Indoor Positioning of RBF Neural Network Based on Improved Fast Clustering Algorithm Combined With LM Algorithm

Abstract

In the indoor environment, due to weak receiver signals, environmental noise, multipath interference, and non-line-of-sight propagation, the traditional positioning algorithms based on received signal strength indication (RSSI) have many problems, such as inaccurate positioning results, great dependence on the signal propagation path loss model, and high time and labor costs. This paper studied the wireless indoor positioning algorithm based on neural network. A weighted median-Gaussian filtering method is proposed to preprocess RSSI and establish a location fingerprint database. An indoor positioning algorithm based on an improved fast clustering algorithm combined with a Levenberg–Marquardt (LM) algorithm is proposed. The improved clustering algorithm is used to design the network structure, initialize the number of radial basis function (RBF) neurons, find the local density peak as the cluster center to achieve rapid clustering of samples, and adjust the parameters of the kernel function of the hidden layer neurons. And the LM algorithm is used for numerical optimization. In order to verify the performance of the algorithm, positioning experiments are performed in the library. The error rate was reduced by 26.2% compared with the RBF network. The positioning results data confirm the effectiveness and applicability of the proposed algorithm.