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Utilizing CSI and RSSI to Achieve High-Precision Outdoor Positioning: A Deep Learning Approach

机译：利用CSI和RSSI实现高精度的户外定位：一种深度学习方法

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Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.
机译：基于位置的服务（LBS）已被广泛部署。 LBS的关键组件之一是定位算法。对于室外环境，全球定位系统（GPS）已用作默认定位方案。但是，GPS需要到卫星的视线。当视线被挡住时，GPS只会停止工作。为了解决GPS的问题，已经提出了各种基于WiFi的定位方案。但是，现有方法的定位精度不能令人满意。在本文中，我们提出了一种名为基于深度学习的定位（DLP）的高精度定位方案。从技术上讲，DLP同时利用接收信号强度指示器（RSSI）和信道状态信息（CSI）来提高定位精度。详细地，使用深度神经网络对接收到的RSSI和CSI测量值进行建模，从而获得令人满意的定位精度。我们从大型测试台获得的实验结果表明，DLP在定位精度方面优于现有的定位方案。

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