Turkish Journal of Electrical Engineering and Computer Sciences




In order to achieve high precision on indoor location, a Wi-Fi indoor location method based on improved back propagation (BP) neural network is proposed. The classical BP neural network is optimized in real time by the ant colony optimization algorithm. Meanwhile, the momentum term is introduced to construct an improved four-layer BP neural network model. The model uses the Wi-Fi signal feature as the input of the BP neural network and succeeds in the area classification under multiple Wi-Fi signal features. The experimental results demonstrate that the improved BP neural network can increase the classification accuracy of the classifier effectively, and achieve a high-precision indoor area location. Furthermore, it performs better practical results while ensuring the time complexity. The advantages of this method are high practicability, low cost, high prediction classification accuracy, and robust stability, which can achieve the efficient classification of the short-range area.


Indoor Wi-Fi location, back propagation neural network, ant colony optimization algorithm, momentum term

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