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.

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