Pattern Classification for Geotag Generation

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. Loran 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 locationdependent parameters. By quantity, we mean the total number of different (independent) location-dependent measurements available. By quality, we mean the amount of 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 error due to temporal variations in location-dependent parameters significantly degrades system reliability. In this paper we propose a new method to generate strong geotags from noisy location data. Pattern classification is the concept of assigning a physical object or measured data to one of the pre-specified groups, using a priori knowledge or statistical information. Three different classifier-based geotag generation algorithms are proposed, and real data are used to evaluate and compare their performance.