Abstract
Location fingerprinting systems can be differentiated for the most part by the following two characteristics: (1) the feature selected to fingerprint the sites; and (2) the mapping algorithm to determine the mobile’s location. In this chapter, we introduce several fingerprinting techniques. Given its prevalence, we concentrate on the RSS feature in the first part of the chapter. The same techniques, however, apply to other features as well. In the first section, an analytical model of a generic fingerprinting system is presented. The model describes how the salient parameters common to most systems affect their performance. The subsequent section showcases a number of methods to compute the similarity metric for memoryless systems—that is—systems which estimate location based on readings taken at a single time instant. Section 4.3 introduces systems with memory and shows how maintaining some historic path data can enhance location precision significantly. In the remainder of the chapter, we introduce some non-RSS features. Section 4.4 investigates the use of the channel impulse response as an alternative radio frequency signature. Conversely, Sect. 4.5 reports on non-RF features altogether—features which are available from devices such as smartphones, namely sound, motion, and color.
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Notes
- 1.
Their application to statistical classification is similar.
- 2.
On a similar note, in (Roos), (Kuschki), (Fox), (Madigan) mathematical expressions which are close to the Nadaraya-Watson Kernel regression are developed.
- 3.
Details of the VNA are described in Chap. 2.
- 4.
This requires normalization of the individual similarity metrics such that each of their minimum and maximum values falls between 0 and 1, respectively.
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Gentile, C., Alsindi, N., Raulefs, R., Teolis, C. (2013). Survey-Based Location Systems. In: Geolocation Techniques. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1836-8_4
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