  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.
