-
1Dissertation/ Thesis
-
2Dissertation/ Thesis
-
3
المساهمون: 國立臺灣師範大學電機工程學系
Relation: 2009 中華民國系統科學與工程研討會，淡江大學，pp. 54-58。; ntnulib_tp_E0607_02_027; http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32149
-
4
المؤلفون: 黎乃仁, Li, Nai-jen
المساهمون: 淡江大學電機工程學系碩士班, 許陳鑑, Hsu, Chen-chien
مصطلحات موضوعية: 粒子濾波器, 競賽選擇法, 單體搜尋法, 函數追蹤, 均方誤差, 室內定位, Particle Filter, tournament selection, simplex search method, function tracking, RMSE, indoor localization
وصف الملف: 143 bytes; application/octet-stream
Relation: [1] N. Chopin, “A sequential particle filter for static models,” Biometrika, vol. 89, no. 3, pp. 539–552, 2002. [2] M. Isard and A. Blake, “Condensation conditional density propagation for visual tracking,” International Journal of Computer Vision, 1998, pp. 5-28. [3] Z. Khan, T. Balch and F. Dellaert, “MCMC-based particle filtering for tracking a variable number of interacting targets,” IEEE Transactions on pattern analysis and machine intelligence, 2005, pp. 1805-1819. [4] A. Hermoso-Carazo and J. Linares-Pérez, “Different approaches for state filtering in nonlinear systems with uncertain observations,” Applied Mathematics and Computation, vol. 187, 2007, pp. 708-724. [5] Y. Rui and Y. Chen, “Better proposal distributions: object tracking using unscented particle filter,” IEEE Computer Vision and Pattern Recognition, pp. 786-793, 2001. [6] P. M. Djuric, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo, and J. M´ıguez, “Particle filtering,” IEEE Signal Processing Magazine, vol. 20, no. 5, pp. 19-38, 2003. [7] P. M. Djuric, T. Lu, and M. F. Bugallo, “Multiple particle filtering,” ICASSP’07, pp. III-1181-III-1184, 2007. [8] Y. C. Ho and R. C. K. Lee, “A Bayesian approach to problems in stochastic estimation and control,” IEEE Transactions on Automatic Control, vol. AC-9, pp. 333–339, 1964. [9] H. Jayesh, Kotecha and Petar M. Djuric, “Gaussian Particle Filtering,” IEEE Transactions on Signal Processing, vol. 51, No.10, 2003, pp. 2592-2601. [10] N. de Freitas, “Rao-Blackwellised particle filtering for fault diagnosis,” IEEE Aerospace, 2002. [11] F. J. Pei, P. Y. Cui and Y. Z. Chen, “Adaptive MCMC Particle Filter for Nonlinear and Non-Gaussian State Estimation,” Innovative Computing Information and Control, pp. 494-494. 2008. [12] G. Kitagawa and W. Gersch, Smoothness Priors Analysis of Time Series, New York: Springer-Verlag, 1996. [13] Brad L. Miller and David E. Goldberg, “Genetic Algorithms, Tournament Selection, and the Effects of Noise,” IlliGAL Report No. 95006 July 1995. [14] M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon, and Tim Clapp, “A Tutorial on Particle Flters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, 2002. [15] N. Gordon, D. Salmond, and A. F. M. Smith, “Novel approach to nonlinear and non-Gaussian Bayesian state estimation,” IEE Proceedings F Radar and Signal Processing, vol. 140, pp. 107–113, 1993. [16] Ruixin Niu, Pramod K. Varshney, Mark Alford, Adnan Bubalo, Eric Jones, and Maria Scalzo, “Curvature nonlinearity measure and filter divergence detector for nonlinear tracking problems,” Information Fusion, pp. 1-8, 2008. [17] P. M. Djuric and M. F. Bugallo, ”Improved target tracking with particle filtering,” IEEE Aerospace, pp. 1-7, 2009. [18] M. F. Bugallo, T. Lu, and P. M. Djuric, “Target tracking by multiple particle filtering,” IEEE Aerospace, pp. 1-7, 2007. [19] Xu Shifang, Xie Li and Liu Jilin, “Robot localization based on MCMC particle filter,” Joural of Zhejiang University(Engineering Science), 2007, pp. 1083-1087. [20] Y. Zhou, W. Liu, and P. Huang, “Laser-activated RFID-based Indoor Localization System for Mobile Robots,” Robotics and Automation, pp. 4600-4605, 2007. [21] S. Lenser and M. Veloso, “Sensor resetting localization for poorly modelled mobile robots,” Robotics and Automation, 2000. [22] H. Andreasson, A. Treptow, and T. Duckett, “Self-localization in non-stationary environments using omni-directional vision,” Robotics and Autonomous Systems, vol. 55, 2007, pp. 541-551. [23] H. Kim, J. Choi, and M. Park, “Indoor localization system using multi-modulation of ultrasonic sensors and digital compass,” Intelligent Robots and Systems, pp. 1359-1364, 2008. [24] G. Jin, X. Lu, and M. Park, “An indoor localization mechanism using active RFID tag,” Sensor Networks, Ubiquitous, and Trustworthy Computing, vol. 1, 2006. [25] J. González, J. Blanco, C. Galindo, A. Ortiz-de-Galisteo, J. Fernández-Madrigal, F. Moreno, and J. Martínez, “Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach,” Robotics and Autonomous Systems, vol. 57, 2009, pp. 496-507. [26] 鍾鎮謙、宋開泰，運用雷射測距儀之機器人定位設計，國立交通大學電機與控制工程學系碩士論文，台灣，民國96年。 [27] F. Yan, Y. Zhuang, and W. Wang, “Large-scale Topological Environmental Model Based Particle Filters for Mobile Robot Indoor Localization,” Robotics and Biomimetics, pp. 858-863, 2006. [28] N. A. Nelder and R. Mead, “A Simplex Method for Function Minimization,” Computer Journal, vol. 7, 1965, pp. 308-313. [29] C. Andrieu, M. Davy, and A. Doucet, “Improved auxiliary particle filtering: applications to time-varying spectral analysis,” IEEE Workshop on Statistical Signal Processing, pp. 309-312, 2001. [30] R. Kurazume, H. Yamada, K. Murakami, Y. Iwashita, and T. Hasegawa, ”Target tracking using SIR and MCMC particle filters by multiple cameras and laser range finders,” Intelligent Robots and Systems, pp. 3838-3844 , 2008. [31] Georges Oppenheim, Anne Philippe, and Jean de Rigal “The particle filters and their applications,” Chemometrics and Intelligent Laboratory Systems, vol. 91, pp. 87-93, 2008. [32] G. Kitagawa, “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,” Computational and Graphical Statistics, vol. 5, no. 1, 1996, pp. 1-25. [33] F. Daum and J. Huang, “Curse of dimensionality and particle filters,” IEEE Aerospace, 2003. [34] Miroslav Simandl, Ondřej Straka, ” Functional sampling density design for particle filters,” Signal Processing, vol 88, 2008, pp. 2784-2789. [35] O. Straka and M. Simandl, “Using the Bhattacharyya distance in functional sampling density of particle filter,” IFAC World Congress, pp. 1006-1011, 2005. [36] M. Simandl and O. Straka, “Sampling density design for particle filters,” IFAC Symposium on System Identification, vol. 1, 2003. [37] B. Carlin, N. Polson, and D. Stoffer, “A Monte Carlo approach to nonnormal and nonlinear state-space modeling,” American Statistical Association, vol. 87, no. 418, pp. 493–500, 1992. [38] Marco A. Luersen and Rodolphe Le Riche, “Globalized Nelder–Mead method for engineering optimization,” Computers and Structures, vol. 82, pp. 2251-2260, 2004. [39] S. Thrun , W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, Cambridge, MA, 2005. [40] John Lin, Ying Wu, and Thomas S. Huang “Articulate hand motion capturing based on a Monte Carlo Nelder-Mead simplex tracker,” Pattern Recognition, vol. 4, pp. 975-978, 2004. [41] F. Walters, L. R. Parker, S.L. Morgan, and S. N. Deming, Sequential Simplex Optimization, CRC Press, Boca Raton, USA, 1991.; U0002-2508200912024900; http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35789; http://tkuir.lib.tku.edu.tw:8080/dspace/bitstream/987654321/35789/1/