The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application such as Proximity-Based Services (PBS), and Location-Based Services (LBS). For these systems to perform, it is essential to have reliable hardware and predict a user's position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize Received Signal Strength Indicators (RSSI) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this work, Bluetooth Low Energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30 % in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

中文翻译：

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通过贝叶斯滤波提高 BLE 信标接近度估计精度

万物之间的相互联系不断扩大，这使每个人都可以提高与周围环境的互动水平。物联网 (IoT) 设备用于大量上下文感知应用程序，例如基于接近的服务 (PBS) 和基于位置的服务 (LBS)。为了让这些系统发挥作用，必须拥有可靠的硬件并高精度地预测用户在该区域中的位置，以便区分小区域内的个人。已经提出了多种利用接收信号强度指示器 (RSSI) 的无线解决方案来为室内环境提供 PBS 和 LBS，尽管每种解决方案都有其自身的缺点。在这项工作中，蓝牙低功耗 (BLE) 信标在接近度估计的准确性方面进行了检查。具体来说，移动应用程序与三种贝叶斯滤波技术一起开发，以提高 BLE 信标邻近度估计精度。这包括卡尔曼滤波器、粒子滤波器和非参数信息 (NI) 滤波器。由于 RSSI 受环境影响很大，因此进行了实验以检查来自三个流行供应商的信标在两种不同环境中的性能。误差根据平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行比较。根据实验结果，当信标和接收器在 3 m 以内时，贝叶斯滤波器与传统滤波相比可以将邻近估计精度提高 30%。这包括卡尔曼滤波器、粒子滤波器和非参数信息 (NI) 滤波器。由于 RSSI 受环境影响很大，因此进行了实验以检查来自三个流行供应商的信标在两种不同环境中的性能。误差根据平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行比较。根据实验结果，当信标和接收器在 3 m 以内时，贝叶斯滤波器与传统滤波相比可以将邻近估计精度提高 30%。这包括卡尔曼滤波器、粒子滤波器和非参数信息 (NI) 滤波器。由于 RSSI 受环境影响很大，因此进行了实验以检查来自三个流行供应商的信标在两种不同环境中的性能。误差根据平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行比较。根据实验结果，当信标和接收器在 3 m 以内时，贝叶斯滤波器与传统滤波相比可以将邻近估计精度提高 30%。误差根据平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行比较。根据实验结果，当信标和接收器在 3 m 以内时，贝叶斯滤波器与传统滤波相比可以将邻近估计精度提高 30%。误差根据平均绝对误差 (MAE) 和均方根误差 (RMSE) 进行比较。根据实验结果，当信标和接收器在 3 m 以内时，贝叶斯滤波器与传统滤波相比可以将邻近估计精度提高 30%。