Robust Geotag Generation Algorithms from Noisy Loran Data for Security Applications

摘要

Geo-security, or location-based security service, provides authorization of persons or facilities based on their distinctive location information. It applies the field of position navigation and time (PNT) to the provision of security. Location-dependent parameters from radio navigation signals are quantized to compute a location verification tag or "geotag" to block or allow accesses by users. Adequate quantization steps of location-dependent parameters should be selected to achieve reliable performance.rnLoran is chosen as a case study because of its beneficial properties for location-based security services. The achievable performance and security of the system are determined by the quantity and quality of location-dependent parameters. By quantity, we mean the total number of different (independent) location-dependent measurements available. By quality, we mean the amountrnof unique location-dependent information and its consistency provided by each parameter that can be used to generate a robust geotag. It is desirable that the parameters be relatively insensitive to temporal changes that can weaken the uniqueness of the information. As a result, reproducibility and repeatable accuracy are fundamental requirements for any location-based security service. In practice, quantization temporal variations in location-dependent parameters significantly degrade system reliability.rnIn this paper we introduce two new methods to generate strong geotags from noisy location data: fuzzy extractor-based and classifier-based. The performance of the different geotag generation algorithms are analyzed and compared; real data are applied to evaluate Loran getoag reliability and spatial discrimination.