Javier Schloemann

Toward a Tractable Analysis of Localization Fundamentals in Cellular Networks

When dedicated positioning systems, such as GPS, are unavailable, a mobile device has no choice but to fall back on its cellular network for localization. Due to random variations in the channel conditions to its surrounding base stations (BS), the mobile device is likely to face a mix of both favorable and unfavorable geometries for localization. Analytical studies of localization performance (e.g., using the Cramér-Rao lower bound) usually require that one fix the BS geometry, and favorable geometries have always been the preferred choice in the literature. However, not only are the resulting analytical results constrained to the selected geometry, this practice is likely to lead to overly-optimistic expectations of typical localization performance. Ideally, localization performance should be studied across all possible geometric setups, thereby also removing any selection bias. This, however, is known to be hard and has been carried out only in simulation. In this paper, we develop a new tractable approach where we endow the BS locations with a distribution by modeling them as a Poisson point process (PPP), and use tools from stochastic geometry to obtain easy-to-use expressions for key performance metrics. In particular, we focus on the probability of detecting some minimum number of BSs, which is shown to be closely coupled with a network operators ability to obtain satisfactory localization performance (e.g., meet FCC E911 requirements). This metric is indifferent to the localization technique (e.g., TOA, TDOA, AOA, or hybrids thereof), though different techniques will presumably lead to different BS hearability requirements. In order to mitigate excessive interference due to the presence of dominant interferers in the form of other BSs, we incorporate both BS coordination and frequency reuse in the proposed framework and quantify the resulting performance gains analytically.

Towards indoor localization of pedestrians via smart building vibration sensing

Indoor localization by means of GNSS or a cellular-based method is known to be difficult. Potentially, other wireless technologies could address the technical requirements, but they usually imply the end user must carry a device compatible with this additional technology too. In this paper we investigate the feasibility of collecting vibration sensor readings within a building to locate pedestrians by their footsteps. Vibration propagation in buildings is markedly different than radio wave propagation in free space, thus prompting one to question the suitability of conventional positioning algorithms for this task. We presents the results of experiments conducted with actual measurements from an instrumented, smart building. We expect such buildings to become more prevalent in the future thanks to the technical advances and cost reductions provided by the Internet-of-Things (IoT). The promising initial findings indicate that time-difference-of-arrival, within a limited spatial extent, could be a viable localization technique, and these results encourage further research into vibration-based indoor localization.

Vibration Event Localization in an Instrumented Building

In this paper, we present the preliminary results of an indoor location estimation campaign using real data collected from vibration sensors mounted throughout an instrumented smart building. The Virginia Tech Smart Infrastructure Laboratory house a unique testbed featuring a fully instrumented operational building with over 240 accelerometers permanently mounted to the steel structure. It is expected that in the future, more and more buildings will be constructed with sensors scattered about their infrastructures, in no small part due to the envisioned promises of such systems which include improved energy efficiency, health and safety monitoring, stronger security, improved construction practices, and improved earthquake resistance. One of the most promising uses of this smart infrastructure is for indoor localization, a scenario in which traditional radio-frequency based techniques often suffer. The detection and localization of indoor seismic events has many potential applications, including that of aiding in meeting indoor positioning requirements recently proposed by the FCC and expected to become law in the near future. The promising initial results of a simplistic time-difference-of-arrival based localization system presented in this paper motivate further study into the use of vibration data for indoor localization.

A Tractable Metric for Evaluating Base Station Geometries in Cellular Network Localization

In this letter, we present a new metric for characterizing the geometric conditions encountered in cellular positioning based on the angular spread of the base stations (BSs). The metric is shown to be closely related to the geometric-dilution-of-precision (GDOP), yet has the benefit of being characterizable in terms of the network parameters for BS layouts modeled according to a Poisson point process (PPP). As an additional benefit, the metric is shown to immediately yield a devices probability of being inside or outside the convex hull of the BSs, which localization researchers will widely-recognize as being a strong indicator of localization performance.

Localization performance in cellular networks

When the Global Positioning System is unavailable, cellular networks become the dominant vehicle for positioning. However, no tractable approach exists for gaining general insights into localization performance in such networks. Instead, analysis is often done using deterministic network models or with complex system-level simulations, resulting in highly context-specific insights, which do not translate well to random network topologies. In this paper, we motivate and introduce a new approach for analyzing localization performance in cellular networks using tools from point process theory and stochastic geometry. After presenting the model, easy-to-use expressions are derived for the distributions of base station hearability, a metric which is closely-related to localization performance, with and without base station coordination.

On the value of collaboration in multidimensional location estimation

In this paper, we investigate the benefit of inter-node collaboration in multidimensional location estimation. In particular, for networks with reference nodes at known locations and source nodes whose locations are unknown and to be estimated, we establish the value of collaboration for source node position estimation by presenting proof of a decreasing Cramér-Rao lower bound as additional source nodes (meeting some minimum connectivity requirements) are introduced into the collaborative position estimation problem. Prior work has shown this for one-dimensional location estimation; however, the previous proof as presented is not easily extendable to multidimensional location estimation. Following the completion of the proof, the minimum connectivity conditions for two-dimensional positioning using time-of-arrival and received-signal-strength ranging information are discussed. Lastly, the theoretical result is verified with numerical results through simulation.

Effect of Collaboration on Localizability in Range-Based Localization Systems

In this paper, we examine, via analysis, the improvement in device localizability through collaboration. Depending on the sensitivity of the receivers in the devices, it is not unusual for an unlocalized device to lack a sufficient number of detectable positioning signals from localized devices to determine its location without ambiguity (i.e., to be uniquely localizable). This occurrence is well-known to be a limiting factor in localization performance, especially in communications systems. In cellular positioning, for example, cellular network designers call this the hearability problem. In this work, we employ tools from stochastic geometry to derive accurate approximations for the probabilities of unique localizability in the noncollaborative and collaborative cases. We consider range-based positioning scenarios with and without shadowing. The results are very promising and motivate further research into enhancing cellular positioning with small-scale collaboration (e.g., using D2D in LTE).

Improved Indoor Positioning Using the Baum-Welch Algorithm

In this paper, we examine the exploitation of individual patterns of behavior to enhance indoor positioning of pedestrians.We make use of the fact that, due to habits and needs, a person is likely to be in some locations more often than others. For example, at their work or in their home, a person is likely to spend more time in some rooms than in others. Therefore, it seems natural to take advantage of established behavior patterns when performing indoor localization in an effort to improve accuracy. Such improvement is particularly beneficial during emergencies, where location inaccuracies may lead to life-threatening delays in response times. In this work, habitual behavior is modeled and learned using a hidden Markov model. It is shown that, applying the Markov model for location estimation results in more accurate estimates when compared to using a standard particle filter with odometry information. Additionally, transition probabilities as well as position error distributions do not need to be known a priori since they can be learned using the Baum-Welch algorithm. Results show how the Baum-Welch algorithm can even learn the distributions of biased estimates. On the other hand, it is shown how user feedback can help accelerate the learning process, while guaranteeing good parameter estimation accuracies.

Using Fisher Information Matrix Summary Statistics to Assess the Value of Collaborative Positioning Opportunities

In this paper, we consider the value of node collaboration for positioning. In particular, we look at the question of whether or not it is possible to assess the value of a collaborative opportunity using a low-complexity calculation. Using concepts from the optimal design of experiments, we consider the usage of Fisher information matrix summary statistics to provide these low-complexity calculations. These statistics are then evaluated for their abilities to help predict the post-collaboration availability and performance of a positioning solution, as well as their ability to aid in collaborator selection. The analysis is performed for both line-of-sight and non-line-of-sight environments. We show that the summary statistics are useful in assessing the value of collaborative positioning opportunities and discuss their strengths, weaknesses, and computational complexities.

On the value of collaboration in anchorless robot self-localization

In this paper, we consider the value of collaboration in anchorless robot self-localization. Robots which rely solely on data from imperfect inertial measurement units become increasingly less certain of their locations over time. Without anchors or landmarks to help them regain their bearings, these robots reach a point at which they essentially have no idea of their locations. If, however, these robots are given the ability to communicate with one another, they can utilize additional information gained through collaboration to correct their beliefs and collectively improve their location estimates. In this work, we characterize how inter-robot collaboration impacts self-localization performance and discuss whether any additional information, such as an estimator confidence measure, may be gleaned from the resulting corrected beliefs.