## 2 Related Work  
Most positioning approaches commonly rely on communication between a target resource (_i.e._, a mobile device) and a series of reference points with known positions. There are several proposals to perform positioning on mobile, heterogeneous outdoor environments. These works aim to estimate the position of the targets using individual or collaborative approaches, and they are usually focused on finding novel alternatives to GPS and improving the accuracy of the measurements. Although some of these works are able to reduce the error of a positioning estimation to the range of centimeters, they typically require an existing communication infrastructure and/or a source of energy to function properly, both of which are usually not available in disaster areas. [PERSON] _et al_. [13] propose a personal indoor/outdoor Wi-Fi Positioning System using received signal strength (RSS) from dense Wi-Fi access points (AP) dedicated to localization, oriented towards Android-based smartphones. Their method measures the RSS from each AP three times, calculating the mean value and comparing it to each training value. If the difference between the mean value and the training values is below a given threshold, the training value is withdrawn and the mean of filtered training values is calculated again. Finally, the mean value is compared with the database value and a proper location on the map is found with a proper scan time and threshold, thereby yielding a low error rate. Although this proposal is interesting and widely used, it requires a mix of both _ad hoc_ elements and a pre-existent infrastructure that is not necessarily available in a disaster scenario. Global Navigation Satellite Systems (GNNS) incorporate several satellite-based positioning systems, including GPS, GLONASS (GLObal NAVigation Satellite System), Galileo, and Compass [14]. In GNSS,the measurements obtained from a group include extra measurements, specifically the relative distances between the neighboring users. As a result, there are several factors that impact on the positioning accuracy, and cause problems for the performance analysis. [PERSON] _et al_. [15] studied the multiplicative effect of various factors on the accuracy of GNSS collaborative positioning algorithms, using their proposed Collaborative Dilution of Precision model. Using such a model, they also analyze the performance of GNSS standalone positioning. Their results show that GNSS collaborative positioning accuracy is always higher than or equal to that of GNSS standalone positioning. [PERSON] _et al_. [16] present an approach in which a database (DB) is trained using navigational data from users, utilizing different strategies to survey sensor-based navigation errors in indoor positioning. They combine different navigation trajectories moving in and out of a target building to create the DB, restricting the time length of available indoor navigation trajectories, _i.e._, the trajectories from the last received GNSS signal before entering an indoor area, until the next GNSS signal is received after walking out of the target building. Their results showed that the errors in pedestrian navigation were reduced from 6.0 to 5.7 m during 5-10 min of indoor walking. This is an interesting study that combines outdoor and indoor positioning, providing a smooth transition between these two types of scenarios; however, it is focused on providing indoor positioning, and outdoor signals are used only to establish entry and exit points throughout the user's indoor route. [PERSON] _et al_. [17] state that collaboration is highly essential for nodes to perform positioning efficiently in outdoor and indoor environments. They use beacon and anchor nodes with known positions to estimate the position of a target node in a distributive manner, employing localization algorithms that consider fading and shadowing effects.