Latest revision as of 11:00, 16 February 2021

Abstract

We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided. Mr. Ivan A. Mantilla-Gaviria has been supported by a FPU scholarship (AP2008-03300) from the Spanish Ministry of Education. Moreover, the authors are grateful to ERA A.S. who supplied the recording of TDOA measurements. Mantilla Gaviria, IA.; Leonardi, M.; Galati, G.; Balbastre Tejedor, JV. (2015). Localization algorithms for multilateration (MLAT) systems in airport surface surveillance. Signal, Image and Video Processing. 9(7):1549-1558. doi:10.1007/s11760-013-0608-1 S 1549 1558 9 7

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/10251/68258 under the license https://creativecommons.org/licenses/by-nc-nd

• https://art.torvergata.it/retrieve/handle/2108/97742/199607/10.1007_s11760-013-0608-1.pdf

• http://link.springer.com/content/pdf/10.1007/s11760-013-0608-1.pdf,

http://link.springer.com/article/10.1007/s11760-013-0608-1/fulltext.html,

http://link.springer.com/content/pdf/10.1007/s11760-013-0608-1,

http://dx.doi.org/10.1007/s11760-013-0608-1 under the license cc-by-nc-nd

• https://link.springer.com/article/10.1007/s11760-013-0608-1,

https://link.springer.com/content/pdf/10.1007%2Fs11760-013-0608-1.pdf,

https://dblp.uni-trier.de/db/journals/sivp/sivp9.html#Mantilla-Gaviria15,

https://riunet.upv.es/handle/10251/68258,

https://rd.springer.com/article/10.1007/s11760-013-0608-1,

https://art.torvergata.it/handle/2108/97742,

https://academic.microsoft.com/#/detail/1969988114 under the license http://www.springer.com/tdm

DOIS: 10.1007/s11760-013-0608-1 10.1155/2011/172902 10.1017/s1759078712000104 10.1109/joe.1987.1145216 10.1049/iet-rsn.2011.0197 10.1016/j.mcm.2012.03.004