This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration\r\nof fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio\r\npropagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and\r\ntrilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it\r\nuses an online phase of fingerprinting and calculates nearest neighbors (NN) of the target node, while in the second stage it uses\r\ntrilateration approach to estimate the coordinate without the use of radio propagation model.The distance between calculated NN\r\nand detective access points (AP) is estimated using Euclidian distance formula.Thus, distance between NN and APs provides radii\r\nfor trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter\r\nis used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance\r\nof proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting\r\napproaches, respectively.
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