| 邢方方,惠向晖.基于 seq2seq 模型的室内 WLAN 定位方法[J].电子测量与仪器学报,2020,34(11):93-100 |
| 基于 seq2seq 模型的室内 WLAN 定位方法 |
| Seq2seq model based WLAN indoor positioning |
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| DOI： |
| 中文关键词: 序列到序列模型 WLAN 定位 神经网络 |
| 英文关键词:seq2seq based model WLAN based indoor positioning neural network |
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| 摘要点击次数: 107 |
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| 中文摘要: |
| 基于 WLAN(wireless local area network)的定位在智能家居、室内导航、个性化服务等应用中扮演着重要的角色。 研究了
基于序列到序列 seq2seq 模型的室内 WLAN 定位方法。 该方法基于在自然语言处理中广泛应用的 seq2seq 神经网络模型,通过
样本数据学习信号指纹空间中的时间序列和坐标空间中的时间序列的关系。 经过滤波等预处理后,再进行样本增强,并设计合
理的输入输出及代价函数,本方法能够实现更高精度定位。 实测的数据表明,提出的方法相比于其他几种基于神经网络的定位
方法,度量学习 RFSM 方法、去噪自编码器 DAE 方法、f-RNN 方法,平均定位精度分别提高了 23%、11%和 20%。 |
| 英文摘要: |
| Wireless local area network (WLAN) based positioning plays an important role in smart homes, indoor navigation and userdefined services. Proposed a seq2seq model based WLAN indoor positioning method. The method is based on the seq2seq neural network
model, which is widely adopted in the natural language processing (NLP). The seq2seq model can learn the relationships of the time
sequences in the signal domain and the coordinate domain. After carefully designed signal pre-processing, sample augmentation and
reasonable loss function, the learned model can be adopted for positioning. According to the experimental results from our collected data,
our method can improve positioning accuracy compared with some other neural network based methods, including the RFSM method, the
denoising autoencoder (DAE) based method and the f-RNN method, by 23%, 11% and 20% respectively. |
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