsjkx.200700170
李达, 雷迎科, 张海川
LI Da, LEI Ying-ke, ZHANG Hai-chuan
摘要: 由于在复杂环境中可以取得良好的定位效果,基于指纹的定位技术一直是研究的热点。通过利用长期演进(Long Term Evolution,LTE)网络,一种基于深度学习的指纹定位方法被提出用来构建良好的定位系统。受到计算机视觉技术的启发,带有地理位置标记的信号指纹被转化为灰度图片然后进行定位,并以最终构建好的灰度图片数据集的分类准确率来表示定位的准确率。文中采用了一种两级分步训练的方法来实现深度神经网络(Deep Neural Network,DNN)的分类识别。首先,利用深度残差网络(Deep Residual Network,Resnet)对指纹库进行预训练并得到粗糙定位模型,然后利用基于反向传播神经网络(Back Propagation Neural Network,BPNN)的迁移学习算法进一步提取信号特征并得到精确定位模型。实验在真实室外环境下进行,且实验结果表明提出的定位系统可以在室外环境下取得较高精度的定位效果。
中图分类号:
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