Empirical fingerprinting is currently one of the most efficient localization methods in indoor environments, due to the now ubiquitous deployment of wireless local area networks. A fingerprint is the pattern of received signal strengths from all the access points visible at a particular position. Reference fingerprints are obtained from extensive measurement campaigns and used during an offline phase to construct suitable radio maps of the environment of interest. However, this approach is very site-specific and the radio maps require therefore to be regularly updated in order to take into account changes in the environment, i.e. a labor-intensive and time-consuming task. Hence, ray-tracing simulations are instead used in this paper to construct these radio maps based on deterministic prediction of the radio wave propagation. To validate this approach, field experiments were conducted in an indoor office environment. The results show (i): the sensitivity of fingerprints to small-scale fading and human shadowing, as well as (ii): good agreement between the measured and ray-tracing simulated fingerprints, especially with strong line-of-sight.

The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning (“PDR”). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model (“GMM”), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model (“GHM”). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.

Mobile indoor navigation systems, such as those provided by indoo.rs, often use WiFi or Bluetooth RSSI fingerprint maps, as this approach works on most mobile devices. Because of device and antenna characteristics, not all devices report the RSSI for a given input. Therefore, it is difficult to combine data from multiple devices. In the indoo.rs SLAM Crowd Engine, which is used to create the radio maps used for indoor navigation, this is a serious problem, as it fuses pseudonymous navigating user data from arbitrary devices.

The radio signal Received Signal Strength Indicator (RSSI) based localization method is one of the most often adopted indoors localization methods, due to the fact that it can be used by any handhold devices on the market without any modification. This paper will present a new deep learning inspired model to predict locational distance/similarity between two points based on their RSSI measurement. This model uses carefully designed input features, neural network architecture, as well as purposely crafted pre-training to combine the features and statistics from multiple hand crafted RSSI to locational similarity models (reference models). The reference models include a RSSI difference based model (euclidean distance), number of visible signals by both measurements (Jaccard distance) and the rank difference of commonly visible signals in the comparing measurements (Spearman's footrule). Our evaluation shows the three reference models have very distinct strengths and weaknesses: the euclidean distance based model generates the most detailed prediction and has best estimation results when the two measurement points are close to each other; the Jaccard distance based model can only provide a very coarse estimation, however, it can distinct points that are far away; the Spearman's footrule based solution has overall good but coarse estimation, and its estimations represent the relative distance very well, especially in the middle range. The proposed method, as we expected, combines the best features from all three reference models and generates the best locational distance estimation.