Radio fingerprinting is a well-established concept for modeling the propagation of Wi-Fi, Bluetooth or other radio signals in a known environment for localization purposes. When using position estimates from such a model for trajectory tracking over time in a Bayesian framework such as a Particle Filter, it must be possible to evaluate arbitrary state hypotheses based on the fingerprinting model. Instead of having a single position estimate for current observations, the model target should be a continuous probability density function from which any hypothesis can be evaluated. This work proposes an interpolation approach for the position estimates of fingerprinting models. The interpolation is based on Gaussian Process Regression. The proposed approach is particularly capable of handling multimodalities and maintains high accuracy even in large environments with little calibration data. It is also efficient in terms of runtime, making it a suitable choice for real-time applications.

Smartphone-based indoor positioning and navigation remains a challenging task, as specialized technologies such as ultra-wideband (UWB) or Wi-Fi fine-time measurement are still niche and supported by only a few flagship smartphones. Therefore, standard technologies based on RSSI measurements, mainly Bluetooth Low Energy (BLE) and Wi-Fi, are used to obtain absolute positioning information of pedestrians inside buildings. Sensor fusion methods combine this with relative information from modeling human movement using sensor data provided by the smartphone’s IMU. It is also common practice to restrict this movement to the actual accessible areas of the building (e.g. restricting moving through walls), using spatial models based on the building’s floor plan. Without further assumptions, this complexity inevitably leads to a non-linear and non-Gaussian state space model. A common tool for (position) estimation in such scenarios is the broad class of particle filters. However, the use of such spatial constraints accelerates the well-known problem of sample impoverishment, which in the worst case can lead to the particle filter completely losing track, getting stuck and never recovering. This work begins with a brief presentation of an award-winning Indoor Positioning System (IPS) derived from previous work. Based on this, we present several approaches using Particle Distribution Optimization (PDO) that attempt to solve the impoverishment problem and ultimately lead to better overall positioning results. In the experiments, we compare them in two different buildings under realistic conditions and discuss the results in detail.

Acquiring accurate ground truth or fingerprint data for indoor localization typically demands specialized hardware and considerable effort. This paper introduces three post-processing methods to simplify the recording of such data within certain limitations. The methods can be categorized into fixed-interval smoothing where the complete recording data is available for the optimal state estimation and there is no live processing requirement. Our methods are based on a particle filter system, employing the CONDENSATION algorithm to estimate the hidden state. Particles represent positions and headings in 3D space. They are moved and evaluated using pedestrian dead reckoning based on IMU measurements and a map of the building only. The development and application of these methods is presented in this paper, demonstrating their effectiveness in reducing the effort required for accurate ground truth and fingerprint data recording.

This work presents SIMUL, a new dataset consisting of 550 minutes of annotated motion data from six synchronized IMUs placed consistently at the same strategically chosen positions on the bodies of 32 participants. With a focus on indoor localization, the selection fell on hand, feet, and trouser pockets. Due to the sensor’s synchronization, this selection allows, for example, to label the data recorded in the hand based on the data captured at the feet. For a better generalizability, the dataset was recorded freestyle under many different environmental conditions and walking speeds. Thus, indoors, numerous floor coverings such as stone, wood, carpet and PVC, as well as stairs are included in the data. The outdoor recordings additionally contain uneven surfaces such as paving stones and slopes. The annotation of the participant’s currently performed activity additionally allows this dataset to be used for activity recognition.

Radio frequency ranging protocols enable a device to estimate the distance to another device, based on signal propagation time. In theory, ranging protocols are promising for indoor localization as one can obtain the position of the pedestrian directly from the ranging results if the access point positions are known. However, in practice, indoor scenarios still pose a challenging problem as the observed distances vary greatly due to non-line-of-sight signal paths, delayed signal propagation, and general hardware inaccuracies. The IEEE 802.11-2016 (formerly IEEE 802.11mc) standard defines a RF ranging protocol for Wi-Fi, namely Fine Timing Measurement (FTM). In order to improve the position estimate, a novel sensor model for FTM is derived from observed data. It is shown that the FTM error varies with the actual distance to the access point. Within this work, different parameter sets are estimated from the observed data for skew normal distributions, depending on the actual distance. For these parameters, low-order polynomials are then fitted to obtain the distribution parameters as functions of the actual distance. Furthermore, a particle filter is described and evaluated in an industrial scenario using cheap Espressif ESP32-S2 IoT FTM access points in the 2.4 GHz band. The filter combines map information, Pedestrian Dead Reckoning, and our novel FTM sensor model to estimate the pedestrian's position in the building. Finally, the localization result of the particle filter is compared to another promising radio frequency ranging method: ultra-wideband.

Particle filters are a commonly used technique for sensor fusion in indoor localization use-cases. Multiple strategies exist, that control when the particle filter is updated and with which data. We take a look at three commonly used update strategies. The first runs a full particle filter update for every incoming measurement, the second uses a fixed-interval update rate and the third is triggered by events, such as detected steps. All of these strategies have different advantages and disadvantages. The first strategy, for example, has the problem that steps are recognized only after they have been completed - which makes for a constant temporal discrepancy between the proposal distribution and the measurements evaluated on top. Due to the configured delay, the fixed-interval strategy, in comparison, can pre-date incoming step events to mitigate this discrepancy. In this paper we present PIPF as a novel approach to combine advantages of the fixed-interval update strategy with advantages of the strategy that runs a full update per measurement. This works by using the transition to calculate a trajectory, which is then followed during the evaluation. The proposal distribution in the form of particles is interpolated on this trajectory for every incoming measurement, which removes the temporal discrepancy between both. To evaluate PIPF's performance and characteristics at different configurations, we compare it to a conventional fixed-interval particle filter on a real-world indoor positioning scenario.

It is common practice to use a sample-based representation to solve problems having a probabilistic interpretation. In many real world scenarios one is then interested in finding a best estimate of the underlying problem, e.g. the position of a robot. This is often done by means of simple parametric point estimators, providing the sample statistics. However, in complex scenarios this frequently results in a poor representation, due to multimodal densities and limited sample sizes. Recovering the probability density function using a kernel density estimation yields a promising approach to solve the state estimation problem i. e. finding the “real” most probable state, but comes with high computational costs. Especially in time critical and time sequential scenarios, this turns out to be impractical. Therefore, this work uses techniques from digital signal processing in the context of estimation theory, to allow rapid computations of kernel density estimates. The gains in computational efficiency are realized by substituting the Gaussian filter with an approximate filter based on the box filter. Our approach outperforms other state of the art solutions, due to a fully linear complexity and a negligible overhead, even for small sample sets. Finally, our findings are evaluated and tested within a real world sensor fusion system.