Ensemble Strategy Utilizing a Broad Learning System for Indoor Fingerprint Localization

Indoor positioning technology based on Wi-Fi fingerprint recognition has been widely studied owing to the pervasiveness of hardware facilities and the ease of implementation of software technology. However, the similarity-based method is not sufficiently accurate, whereas the offline training of the neural network-based method is overly time consuming. An efficient model with high positioning accuracy is therefore not yet available. We propose a stacking ensemble broad learning localization system using channel state information as a fingerprint, which is termed EnsemLoca. A bootstrapping method is used to build the training set, which enables the EnsemLoca system to build the base learner in parallel by using bagging. The broad learning system (BLS), which is a novel neural network model, as a base learner, not only has the advantage of time complexity but also offers a sparse representation in which the features are filtered. A unique base learner is constructed by randomly selecting the samples and features, and they are combined by stack generalization. The experimental results show that the EnsemLoca system achieves higher accuracy than several machine-learning algorithms in both line-of-sight (LOS) and non-LOS environments, and is even stronger than deep neural networks characterized by accuracy. At the same time, it has the same theoretical complexity as BLS, which greatly reduces the offline training time.