# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 1 Introduction
In 2012, [PERSON] and [PERSON] created the Nuzzer system [12] which was capable of locating a single human target with a median error of 1.82 m in an office building floor which had a total area of approximately 1500 m\({}^{2}\). Only three 802.11b access points and two consumer-grade laptops were used as stationary nodes, leading to a total of six one-way communication links. The fingerprint matching algorithm primarily consisted of a Naive Bayesian classifier which made use of the RSS-values reported by the network interface cards (NICs). Much more complex systems have been developed in the years since, such as SCPL (sequential counting, parallel localizing) [13] and ACE [14] which allowed for robust multitracking without requiring manually constructed fingerprints for each possible combination of target locations. In [15], [PERSON] et al. investigated the longevity of passive fingerprint databases in relation to changes within the environment. While they were not the first to investigate this aspect [16], their research showed the magnitude of this issue. Through the combination of a random forest classification algorithm with RSS measurements which were performed on multiple frequency channels within the 2.4 GHz band, they managed to mitigate this problem. In [17], [PERSON] et al. improved upon this research by improving the channel selection algorithm and using a logistic regression classifier instead of random forests. Despite these advances, there are still many unanswered questions within this field of research. The previously mentioned requirement to regularly update the fingerprint database when the presence and/or location of static objects changes still remains a major disadvantage of passive fingerprinting techniques. Additionally, there have only been very limited investigations into the potential impact the body type of the human target can have on the localization accuracy. In this paper, we describe the passive fingerprinting experiments we performed in a 42 m\({}^{2}\) environment which contained fifteen 433 MHz and 868 MHz nodes and analyze the results. We defined fifteen locations within this environment and had multiple participants of differing height and weight stand at each of these locations while RSS-measurements were performed. Two sets of measurements were performed. In the first set, the measurement duration for one individual standing at a specific location lasted for thirty seconds. During this period, the person was instructed to move in a stationary manner. Each measurement for the second set lasted for only ten seconds and the person was instructed to stand still alongside a single orientation. The first set was used to create a fingerprint database and the second was used for evaluation. In our analyses of the resulting data, we investigated the following research questions:  
* What impact does the number of nodes have on the localization accuracy? Based on existing literature, we expect the maximum number of nodes in our experiment (15) to be far beyond what is required to be sufficiently accurate. * Are there any benefits to the use of sub-GHz frequencies? While 2.4 GHz is very common within DFL in general, sub-GHz frequencies have been successfully used within the field of passive fingerprinting. Is it useful to combine multiple frequencies in a single system? * What is the impact of the body type of a human individual who is present in the environment on the localization accuracy? What if the individual whose presence was used to construct the fingerprint database differs significantly from the human present in the environment while the system is active? * How feasible is it to differentiate between multiple individuals with different body types based on the impact on RSS measurements between the stationary nodes? Can a rudimentary form of identification be implemented? In regards to this final research question, it is only in recent years that any progress has been made towards solving the identification problem within (noncamera based) DFL.