Over the past decade, there has been an increasing interest in research regarding device-free localization (DFL) systems. Unlike most types of automatic localization, DFL does not require the entity to wear an active or passive hardware device (tag). Instead, the influence that the physical presence of an entity has on its environment is used to determine its location. Classic examples such as applications involving assisted living or police and military raids in which applying a tag to each entity would be difficult or even outright impossible, clearly illustrate the usefulness of this concept.
One possible approach to DFL consists of relying on the impact the presence of an entity has on the propagation of RF-signals throughout the environments. In these systems, RF-transmitters and receivers are not (solely) used for communication, but act as sensors themselves. The techniques utilizing this approach can be subdivided into different categories. Through-the-wall radar-like ultra-wideband systems are commercially available and are primarily marketed towards police and military related applications. They are expensive, however, and potentially suffer from accuracy loss at larger distances. Other possible RF-based DFL methods consist of installing a network of RF-transceivers in an environment and measuring the impact of an entity on the signal strengths of the transmissions between these transceivers. The use of such a wireless sensor network (WSN) has become well-known in recent years, with techniques like Radio Tomographic Imaging (RTI) and passive fingerprinting.
DFL can be considered to consist of three different aspects: detection, tracking and identification.
Detection is defined as the ability of the system to detect changes in the environment and establish how many entities are responsible. Tracking refers to the tracking of the positions of these entities. A velocity and a sequence of positions of an entity are estimated over a certain duration of time based on a set of location estimations. Finally, identification is defined as determining the identity of the entities. ‘Identity’ can refer to the size or shape of the entity or to an actual identity of a human individual. This aspect is usually regarded as an extremely difficult task for tagless localization in general (’the identification problem’), only feasible to achieve through the use of optical-based techniques. For some applications, this can even be seen as an important advantage, due to the fact that a total lack of identification capabilities means that no privacy- related concerns can arise.
Our DFL-research within IDLab is primarily focused on detection and tracking, although we investigate these two aspects in vastly different contexts. The use of WSN-based tagless detection is studied within the domain of large-scale crowd estimation, while our tracking research is focused on small-scale multi-frequency RTI setups.
Estimating Crowd Sizes
Quite a lot of research has been performed in recent years regarding the use of wireless sensor networks to construct systems which are entirely focused on the detection aspect in environments which contain a large amount of human individuals: crowd density estimation. The density of a crowd can be important information for a multitude of applications. Examples include but are not limited to traffic control, crowd and safety management during events and gauging the interest of consumers for different products at a trade fair. The classic approach to obtaining this information is to make use of an optical camera-based system. The accuracy of these systems can be rather dependent on the lighting conditions and they tend to require the availability of a large amount of computing power. Furthermore, the use of optical cameras raises the specter of privacy- related issues.
A WSN-based system could potentially solve these issues. In the current state-of-the-art, however, the crowds on which the experiments are being performed tend to contain no more than a few tens of people. In order to look into the possibility of using these techniques for real-life applications, larger crowd sizes are needed. It is precisely this aspect that we investigate. We deploy our WSNs (which operate primarily on the sub-GHz 433 MHz and 868 MHz bands) at large-scale events such as popular music festivals. At these types of locations, hundreds or even thousands of people can be present at a single stage. This provides us with large amounts of useful data to aid us in developing a full-fledged RF-based tagles crowd estimation system.
Radio Tomographic Imaging (RTI)
Radio Tomographic Imaging is an RF-based tagless localization technique also makes use of a WSN. It attempts to determine which locations within the environment were most likely responsible for measured RSS-changes of the communication links. The basic algorithm takes as input a list of RSS-differences between the current measurement and an earlier calibration measurement when the environment did not contain any targets and creates an attenuation image. This image is a representation of the environment in which the value of each pixel indicates the average attenuation a communication link will experience when its line-of-sight traverses the corresponding location. Pixels with a high amount of attenuation are assumed to be more likely to contain locatable entities.
Several variations and improvements of the basic RTI-technique do exist. In variance based RTI (VRTI), a windowed variance of the most recent RSS-link measurements is used as input to the algorithm. This eliminates the need for an earlier calibration step and paves the way for the use of RTI in emergency applications. Multi-channel approaches where the nodes communicate on multiple frequency channels have also been introduced in the state-of-the-art, as well as advanced multi-tracking systems which allow for accurate simultaneous tracking of up to 4 individuals.
In the majority of current Radio Tomographic Imaging systems, the nodes communicate using 2.4 GHz signals. Our research is primarily focused on investigating the potential use of multiple sub-GHz frequency bands in a single RTI-system and whether this approach could potentially solve several shortcomings of the technique. The larger wavelengths of sub-GHz signals makes it more difficult for them to be influenced by human presence, thus one would generally expect their use to consistently lead to worse localization accuracies. However, this same aspect also tends to make them less susceptible to multi-path effects caused by a multitude of static objects, which the RTI-algorithm interprets as noise. This can be an important advantage in complex environments. Furthermore, the increased communication range offered by sub-1 GHz frequencies could play an important role regarding the size of the environments in which a system can be installed. Finally, the use of sub-1 GHz frequencies could enable the creation of more energy-efficient RTI-systems using existing low-power communication solutions.
- DOCPRO Stijn Denis [TBC]
- ICON iFEST - improved festival experience through wireless
- FWO SB Device-free crowd sensing at large music festivals using radio frequency signal features (submitted)
- S. Denis, R. Berkvens, and M. Weyn, “A Survey on Detection, Tracking and Identification in Radio Frequency-Based Device-Free Localization,” in Sensors 2019, vol. 19 no. 23, 2019, pp. 5329.
- S. Denis, R. Berkvens, G. Ergeerts, B. Bellekens, and M. Weyn, “Combining multiple sub-1 GHz frequencies in Radio Tomographic Imaging,” in 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016, pp. 1–8.
- S. Denis, R. Berkvens, G. Ergeerts, and M. Weyn, “Multi-frequency sub-1 GHz radio tomographic imaging in a complex indoor environment,” in 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017, pp. 1–8.
- S. Denis, R. Berkvens, B. Bellekens, and M. Weyn, “Large Scale Crowd Density Estimation Using a sub-GHz Wireless Sensor Network,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2018, pp. 849–855.