| 1. |
- Yin, Feng, et al.
(author)
-
Fundamental Bounds on Position Estimation Using Proximity Reports
- 2016
-
Conference paper (peer-reviewed)abstract
- There is a big trend nowadays toward indoor proximity report based positioning. A binary valued proximity report can be obtained opportunistically through event-triggering, leading to significantly reduced signaling overhead for wireless communications. In this paper, we aim to derive two types of fundamental lower bound, namely the Cram´er-Rao bound and the Barankin bound, on the mean-square-error of any proximity report based position estimator. Using the maximum-likelihood estimator as a representative example, we show that the Barankin bound is potentially much tighter than the Cram´er-Rao bound and conclude that the Barankin bound ought be better suited for benchmarking any proximity report based position estimator.
|
|
| 2. |
- Zhao, Yuxin, 1986-, et al.
(author)
-
Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
- 2016
-
In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). - : IEEE. - 9781509016983 ; , s. 1-5
-
Conference paper (peer-reviewed)abstract
- The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.
|
|
| 3. |
- Zhao, Yuxin, 1986-, et al.
(author)
-
Gaussian Processes for Flow Modeling and Prediction of Positioned Trajectories Evaluated with Sports Data
- 2016
-
In: 19th International Conference on Information Fusion (FUSION), 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9780996452748 - 9781509020126 ; , s. 1461-1468
-
Conference paper (peer-reviewed)abstract
- Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernelbased methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to other use cases with device trajectories of positioned data. Some specific aspects can be addressed when the data is periodic, like in sports where the event is split up over multiple laps along a specific track. Flow models of both the individual skier and a cluster of skiers are derived and analyzed. Performance has been evaluated using data from the Falun Nordic World Ski Championships 2015, in particular the Men’s cross country 4 × 10 km relay. The results show that the flow models vary spatially for different skiers and clusters. We further demonstrate that GP regression provides powerful and accurate models for flow prediction.
|
|
