Name
Abstract | ||
|---|---|---|
Despite popularity of Fingerprinting Localization Algorithms (FPS), general theoretical frameworks for their performance studies have rarely been discussed in the literature. In this work, after setting up an abstract model for the FPS, it is shown that fingerprinting-based localization problem can be cast as a Hypothesis Testing (HT) problem and therefore various results in the HT literature can be used to provide insights for the general FPS. This includes scaling limits of localization reliability in terms of measurement numbers and the precise characterization of a geometric error. The main quantity that encapsulates this information is shown to be the Kullback-Leibler (KL) divergence between probability distributions of a selected feature for fingerprinting at different locations. The KL divergence can be used as a central performance metric, indicating how well a localization algorithm can distinguish two points. The framework is instantiated for Received Signal Strength (RSS)-based algorithms, where the effect of various parameters on the performance of fingerprinting algorithms is discussed, including path loss and fading characteristics, number of measurements, number of anchors and their locations and placement of training points. Simulations and experimental results characterize numerically the findings of the theoretical framework and demonstrate its consistency with realistic localization scenarios. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Information Theory | Fading,Performance metric,Algorithm,Probability distribution,Path loss,RSS,Scaling,Statistical hypothesis testing,Kullback–Leibler divergence,Mathematics |
DocType | Volume | Citations |
Journal | abs/1610.07636 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arash Behboodi | 1 | 65 | 13.77 |
| filip lemie | 2 | 81 | 11.76 |
| Adam Wolisz | 3 | 2693 | 407.71 |