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首页> 外文会议>The IEEE 17th International Conference on Advanced Communication Technology : "Named Data Networking - A Future Internet Architecture" >D2D distance measurement using Kalman filter algorithm for distance-based service in an office environment
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D2D distance measurement using Kalman filter algorithm for distance-based service in an office environment

机译：使用卡尔曼滤波算法的D2D距离测量，用于办公环境中的基于距离的服务

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The device-to-device (D2D) distance-based service is affected by distance between the D2D devices. Thus, the wireless distance measurement between devices is an indispensable technology in D2D applications. When mobile devices use the distance between D2D devices in the D2D application, the distance should be measured easily and accurately. Several methods can be used to measure the distance between D2D devices. The RSSI system can be used to estimate the D2D distance easily and inexpensively because most D2D devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. We tested the relationship between distance and a Bluetooth RSSI to look into the inaccuracy of the RSSI in an office environment. Through the results of this test, we realize that the D2D distance is hard to be classified due to the inaccuracy of Bluetooth RSSI. Therefore, filtering algorithm should be applied to reduce the inaccuracy of the Bluetooth RSSI. Nowadays, Kalman filter is widely used in many research fields and shows good performance. Thus, we use the Kalman filter to increase the accuracy of Bluetooth RSSI. To evaluate the effectiveness of the Kalman filter algorithm, we tested the D2D distance measurement in an office environment. The result of this test indicates that the Kalman filter reduces the inaccuracy of Bluetooth RSSI significantly. In addition, we describe wireless body area network (WBAN) region classification using Bluetooth RSSI and Kalman filter in an office environment.
机译：基于设备到设备（D2D）距离的服务受D2D设备之间的距离影响。因此，设备之间的无线距离测量是D2D应用程序中必不可少的技术。当移动设备在D2D应用程序中使用D2D设备之间的距离时，应轻松，准确地测量距离。可以使用几种方法来测量D2D设备之间的距离。 RSSI系统可用于轻松，廉价地估算D2D距离，因为大多数D2D设备都可以估算接收到的信号强度。但是，由于不准确，使用RSSI估算距离很困难。我们测试了距离与蓝牙RSSI之间的关系，以研究办公环境中RSSI的不准确性。通过该测试的结果，我们意识到由于蓝牙RSSI的不准确，很难对D2D距离进行分类。因此，应采用过滤算法以减少蓝牙RSSI的不准确性。如今，卡尔曼滤波器已在许多研究领域中得到广泛应用，并显示出良好的性能。因此，我们使用卡尔曼滤波器来提高蓝牙RSSI的准确性。为了评估Kalman滤波器算法的有效性，我们在办公环境中测试了D2D距离测量。该测试的结果表明，卡尔曼滤波器可显着降低蓝牙RSSI的不准确性。此外，我们在办公室环境中使用蓝牙RSSI和卡尔曼滤波器描述无线人体局域网（WBAN）区域分类。

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