A Study On The Application Of Bio-inspired Algorithms To The Problem Of Direction Of Arrival Estimation [um Estudo Da Aplicação De Algoritmos Bio-inspirados Ao Problema De Estimação De Direção De Chegada]

The classical solution to the problem of estimating the direction of arrival (DOA) of plane waves impinging on a sensor array is based on the application of the maximum likelihood method. This approach leads to the problem of optimizing a cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-noise ratio (SNR). The methods proposed in the literature to solve this problem fail for a wide set of SNR values. This work presents the results obtained from a study on the application of natural computing algorithms to the DOA estimation problem. Computational simulations show that four of the analyzed algorithms find the global optimum for a broad range of SNR values with computational efforts lower than that associated with an exaustive search.204609626Ada, G.L., Nossal, G.J.V., The clonal selection theory (1987) Scientific American, pp. 50-57Attux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal of Applied Signal Processing, 2003 (6), pp. 740-747Bäck, T., Fogel, D., Michalewicz, Z., (1997) Handbook of Evolutionary Computation, , Institute of Physics Publishing and Oxford University PressCioppa, A.D., Stefano, C.D., Marcelli, A., On the role of population size and niche radius in fitness sharing (2004) IEEE Transactions on Evolutionary Computation, 8 (6), pp. 580-592Coelho, L.S., Mariani, V.C., Sistema híbrido neuroevolutivo aplicado ao controle de um processo multivariável (2006) SBA Controle & Automação, 17, pp. 32-48Darwen, P., Yao, X., A dilemma for fitness sharing with a scaling function (1995) Evolutionary Computation, Proceedings of IEEE International Conference on, pp. 166-171. , Piscataway, NJDe Castro, L.N., (2006) Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications, , Chapman & Hall/CRCDe Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) IEEE International Conference on Evolutionary Computation, 1, pp. 674-699De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Forster, P., Larzabal, P., Boyer, E., Threshold performance analysis of maximum likelihood DOA estimation (2004) Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 52 (11), pp. 3183-3191Gershman, A., Stoica, P., MODE with extra-roots (MODEX): A new DOA estimation algorithm with an improved threshold performance (1999) IEEE International Conference on Acoustics, Speech, and Signal Processing, 5, pp. 2833-2836Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-WesleyGoldberg, D.E., Richardson, J., Genetic algorithms with sharing for multimodal function optimization (1987) 2nd Int. Conf. Genetic Algorithms, pp. 41-49Haykin, S., (1985) Array Signal Processing, , Prentice Hall, Englewood Cliffs, NJHolland, J., (1992) Adaptation in Natural and Artificial Systems, , 2nd edn, The MIT PressJerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol. (Inst. Pasteur), pp. 373-389Kay, S.M., (1993) Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, , Prentice Hall Signal Processing Series, Englewood Cliffs, NJKennedy, J., The particle swarm: Social adaptation of knowledge (1997) IEEE International Conference on Evolutionary Computation, pp. 303-308Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, 4, pp. 1942-1948Krim, H., Viberg, M., Two decades of array signal processing research: The parametric approach (1996) IEEE Signal Processing Magazine, 13 (4), pp. 67-94Krummenauer, R., (2007) Filtragem ótima na estimação de direção de chegada de ondas planas usando arranjo de sensores, , Master's thesis, School of Electrical and Computer Engineering - UNICAMP, Campinas-SP-BrazilLopes, A., Bonatti, I.S., Peres, P.L.D., Alves, C.A., Improving the MODEX algorithm for direction estimation (2003) Signal Processing, 83 (9), pp. 2047-2051Mahfoud, S.W., (1995) Niching Methods for Genetic Algorithms, , PhD thesis, University of Illinois at Urbana-ChampaignManikas, A., (2004) Differential Geometry in Array Processing, , Imperial College PressPétrowski, A., A clearing procedure as a niching method for genetic algorithms (1996) Evolutionary Computation, Proceedings of IEEE International Conference on, pp. 798-803Rife, D., Boorstyn, R., Single tone parameter estimation from discrete-time observations (1974) IEEE Transactions on Information Theory, 20 (5), pp. 591-598Sareni, B., Krahenbuhl, L., Fitness sharing and niching methods revisited (1998) IEEE Transactions on Evolutionary Computation, 2, pp. 97-106Silva, V.V.R., Khatib, W., Fleming, P.J., Control system for a gas turbine engine using evolutionary computing for multidisciplinary optimization (2007) SBA Controle & Automação, 18 (4), pp. 471-478Stoica, P., Nehorai, A., Performance study of conditional and unconditional direction-of-arrival estimation (1990) IEEE Transactions on Acoustics, Speech, and Signal Processing, 38 (10), pp. 1783-1795Stoica, P., Sharman, K.C., Novel eigenanalysis method for direction estimation (1990) IEE Proceedings part F (Radar and Signal Processing), 137 (1), pp. 19-26Van Trees, H.L., (2001) Optimum Array Processing. Part IV of Detection, Estimation and Modulation Theory, , John Wiley and Sons, New York, US

Multivariate Ant Colony Optimization In Continuous Search Spaces

This work introduces an ant-inspired algorithm for optimization in continuous search spaces that is based on the generation of random vectors with multivariate Gaussian pdf. The proposed approach is called MACACO - Multivariate Ant Colony Algorithm for Continuous Optimization - and is able to simultaneously adapt all the dimensions of the random distribution employed to generate the new individuals at each iteration. In order to analyze MACACO's search efficiency, the approach was compared to a pair of counterparts: the Continuous Ant Colony System (CACS) and the approach known as Ant Colony Optimization in Rn (ACOR). The comparative analysis, which involves wellknown benchmark problems from the literature, has indicated that MACACO outperforms CACS and ACOR in most cases as the quality of the final solution is concerned, and it is just about two times more costly than the least expensive contender. Copyright 2008 ACM.916Bilchev, G., Parmee, I.C., The ant colony metaphor for searching continuous design spaces (1995) Lecture Notes in Computer Science, 993, pp. 25-39. , T. C. Fogarty, editor, Evolutionary Computing, AISB Workshop, of, SpringerBox, G.E.P., Muller, M.A., A note on the generation of random normal deviates (1958) Annals. Math. Stat, 29, pp. 610-611de França, F.O., Von Zuben, F.J., de Castro, L.N., Max min ant system and capacitated p-medians: Extensions and improved solutions (2005) Informatica (Slovenia), 29 (2), pp. 163-172Dorigo, M., (1992) Optimization, Learning and Natural Algorithms, , PhD thesis, Politecnico di Milano, ItalyDorigo, M., Di Caro, G., The ant colony optimization meta-heuristic (1999) New Ideas in Optimization, pp. 11-32. , D. Corne, M. Dorigo, and F. Glover, editors, McGraw-Hill, LondonDorigo, M., Stützle, T., The ant colony optimization metaheuristic: Algorithms, applications, and advances (2003) Handbook of Metaheuristics, pp. 251-286. , F. W. Glover and G. A. Kochenberger, editors, Kluwer Academic PressDréo, J., Siarry, P., A new ant colony algorithm using the heterarchical concept aimed at optimization of multiminima continuous functions (2002) Lecture Notes in Computer Science, 2463, pp. 216-221. , M. Dorigo, G. D. Caro, and M. Sampels, editors, Ant Algorithms, of, SpringerM. Guntsch and M. Middendorf. A population based approach for ACO. In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, and G. Raidl, editors, Applications of Evolutionary Computing, Proceedings of Evo Workshops2002: Evo COP, EvoIASP, EvoSTim, 2279 of LNCS, pages 72-81, Kinsale, Ireland, 3-4 2002. Springer-VerlagHernádvölgyi, I.T., Generating random vectors from the multivariate normal distribution (1998), Technical Report TR-98-07, University of Ottawa, Aug. 20Marsaglia, G., Tsang, W.W., The ziggurat method for generating random variables (2000) Journal of Statistical Software, 5 (8), pp. 1-7Monmarché, N., Venturini, G., Slimane, M., On how Pachycondyla apicalis ants suggest a new search algorithm (2000) Future Generation Computer Systems, 16 (8), pp. 937-946Pourtakdoust, S.H., Nobahari, H., An extension of ant colony system to continuous optimization problems (2004) Lecture Notes in Computer Science, 3172, pp. 294-301. , M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, and T. Stützle, editors, ANTS Workshop, of, SpringerShang, Y.-W., Qiu, Y.-H., A note on the extended Rosenbrock function (2006) Evolutionary Computation, 14 (1), pp. 119-126. , MarchSocha, K., Dorigo, M., Ant colony optimization for continuous domains (2006) European Journal of Operational Research, In Press, Corrected ProofStützle, T., Dorigo, M., ACO algorithms for the quadratic assignment problem (1999) New Ideas in Optimization, pp. 33-50. , D. Corne, M. Dorigo, and F. Glover, editors, McGraw-Hill, Londo

Concerning Criteria For Unsupervised Equalization [sobre Critérios Para Equalização Não- Supervisionada]

In this work, we study the criteria used to solve the blind equalization problem. Two approaches are considered in detail: the constant modulus and the Shalvi-Weinstein criteria. In the course of our exposition, a more recent and less studied technique, the generalized constant modulus criterion, is also discussed. Some of the most important results found in the literature are presented together with some recent contributions related to the comparison between blind criteria and between unsupervised techniques and the Wiener criterion.173278299Benveniste, A., Goursat, M., Ruget, G., Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications (1980) IEEE Trans. on Automatic Control, AC-25 (3), pp. 385-399Cavalcanti, F., Brandão, A., Romano, J., A generalized constant modulus algorithm for blind equalization (1998) Globecom, Sidney, AustráliaCavalcanti, F.R., (1999) Antenas Inteligentes e Processamento Espaço-Temporal para Sistemas de Comunicação sem fio, , Tese de doutorado, UNICAMPCavalcanti, F.R., Brandão, A., Romano, J.M., A generalized constant modulus algorithm for blind equalization (1998) Proc. of Globecom, , Sidney, AustraliaDing, Z., Jonhson, C., Kennedy, R., On the (non)existence of undesirable equilibria of Godard blind equalizers (1992) IEEE Trans. on Signal Processing, 40 (10), pp. 2425-2432Ding, Z., Kennedy, R., Anderson, B., Jonhson, C., Ill- convergence of Godard blind equalizers in data communication systems (1991) IEEE Trans. on Communications, 39 (9), pp. 1313-1327Donoho, D., On minimum entropy deconvolution (1981) Applied Time Series Analysis, 2. , D. F. Findley ed, Academic PressFoschini, G., Equalization without altering or detecting data (1985) Bell Syst. Tech. J, 64 (8), pp. 1885-1911Godard, D., Self-recovering equalization and carrier tracking in two-dimensional data communication systems (1980) IEEE Trans. on Communications, 28 (11), pp. 1867-1875Gu, M., Tong, L., Geometrical characterizations of constant modulus receivers (1999) IEEE Trans. on Signal Processing, 47 (10), pp. 2745-2756Gu, M., Tong, L., Domains of attraction of Shalvi-Weinstein receivers (2001) IEEE Trans. on signal Processing, 49 (7), pp. 1397-1408Haykin, S., (1996) Adaptive Filter Theory, , 3 edn, Prentice HallJohnson, C., Anderson, B., Godard blind equalizer error surface characteristics: White, zero-mean, binary source case (1995) International Journal of Adaptive Control and Signal Processing, pp. 301-324Johnson, C., Schniter, P., Endres, T., Behm, J., Brown, D., Casas, R., Blind equalization using the constant modulus criterion: A review (1998) Proc. of the IEEE, 86 (10), pp. 1927-1950Kennedy, R., Ding, Z., Blind adaptive equalizers for quadrature amplitude modulated communication systems based on convex cost functions (1992) Optical Engeneering, 31 (6), pp. 1189-1199Li, Y., Ding, Z., Convergence analysis of finite length blind adaptive equalizers (1995) IEEE Trans. on Signal Processing, 43 (9), pp. 2121-2129Li, Y., Liu, K.R., Static and dynamic convergence behavior of adaptive blind equalizers (1996) IEEE Trans. on Signal Processing, 44 (11), pp. 2726-2735Li, Y., Liu, K.R., Ding, Z., Length- and cost-dependent local minima of unconstrained blind channel equalizers (1996) IEEE Trans. on Signal Processing, 44 (11), pp. 2726-2735Lucky, R., Salz, J., Weldon, E., (1968) Principles of Data Communication, , MacGraw-Hill, Nova YorkMboup, M., Regalia, P., On the equivalence between the super-exponential algorithm and a gradient search method (1999) Proc. of IEEE Int. Conference on Acoustic, Speech and Signal Processing, 5, pp. 2643-2646. , Phoenix, ppMboup, M., Regalia, P., A gradient search interpretation of the super-exponential algorithm (2000) IEEE Trans. on Information Theory, 46 (7), pp. 2731-2734Neves, A., (2001) Uma abordagem unificada para algoritmos de equalização autodidata, , Tese de mestrado, UnicampNeves, A., Miranda, M., Romano, J., New issues on the criterion properties and algorithm convergence of the generalized CM approach (2002) Proc. of the International Telecommunication Symposium, ITS, , Natal, BrasilNikias, C., Petropulu, A., (1993) Higher Order Spectra Analysis - A Nonlinear Signal Processing Framework, , 1 ednPapoulis, A., (1991) Probability, Random Variables, and Stochastic Process, , 3 ednPicchi, G., Prati, G., Blind equalization and carrier recovery using a "stop-and-go"decision-directed algorithm (1987) IEEE Trans. on Communications, COM-35, pp. 877-887Proakis, J., (1995) Digital Communications, , 3 ednQuresh, S., Adaptive equalization (1985) Proceedings of the IEEE, 73 (9), pp. 1349-1387Regalia, P., On the equivalence between the Godard and Shalvi Weinstein schemes of blind equalization (1999) Signal Processing, 73 (1-2), pp. 185-190Regalia, P., Mboup, M., Undermodeled equalization: A characterization of stationary points for a family of blind criteria (1999) IEEE Trans. on Signal Processing, 47 (3), pp. 760-770Sato, Y., A method for self recovering equalization (1975) IEEE Trans. on Communications, COM-23 (6), pp. 679-682Schniter, P., Johson, C., Bounds for the MSE performance of constant modulus estimators (2000) IEEE Trans. on Information Theory, 46 (7), pp. 2544-2560Shalvi, O., Weinstein, E., New criteria for blind deconvolution of nonminimum phase systems (channels) (1990) IEEE Trans. on Information Theory, 36 (2), pp. 312-321Shalvi, O., Weinstein, E., Super-exponential methods for blind deconvolution (1993) IEEE Trans. on Information Theory, 39 (2), pp. 504-519Shalvi, O., Weinstein, E., (1994) Blind Deconvolution, , Prentice Hall, chapter Universal Methods for Blind DeconvolutionSuyama, R., Attux, R.R.F., Romano, J.M.T., Bellanger, M., Relations entre les critères du module constant et de wiener (2003) 19e Colloque GRETSI - Paris - FrançaTreichler, J., Agee, B., New approach to multipath correction of constant modulus signals (1983) IEEE Trans. on Acoust., Speech and Signal Processing, ASSP-31 (2), pp. 459-472Treichler, J., Fijalkow, I., Johnson, C., Fractionally spaced equalizers (1996) IEEE Signal Processing Magazine, pp. 65-81Tugnait, J., Comments on 'New approach for blind deconvolution of nonminimum phase systems (channels) (1992) IEEE Trans. on Information Theory, 28, pp. 210-213Walach, E., Widrow, B., The least mean fourth (LMF) adaptive algorithm and its family (1984) IEEE Transactions on Information Theory, IT-30 (2), pp. 275-283Wiggins, R., Minimum entropy deconvolution (1978) Geoexplorations, 16, pp. 21-35Zeng, H., Tong, L., Johnson, C., Relationships between the constant modulus and wiener receivers (1998) IEEE Trans. on Information Theory, 44 (4), pp. 1523-1538Zeng, H., Tong, L., Johnson, C., An analysis of constant modulus receivers (1999) IEEE Trans. on Signal Processing, 47 (11), pp. 2990-299