Fingerprinting localization based on affinity propagation clustering and artificial neural networks

Fingerprinting localization techniques have been intensively studied in indoor WLAN environment. Artificial neural networks (ANN) based fingerprinting technique could potentially provide high accuracy and robust performance. However, it has the limitations of slow convergence, high complexity and large memory storage requirement, which are the bottlenecks of its wide application, especially in the case of a large-scale indoor environment and the terminal with limited computing capability and memory resources. In this paper, we firstly introduce affinity propagation (AP) clustering algorithm to reduce the computation cost and memory overhead, and then explore the properties of radio basis function (RBF) neural networks that may affect the accuracy of the proposed fingerprinting localization systems. We carry out various experiments in a real-world setup where multiple access points are present. The detailed comparison results reveal how the clustering algorithm and the neural networks affect the performance of the proposed algorithms.