Name
Abstract | ||
|---|---|---|
Location-awareness enables a variety of emerging applications on mobile devices. For indoor applications, a desirable way of obtaining real-time locations is by combining different sources of positional information, such as the inertial measurements, ranging measurements, and map information with an infrastructure-free system that does not rely on any customized hardware. These sources of information can be incorporated into the paradigm of network localization and navigation (NLN). However, there still lacks an infrastructure-free localization system that applies the insights of NLN to effectively fuse different types of information. In this paper, we present the Mercury system, which realizes the key ideas of NLN, including the exploitation of spatiotemporal cooperation and the use of environmental knowledge. We design a real-time belief propagation algorithm to fuse inertial measurements as well as range measurements among different users with map information. We implement this algorithm in the Mercury system formed by a network of smartphones, and evaluate its localization accuracy through experimentation. Results show that Mercury provides reliable location information and that combining spatiotemporal cooperation with environmental knowledge remarkably reduces the location uncertainty of users. Moreover, the performance of Mercury is more robust to imperfect initial positional knowledge compared with that of existing systems. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMC.2017.2725265 | IEEE Trans. Mob. Comput. |
Keywords | Field | DocType |
Velocity measurement,Navigation,Position measurement,Mobile computing,Smart phones,Real-time systems,Fuses | Mobile computing,Inertial frame of reference,Mercury (element),Computer science,Free system,Ranging,Mobile device,Fuse (electrical),Belief propagation,Distributed computing | Journal |
Volume | Issue | ISSN |
17 | 5 | 1536-1233 |
Citations | PageRank | References |
6 | 0.47 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Liu | 1 | 23 | 4.52 |
| Wenhan Dai | 2 | 222 | 17.69 |
| Moe Z. Win | 3 | 48 | 7.69 |