近年來，自從FCC 提出了E-911 的需求之後，行動定位就開始受到了很大的關注。在這篇論文中，我們使用了粒子濾波器以及凸型最佳化的方法做估測。對現今來說，使用粒子濾波器解決非線性的通道已經成為一個眾所皆知的方法。然而，對於NLOS 傳送所產生的問題，仍然沒有一個定論。在這個研究裡，我們使用了[1]中所提出的混合規範(mixed norm)的概念去發展我們提出解決NLOS問題的最佳化方法。由於在[1]中所提出的最佳化方法不夠全面且過於複雜，所以我們使用一個’虛擬’的觀念，去將混和傳送的情形做進一步的簡化。這個觀念不只可以降低最佳化方法的複雜度，同時也可以增加估測的精準度。另外，我們將[1]中所提出的地圖參數概念，做了更進一步的定義，以便有效的使用。此外，我們還提出了一個如何將粒子濾波器中預測出的距離做個權重的法方，進而增加精準度。在整理完演算法之後，我們介紹了粒子濾波器以及凸型最佳化的架構。由於在粒子濾波器中重新取樣的步驟太過於浪費時序，因此我們提出了一個重新取樣修正的粒子濾波器。我們提出的這個方法，在增進時序的同時，並不會把粒子濾波器該有的精準度給消耗掉。最後，我們將提出的行動定位系統架構放在FPGA 上做了驗證。此外，我們也使用了射頻模組，達到距離預估的實現。

In recent years, the mobile positioning has attracted many attentions since the proposition of E-911 purpose. This thesis adopt particle filter and convex optimization to complete the estimation. For the mobile positioning, particle filter based algorithm has become the common solution of solving the effect of non-linear channel. However, the solution of NLOS problem still does not come to an end. In this thesis, we adopt the mixed norm idea and the concepts of optimization problem of [1] to developing our positioning algorithm for solving NLOS problem. Due to the convex optimization problem of [1] is not general and too complicated, we introduce a ’virtual’ concept to describe the propagation condition. This concept reduces complexity of the optimization problem and enhances the performance simultaneously. Also, the idea of map factor introduced in [1] is discussed in detail and we determine how to assign the map factor. Another approach to improve the positioning performance by weighting the estimated distances come from particle filters is introduced. After constructing the algorithms, we analysed the architectures of particle filter and convex optimization processor. Because the resampling step of particle filter is too exhausted on timing, we proposed a modified resampling particle filter to improve the timing. The proposed modified resampling particle filter is verified without losing performance. Finally, we implement the architecture
design on FPGA to verify our proposed algorithms. We also realize the distance estimation by RF modules and FPGA.

[1] W.-Y. Chiu and B.-S. Chen, “Mobile location estimation in urban areas using mixed manhattan/euclidean norm and convex optimization,” Wireless Communications, IEEE Transactions on, vol. 8, no. 1, pp. 414 –423, jan. 2009.
[2] B. Ye and Y. Zhang, “Improved fpga implementation of particle filter for radar tracking applications,” in Synthetic Aperture Radar, 2009. APSAR 2009. 2nd Asian-Pacific Conference on, oct. 2009, pp. 943 –946.
[3] J. U. Cho, S. H. Jin, X. D. Pham, and J. W. Jeon, “Object tracking circuit using particle filter with multiple features,” in SICE-ICASE, 2006. International Joint Conference, oct. 2006, pp. 1431 –1436.
[4] “http://transition.fcc.gov/e911.”
[5] I. Paton, E. Crompton, J. Gardiner, and J. Noras, “Terminal self-location in mobile radio systems,” in Mobile Radio and Personal Communications, 1991., Sixth
International Conference on, dec 1991, pp. 203 –207.
[6] H. Hashemi, “Pulse ranging radiolocation technique and its application to channel assignment in digital cellular radio,” in Vehicular Technology Conference, 1991. Gateway to the Future Technology in Motion., 41st IEEE, may 1991, pp. 675 –680.
[7] A. Giordano, M. Chan, and H. Habal, “A novel location-based service and architecture,”in Personal, Indoor and Mobile Radio Communications, 1995. PIMRC’95.’Wireless: Merging onto the Information Superhighway’., Sixth IEEE International Symposium on, vol. 2, sep 1995, pp. 853 –857 vol.2.
[8] H. Koshima and J. Hoshen, “Personal locator services emerge,” Spectrum, IEEE, vol. 37, no. 2, pp. 41 –48, feb 2000.
[9] T. Kerr, “A critical perspective on some aspects of gps development and use,” in Digital Avionics Systems Conference, 1997. 16th DASC., AIAA/IEEE, vol. 2, oct
1997, pp. 9.4 –9–9.4–20 vol.2. [10] J. J. Caffery and G. L. Stuber, “Overview of radiolocation in cdma cellular systems,”IEEE Communications Magazine, vol. 36, pp. 38–45, 1998.
[11] I. Jami, M. Ali, and R. Ormondroyd, “Comparison of methods of locating and tracking cellular mobiles,” in Novel Methods of Location and Tracking of Cellular Mobiles and Their System Applications (Ref. No. 1999/046), IEE Colloquium on, 1999, pp. 1/1 –1/6.
[12] M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking,” Signal Processing, IEEE Transactions on, vol. 50, no. 2, pp. 174 –188, feb 2002.
[13] B. Ristic, S. Arulampalm, and N. Gordon, Beyond the Kalman filter : particle filters for tracking applications. Artech House, Boston, Ma. ; London :, 2004. [14] S. Al-Jazzar, J. Caffery, and H.-R. You, “Scattering-model-based methods for toa location in nlos environments,” Vehicular Technology, IEEE Transactions on, vol. 56, no. 2, pp. 583 –593, march 2007.
[15] M. Wylie and J. Holtzman, “The non-line of sight problem in mobile location estimation,” in Universal Personal Communications, 1996. Record., 1996 5th IEEE
International Conference on, vol. 2, sep-2 oct 1996, pp. 827 –831 vol.2.
[16] S. Gezici, H. Kobayashi, and H. Poor, “Nonparametric nonline-of-sight identification,”in Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, vol. 4, oct. 2003, pp. 2544 – 2548 Vol.4.
[17] A. Sankaranarayanan, R. Chellappa, and A. Srivastava, “Algorithmic and architectural design methodology for particle filters in hardware,” in Computer Design:
VLSI in Computers and Processors, 2005. ICCD 2005. Proceedings. 2005 IEEE International Conference on, oct. 2005, pp. 275 – 280.
[18] M. McGuire, K. N. Plataniotis, and A. N. Venetsanopoulos, “Data fusion of power and time measurements for mobile terminal location,” IEEE Transactions on Mobile Computing, vol. 4, pp. 142–153, March 2005. [Online]. Available:http://dx.doi.org/10.1109/TMC.2005.24
[19] B. Fang, “Simple solutions for hyperbolic and related position fixes,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 26, no. 5, pp. 748 –753, sep 1990.
[20] S. Boyd and L. Vandenberghe, Convex Optimization. New York, NY, USA: Cambridge University Press, 2004.
[21] N. Z. Shor, K. C. Kiwiel, and A. Ruszcay`nski, Minimization methods for non-differentiable functions. New York, NY, USA: Springer-Verlag New York, Inc.,
1985.
[22] G. Marsaglia and W. W. Tsang, “The ziggurat method for generating random variables,” Journal of Statistical Software, vol. 5, no. 8, pp. 1–7, 10 2000. [Online].
Available: http://www.jstatsoft.org/v05/i08
[23] (2010) Smims. [Online]. Available: http://www.smims.com/