Indoor positioning plays a pivotal role in a wide range of applications, from smart homes to industrial automation. In this paper, we propose a comprehensive approach for accurate positioning in indoor environments through the integration of existing Wi-Fi and Bluetooth Low Energy (BLE) devices. The proposed algorithm involves acquiring the received signal strength indicator (RSSI) data from these devices and capturing the complex interactions between RSSI and positions. To enhance the accuracy of the collected data, we first use a Kalman filter for denoising RSSI values, then categorize them into distinct classes using the K-nearest neighbor (KNN) algorithm. Incorporating the filtered RSSI data and the class information obtained from KNN, we then introduce a recurrent neural network (RNN) architecture to estimate the positions with a high precision. We further evaluate the accuracy of our proposed algorithm through testbed experiments using ESP32 system on chip with integrated Wi-Fi and BLE. The results show that we can accurately estimate the positions with an average error of 61.29 cm, which demonstrates a 56\% enhancement compared to the state-of-the-art existing works.

翻译：室内定位在从智能家居到工业自动化的广泛应用中发挥着关键作用。本文提出一种通过整合现有Wi-Fi与蓝牙低功耗（BLE）设备的综合性室内精确定位方法。该算法通过采集上述设备的接收信号强度指示（RSSI）数据，并捕捉RSSI与位置之间的复杂交互关系。为提升采集数据的准确性，我们首先利用卡尔曼滤波器对RSSI值进行去噪处理，随后采用K近邻（KNN）算法将其划分为不同类别。结合滤波后的RSSI数据与KNN分类结果，我们引入递归神经网络（RNN）架构进行高精度位置估计。进一步，我们通过搭载集成Wi-Fi和BLE的ESP32片上系统搭建测试平台，验证所提算法的定位精度。实验结果表明，该方法可实现平均误差61.29厘米的精准定位，相较于现有最先进技术提升56%。