An adaptive indoor localization model based on Cauchy-PSO optimized BPNN

Abstract

Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate “high-dimensional disasters”, Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment.