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DRVAT: Exploring RSSI series representation and attention model for indoor positioning
International Journal of Intelligent Systems ( IF 5.0 ) Pub Date : 2021-10-18 , DOI: 10.1002/int.22712
Haojun Ai 1 , Xu Sun 1 , Jingjie Tao 1 , Mengyun Liu 2 , Shengchen Li 3
Affiliation  

Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and self-attention (AT) among the pairs of MAC-RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre-training on all MAC-RSSIs. Then we exploit self-attention mechanism to learn the latent spatial relation of beacons. Finally, MAC-RSSIs labeled with locations are used to fine-tune the model for estimating location. Localization accuracy results demonstrated the superior performance as compared with other positioning methods, and the visualization of DRV and attention mechanism are consistent with the spatial deployment of BLE.

中文翻译:

DRVAT:探索室内定位的RSSI系列表示和注意力模型

尽管蓝牙低功耗(BLE)指纹定位已成为热门研究课题并取得了令人鼓舞的成果,但由于RSSI的剧烈波动,在现实场景中很难根据短时接收信号强度指示(RSSI)序列来预测位置。我们引入了一个新的视角来通过无线电地图指纹来看待室内定位问题。我们认为,即使信标可以独立部署,RSSI 系列也具有一定的空间关系,因为它们共同存在于同一物理空间中。它们的信号在不同的室内位置共存隐含地传达了潜在的关系。与试图在感测信号和相应位置之间找到直接映射的现有方法不同,我们从输入数据中探索信标的空间关系以估计位置。我们提出了一种深度学习定位系统,称为 DRVAT,它基于 MAC-RSSI 对之间的分布式表示向量 (DRV) 和自注意力 (AT)。首先,我们通过对所有 MAC-RSSI 的预训练获得代表低维密集特征的 DRV。然后我们利用自我注意机制来学习信标的潜在空间关系。最后,标有位置的 MAC-RSSI 用于微调模型以估计位置。定位精度结果表明与其他定位方法相比具有优越的性能,并且 DRV 的可视化和注意力机制与 BLE 的空间部署一致。它基于 MAC-RSSI 对之间的分布式表示向量 (DRV) 和自注意力 (AT)。首先,我们通过对所有 MAC-RSSI 的预训练获得代表低维密集特征的 DRV。然后我们利用自我注意机制来学习信标的潜在空间关系。最后,标有位置的 MAC-RSSI 用于微调模型以估计位置。定位精度结果表明与其他定位方法相比具有优越的性能,并且 DRV 的可视化和注意力机制与 BLE 的空间部署一致。它基于 MAC-RSSI 对之间的分布式表示向量 (DRV) 和自注意力 (AT)。首先,我们通过对所有 MAC-RSSI 的预训练获得代表低维密集特征的 DRV。然后我们利用自我注意机制来学习信标的潜在空间关系。最后,标有位置的 MAC-RSSI 用于微调模型以估计位置。定位精度结果表明与其他定位方法相比具有优越的性能,并且 DRV 的可视化和注意力机制与 BLE 的空间部署一致。然后我们利用自我注意机制来学习信标的潜在空间关系。最后,标有位置的 MAC-RSSI 用于微调模型以估计位置。定位精度结果表明与其他定位方法相比具有优越的性能,并且 DRV 的可视化和注意力机制与 BLE 的空间部署一致。然后我们利用自我注意机制来学习信标的潜在空间关系。最后,标有位置的 MAC-RSSI 用于微调模型以估计位置。定位精度结果表明与其他定位方法相比具有优越的性能,并且 DRV 的可视化和注意力机制与 BLE 的空间部署一致。
更新日期:2021-10-18
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