Indoor Geomagnetic Positioning Using the Enhanced Genetic Algorithm-Based Extreme Learning Machine
Meng Sun, Yunjia Wang, Shenglei Xu, Hongchao Yang, Kewei Zhang
Abstract
Indoor positioning using the geomagnetic field has become a popular technique because of the infrastructure-free characteristic and the ubiquitous magnetic signals in indoor environments. Geomagnetic intensity will generate fluctuations due to the factors such as device heterogeneity, dates, and electronic facilities. Stable geomagnetic data are essential for high-precision positioning. To find a robust and low time-consuming geomagnetic positioning model (GPM), the extreme learning machine (ELM) optimized by the enhanced genetic algorithm (EGA) is constructed. The EGA with three optimization strategies is designed to find the best initial parameters solutions for the ELM. Magnetic patterns collected by different participants using different mobile phones on different dates are extracted characteristics and divided into segments for ELM training. Extensive experiments are conducted to evaluate the proposed model’s performance. The experimental results demonstrate that the EGA-based ELM model can achieve meter-level location accuracy and has good robustness under different testing conditions. The model construction is much faster than that of the popular learning algorithms, such as the convolutional neural networks (CNNs) and backpropagation (BP) networks.