論文使用權限 Thesis access permission:校內外都一年後公開 withheld
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available
論文名稱 Title |
以新型模擬退火演算法實現通道感知系統的最佳化本地感測器偵測法則之設計 Optimal Local Sensor Decision Rule Design for the Channel-Aware System with Novel Simulated Annealing Algorithms |
||
系所名稱 Department |
|||
畢業學年期 Year, semester |
語文別 Language |
||
學位類別 Degree |
頁數 Number of pages |
83 |
|
研究生 Author |
|||
指導教授 Advisor |
|||
召集委員 Convenor |
|||
口試委員 Advisory Committee |
|||
口試日期 Date of Exam |
2009-06-19 |
繳交日期 Date of Submission |
2009-08-18 |
關鍵字 Keywords |
分散式偵測、通道感知系統、模擬退火演算法、全域最佳、本地感測器、偵測法則 Simulated Annealing Algorithm, Channel-Aware System, Distributed Detection, Decision Rule, Local Sensor, Global Optimal |
||
統計 Statistics |
本論文已被瀏覽 5663 次,被下載 1554 次 The thesis/dissertation has been browsed 5663 times, has been downloaded 1554 times. |
中文摘要 |
近年來,分散式偵測已經被廣泛的研究。分散式偵測最普遍的模型是包含分散的本地感測 器及融合中心。在一個分散式偵測系統中,許多個感測器共同去區別兩個或者多個假設。 例如,有無目標存在。在這篇論文中,傳統的分散式偵測在無線感測器網路的應用下被重 新檢視。為了將融合中心的錯誤率最小化,我們考慮一個在通道感知系統下設計最佳二位 元本地感測器偵測法則的傳統方法。換言之,它結合傳輸通道特性去找到最佳二位元本地 感測器偵測門檻以將融合中心的錯誤率最小化。並且,在不同的通道狀態資訊下,會有不 同的最佳二位元本地感測器偵測門檻。由於最佳多位元(軟式)本地感測器偵測比最佳二位 元本地感測器偵測更實用。為了允許多位元本地感測器輸出,我們也考慮了另一個在通道 感知系統下設計最佳多位元本地感測器偵測法則的傳統方法。然而要設計最佳的本地感測 器偵測法則,傳統的兩個方法都很容易陷入局部最佳門檻,取決於預先選定的初始值。為 了克服這個問題,我們考慮了數種改良式模擬退火演算法。根據這些改良式模擬退火演算 法及兩種傳統方法,我們提出兩種新型模擬退火演算法以實現最佳本地感測器偵測法則。 電腦模擬結果顯示使用兩種新型模擬退火演算法在最佳二位元本地感測器偵測問題及最 佳多位元本地感測器偵測問題都可以避免陷入局部最佳門檻。並且兩種新型模擬退火演算 法比起傳統的模擬退火演算法提供較低的搜尋點數效能。 |
Abstract |
Recently, distributed detection has been intensively studied. The prevailing model for distributed detection (DD) is a system involving both distributed local sensors and a fusion center. In a DD system, multiple sensors work collaboratively to distinguish between two or more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD problem is reexamined in the context of wireless sensor network applications. For minimize the error probability at the fusion center, we consider the conventional method that designs the optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the transmission channel characteristics for find the optimal binary local sensor decision threshold to minimize the error probability at the fusion center. And there have different optimal local sensor decision thresholds for different channel state information. Because of optimal multi-bit (soft) local sensor decision is more practical than optimal binary local sensor decision. Allowing for multi-bit local sensor output, we also consider another conventional method that designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system. However, to design the optimal local sensor decision rule, both of two conventional methods are easily trapped into local optimal thresholds, which are depended on the pre-selected initialization values. To overcome this difficulty, we consider several modified Simulated Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional methods, we propose two novel SA algorithms for implementing the optimal local sensor decision rule. Computer simulation results show that the employments of two novel SA algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor decision problem and optimal multi-bit local sensor decision problem. And two novel SA algorithms offer superior performance with lower search points compared to conventional SA algorithm. |
目次 Table of Contents |
誌謝 ............................................................................................................................. .i Abstract .................................................................................................................... .ii Contents .................................................................................................................... iv List of Figures ........................................................................................................ vi List of Tables ........................................................................................................ viii Chapter 1 Introduction......................................................................................1 Chapter 2 Model of the Distributed Detection System ..........................5 2.1 Introduction ....................................................................................................5 2.2 System Model Description .............................................................................6 2.3 LRT for Optimal Binary Local Sensor Decision .........................................8 2.4 LRT for Optimal Multi-Bit Local Sensor Decision ................................... 11 Chapter 3 Simulated Annealing Parameter Setting and Novel Simulated Annealing Algorithms ............................................16 3.1 Introduction ..................................................................................................16 3.2 Initial Current Solution Setting of the SA Algorithms .............................19 3.2.1 The Diagonal Initial Current Solution Setting of the SA Algorithm .......20 3.2.2 Initial Current Solution Setting in the SA Algorithm with the Parallel Simulated Annealing Algorithm ...............................................................21 v 3.3 Neighborhood Structure of the SA Algorithm ...........................................23 3.3.1 The Directional Neighborhood Structure of the SA Algorithm ...............23 3.4 Cooling Schedule ..........................................................................................26 3.4.1 Conventional Cooling Schedule ..............................................................26 3.4.2 The Modified Lam Schedule ....................................................................27 3.4.3 Parallel Temperature Cooling Schedule (PTCS) ....................................30 3.5 Novel Simulated Annealing Algorithms .....................................................31 Chapter 4 Computer Simulation Results ..................................................34 4.1 Preliminaries ................................................................................................34 4.2 Computer Simulation Results of Optimal Binary Local Sensor Decision Rule................................................................................................................36 4.3 Computer Simulation Results of Optimal Multi-Bit Local Sensor Decision Rule ................................................................................................42 Chapter 5 Conclusions .....................................................................................46 Appendix A Method of [4] for Optimal Binary Local Sensor Decision ........................................................................................48 Appendix B Method of [6] for Optimal Multi-Bit Local Sensor Decision ........................................................................................53 Appendix C The Transformation for Local Sensor Decision Thresholds ...................................................................................63 Appendix D The SA-Based Approach [9] for Optimal Local Sensor Decision Rule .............................................................................65 vi References ................................................................................................................68 |
參考文獻 References |
[1] Biao Chen, Lang Tong, and Pramod K. Varshney, “Channel-Aware Distributed Detection in Wireless Sensor Networks.” IEEE Signal Processing Magazine, vol. 23, No. 4, pp. 16-26, July 2006. [2] Biao Chen, Ruixiang Jiang, Kasetkasem T., Varshney P.K., “Channel Aware Decision Fusion in Wireless Sensor Networks,” IEEE Trans. on Signal Processing., vol. 52, No. 12, pp. 3454-3458, December 2004. [3] Z. Chair and P. K. Varshney, “Optimal data fusion in multiple sensor detection systems,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-22, pp. 98–101, Jan. 1986. [4] B. Chen and P.K. Willett, “On the optimality of likelihood ratio test for local sensor decision rules in the presence of non-ideal channels,” IEEE Trans. on Inform. Theory, vol. 51, pp. 693–699, Feb. 2005. [5] B. Chen and P.K. Willett, “Channel optimized binary quantizers for distributed sensor networks,’’ in Proc. IEEE Int. Conf. Acoustic Speech, Signal Processing (ICASSP’2004), Montreal, Canada, May 2004, vol. 3, pp. 845-848. [6] B. Liu and B. Chen, “Channel-Optimized Quantizers for Decentralized Detection in Sensor Networks,” IEEE Trans. Inform. Theory, vol. 52, pp. 3349–3358, July. 2006. [7] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671-680, May 1983. [8] William L. Goffe, Gary D.Ferrier, John Rogers, “Global optimization of statistical functions with simulated annealing,” Journal of Econometrics 60 (1994) 65-99. North-Holland. [9] Mon-Chau SHIE, Wen-Hsien FANG, Kuo-Jui HUNG, and Feipei LAI, “Fast, Robust Block Motion Estimation Using Simulated Annealing,” IEICE Trans. Fundamentals, vol.E83-A, No.1 January 2000. [10] D. Janaki Tam, T. H. Sreenivas, and K. Ganapathy Subramaniam, “Parallel Simulated Annealing Algorithms,” JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 37, 207–212 (1996). [11] Zbigniew J. Czech, “Speeding up sequential simulated annealing by parallelization,” IEEE CNF PARELEC.2006, 349-356. 69 [12] Keiko ANDO, Mitsunori MIKI, and Tomoyuki HIROYASU, “Multi-point Simulated Annealing with Adaptive Neighborhood,” IEICE Trans. Inf.& Syst., vol.E90-D, No.2 February 2007. [13] Hajime KITA, Isao ONO and Shigenobu KOBAYASHI, “Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms,” The 1998 IEEE International Conference on Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, 4-9 May 1998 p.p. 529 – 534. [14] Boyan, J.A.: Learning Evaluation Functions for Global Optimization. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania (1998). [15] Vincent A. Cicirello “On the Design of an Adaptive Simulated Annealing Algorithm,” Computer Science and Information Systems, The Richard Stockton College of New Jersey. [16] Mitsunori MIKI, Tomoyuki HIROASU, Masayuki KASAI, Keiko ONO, and Takeshi JITTA, “Temperature Parallel Simulated Annealing with Adaptive Neighborhood for Continuous Optimization Problem,” Computational Intelligence and Applications (2002) p.p. 149-154. |
電子全文 Fulltext |
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。 論文使用權限 Thesis access permission:校內外都一年後公開 withheld 開放時間 Available: 校內 Campus: 已公開 available 校外 Off-campus: 已公開 available |
紙本論文 Printed copies |
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。 開放時間 available 已公開 available |
QR Code |