基于 seq2seq 模型的室内 WLAN 定位方法

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%。

Seq2seq model based WLAN indoor positioning

Wireless local area network (WLAN) based positioning plays an important role in smart homes, indoor navigation and user-defined 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.