Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

Indoor target tracking based on Wi-Fi signals entails the integration of both the target motion model and the measurement model. However, a single motion model is inadequate to match the changing motion state of the target at each moment, and linearization of the measurement model may introduce additional errors. To tackle these problems, we propose a localization method called IMM-MLKF. For the measurement equation, we adopt the fingerprinting model to perform maximum likelihood estimation on the target, obtaining the mean and covariance matrix of the target state distribution. This method eliminates the error introduced by linearization. For the motion equation, multiple models are used for parallel computation. The likelihood function values generated by each model from the measurements are used as their model confidences, enabling the combining model to match the motion of the target. We validated the method in two different scenarios using channel state information as the fingerprint feature. Our results show that compared with similar Bayesian filtering methods based on interactive multiple models, this method has superior tracking accuracy. Additionally, this method’s operating efficiency is higher than particle filtering methods.