Hostname: page-component-cc8bf7c57-llmch Total loading time: 0 Render date: 2024-12-11T12:57:13.639Z Has data issue: false hasContentIssue false

An effective procedure to design the layout of standard and enhanced mode-S multilateration systems for airport surveillance

Published online by Cambridge University Press:  03 April 2012

Ivan A. Mantilla-Gaviria*
Affiliation:
ITACA Research Institute, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain.
Mauro Leonardi
Affiliation:
DISP, “Tor Vergata” University, via del Politecnico 1, 00131 Rome, Italy.
Gaspare Galati
Affiliation:
DISP, “Tor Vergata” University, via del Politecnico 1, 00131 Rome, Italy.
Juan V. Balbastre-Tejedor
Affiliation:
ITACA Research Institute, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain.
Elías de los Reyes Davó
Affiliation:
ITACA Research Institute, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain.
*
Corresponding author: I.A. Mantilla-Gaviria Email: imantilla@itaca.upv.es

Abstract

In this paper, an effective procedure to emplace standard and enhanced mode-S multilateration stations for airport surveillance is studied and developed. This procedure is based on meta-heuristic optimization techniques, such as genetic algorithm (GA), and is intended to obtain useful parameters for an optimal system configuration that provides acceptable performance levels. Furthermore, the procedure developed here is able to evaluate and improve previous system designs, as well as possible system enhancements. Additionally, the design strategies to be used along with the procedure proposed here are fully described. Parameters such as the number of stations, the system geometry, the kind of measurements to be used, and the system accuracy are obtained taking into account the basic requirements such as the Line of Sight, the probability of detection, and the accuracy levels.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]The European Organisation for the Safety of Air Navigation, Ed-117, MOPS for mode S-multilateration systems for use in advanced surface movement guidance and control systems (A-SMGCS), in: EUROCAE (Ed.), EUROCAE, November 2003.Google Scholar
[2]Galati, G.; Leonardi, M.; Tosti, M.: Multilateration (local and wide area) as a distributed sensor system: lower bounds of accuracy, in European Radar Conf., EuRAD, Amsterdam, 30–31 October 2008, 196199.Google Scholar
[3]Lee, H.B.: Accuracy limitations of hyperbolic multilateration system. IEEE Trans. Aerosp. Electron. Syst., 11 (1975), 1629.CrossRefGoogle Scholar
[4]Levanon, N.: Lowest gdop in 2-d scenarios. IEE Proc. Radar, Sonar Navig., 147 (2000), 149155.CrossRefGoogle Scholar
[5]Torrieri, D.J.: Statistical theory of passive location systems. IEEE Trans. Aerosp. Electron. Syst., 20 (1984), 183198.CrossRefGoogle Scholar
[6]Mantilla-G, I.A.; Ruiz, R.F.; Balbastre-T, J.V.; Reyes, E.D.L.: Application of metaheuristic optimization techniques to multilateration system deployment, in Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, ESAVS 2010, German Institute of Navigation (DGON), Berlin, Germany, 1617 March 2010, Session 2B/3.Google Scholar
[7]Perl, E.; Gerry, M.J.: Target localization using TDOA distributed antenna, US 2005/0035897 A1, USA, February 17 2005.Google Scholar
[8]Reck, C.; Berold, U.; Schmidt, L.P.: High precision DOA estimation of SSR transponder signals, in IEEE Int. Conf. on Wireless Information Technology and Systems, Honolulu, USA, 29 August–3 September 2010, 14.CrossRefGoogle Scholar
[9]Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, Upper Saddle River, New Jersey, 2001.Google Scholar
[10]Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Boston, 1989.Google Scholar
[11]Sivanandam, S.N.; Deepa, S.N.: Introduction to Genetic Algorithms, Springer, Berlin, 2007.Google Scholar
[12]Clerc, M.: Particle Swarm Optimization, ISTE, London, 2006.Google Scholar
[13]Dorigo, M.; Stützle, T.: Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.Google Scholar