Diseño de filtros corrugados mediante la técnica de enjambre de partículas

En este artículo se presenta la experiencia en la aplicación de la técnica de optimización de enjambre de partículas o PSO (Particle Swarm Optimization) al diseño de filtros corrugados en guía de onda. Los resultados obtenidos indican que esta técnica permite realizar diseños que se adaptan a una máscara de filtrado predeterminada con una alta convergencia hacia la solución óptima

Efficient location strategy for airport surveillance using mode-s multilateration systems

© Cambridge University Press and the European Microwave Association, 2012[EN] In this paper, the use of regularization methods to solve the location problem in multilateration systems, using Mode-S signals, is studied, evaluated, and developed. The Tikhonov method has been implemented as a first application to solve the classical system of hyperbolic equations in multilateration systems. Some simulations are obtained and the results are compared with those obtained by the well-established Taylor linearization and with the Cramér-Rao lower bound analysis. Significant improvements, for the accuracy, convergence, and the probability of location, are found for the application of the Tikhonov method. © Cambridge University Press and the European Microwave Association, 2012.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 Thales Italia S. p. A. (Dr. Ing. R. Scaroni) who supplied the geometry of the Multilateration system in Linate (Milan, Italy) airport.Mantilla Gaviria, IA.; Leonardi, M.; Galati, G.; Balbastre Tejedor, JV.; Reyes Davó, EDL. (2012). Efficient location strategy for airport surveillance using mode-s multilateration systems. International Journal of Microwave and Wireless Technologies. 1-8. https://doi.org/10.1017/S1759078712000104S18Bertero, M., Boccacci, P., Brakenhoff, G. J., Malfanti, F., & Voort, H. T. M. (1990). Three-dimensional image restoration and super-resolution in fluorescence confocal microscopy. Journal of Microscopy, 157(1), 3-20. doi:10.1111/j.1365-2818.1990.tb02942.xSchau, H., & Robinson, A. (1987). Passive source localization employing intersecting spherical surfaces from time-of-arrival differences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(8), 1223-1225. doi:10.1109/tassp.1987.1165266Gfrerer, H. (1987). An a posteriori parameter choice for ordinary and iterated Tikhonov regularization of ill-posed problems leading to optimal convergence rates. Mathematics of Computation, 49(180), 507. doi:10.1090/s0025-5718-1987-0906185-4[6] 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.[1]The European Organisation for the Safety of Air Navigation. The ATM surveillance strategy for ECAC, in European Air Traffic Management Programme, Eurocontrol, 2008.Torrieri, D. (1984). Statistical Theory of Passive Location Systems. IEEE Transactions on Aerospace and Electronic Systems, AES-20(2), 183-198. doi:10.1109/taes.1984.310439[12] Perl E. ; Gerry M.J. : Target localization using TDOA distributed antenna, US 2005/0035897 A1, USA, 17 February 2005.[2]The European Organisation for Civil Aviation Equipment. 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.Leonardi, M., Mathias, A., & Galati, G. (2009). Two efficient localization algorithms for multilateration. International Journal of Microwave and Wireless Technologies, 1(3), 223-229. doi:10.1017/s1759078709000245Ho, K. C., & Chan, Y. T. (1993). Solution and performance analysis of geolocation by TDOA. IEEE Transactions on Aerospace and Electronic Systems, 29(4), 1311-1322. doi:10.1109/7.259534FOY, W. (1976). Position-Location Solutions by Taylor-Series Estimation. IEEE Transactions on Aerospace and Electronic Systems, AES-12(2), 187-194. doi:10.1109/taes.1976.308294Phillips, D. L. (1962). A Technique for the Numerical Solution of Certain Integral Equations of the First Kind. Journal of the ACM, 9(1), 84-97. doi:10.1145/321105.321114Hanke, M., & Raus, T. (1996). A General Heuristic for Choosing the Regularization Parameter in Ill-Posed Problems. SIAM Journal on Scientific Computing, 17(4), 956-972. doi:10.1137/0917062Golub, G. H., Heath, M., & Wahba, G. (1979). Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter. Technometrics, 21(2), 215-223. doi:10.1080/00401706.1979.10489751Harrington, R. F. (1993). Field Computation by Moment Methods. doi:10.1109/978047054463

An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

A practical and useful application of the Ant Colony Optimization (ACO) method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA). The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

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. https://doi.org/10.1007/s11760-013-0608-1S1549155897EUROCAE-WG-41: ED-117, MOPS for Mode S multilateration systems for use in advanced surface movement guidance and control systems (A-SMGCS). In: The European Organisation for Civil Aviation Equipment (EUROCAE) (November 2003)EUROCAE-WG-70: ED-142, technical specification for wide area multilateration (WAM) systems. In: The European Organisation for Civil Aviation Equipment (EUROCAE) (September 2010)Galati, G., Genderen, P.V.: Proceedings Book of the Tyrrhenian International Workshop on Digital Communications, Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV’11), Capri, Italy (2011)Galati, G., Genderen, P.V.: Proceedings Book of the Tyrrhenian International Workshop on Digital Communications, Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV’08), Capri, Italy (2008)Galati, G., Zellweger, A.: Proceedings Book of the ATM 2002 Advanced Workshop. Capri, Italy (2002)Hahn, W.R., Tretter, S.A.: Optimum processing for delay-vector estimation in passive signal arrays. IEEE Trans. Inf. Theory IT–19(5), 608–614 (1973)Torrieri, D.J.: Statistical theory of passive location systems. IEEE Trans. Aerosp. Electron. Syst. AES–20, 183–198 (1984)Levanon, N.: Lowest GDOP in 2-D scenarios. IEE Proc. Radar Sonar Navig. 147(3), 149–155 (2000)Galati, G., Leonardi, M., Mantilla-Gaviria, I.A., Tosti, M.: Lower bounds of accuracy for enhanced mode-s distributed sensor networks. IET Radar Sonar Navig. 6(3), 190–201 (2012). doi: 10.1049/iet-rsn.2011.0197Smith, J.O., Abel, J.S.: Closed-form least-squares source location estimation from range-difference measurements. IEEE Trans. Acoust. Speech Signal Process. ASSP–35(12), 1661–1669 (1987)Mantilla-Gaviria, I.A., Leonardi, M., Galati, G., Balbastre-Tejedor, J.V., Reyes, E.D.L.: Efficient location strategy for airport surveillance using Mode-S multilateration systems. Int. J. Microw. Wireless Technol. 4(2), 209–216 (2012). doi: 10.1017/S1759078712000104Romero, L., Mason, J.: Evaluation of direct and iterative methods for overdetermined systems of TOA geolocation equations. IEEE Trans. Aerosp. Electron. Syst. 47(2), 1213–1229 (2011)Yang, K., An, J., Bu, X., Sun, G.: Constrained total least-squares location algorithm using time-difference-of-arrival measurements. IEEE Trans. Veh. Technol. 59(3), 1558–1562 (2010)Weng, Y., Xiao, W., Xie, L.: Total least squares method for robust source localization in sensor networks using TDOA measurements. Int. J. Distrib. Sens. Netw. 2011 (Article ID 172902) (2011). doi: 10.1155/2011/172902Mantilla-Gaviria, I.A., Leonardi, M., Galati, G., Balbastre-T, J.V., Reyes, E.D.L.: Improvement of multilateration (MLAT) accuracy and convergence for airport surveillance. In: Tyrrhenian International Workshop on Digital Communications—Enhanced Surveillance of Aircraft and Vehicles (ESAV’11), Capri, Italy (September 12–14, 2011)Mantilla-Gaviria, I.A., Leonardi, M., Balbastre-Tejedor, J.V., Reyes, Edl: On the application of singular value decomposition and Tikhonov regularization to ill-posed problems in hyperbolic passive location. Math. Comput. Model. 57(7–8), 1999–2008 (2013). doi: 10.1016/j.mcm.2012.03.004El-Rabbany, A.: Introduction to GPS: The Global Positioning System, 2nd edn. Artech House, Boston, USA (2006)Trees, HLv: Detection, Estimation and Modulation Theory, Part I. Wiley, New York (2001)Foy, W.H.: Position-location solution by Taylor-series estimation. IEEE Trans. Aerosp. Electron. Syst. AES–12(2), 187–194 (1976)Smith, J.O., Abel, J.S.: The spherical interpolation method of source localization. IEEE J. Ocean. Eng. OE–12(1), 246–252 (1987)Friedlander, B.: A passive localization algorithm and its accuracy analysis. IEEE J. Ocean. Eng. OE–12(1), 234–245 (1987). doi: 10.1109/JOE.1987.1145216Schau, H.C., Robinson, A.Z.: Passive source localization employing intersecting spherical surfaces from time-of-arrival differences. IEEE Trans. Acoust. Speech Signal Process. ASSP–35(8), 1223–1225 (1987)Chan, Y.T., Ho, K.C.: A simple and efficient estimator for hyperbolic location. IEEE Trans. Signal Process. 42(8), 1905–1915 (1994)Schmidt, R.O.: A new approach to geometry of range difference location. IEEE Trans. Aerosp. Electron. Syst. AES–8(6), 821–835 (1972)Geyer, M., Daskalakis, A.: Solving passive multilateration equations using Bancroft’s algorithm. In: Digital Avionics Systems Conference, pp. F41/41–F41/48, Bellevue, WA, USA (1998)Wikipedia: http://en.wikipedia.org/wiki/MultilaterationBancroft, S.: An algebraic solution of the GPS equations. IEEE Trans. Aerosp. Electron. Syst. AES–21(7), 56–59 (1985)Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population