臺灣博碩士論文加值系統

本文提出聯邦α -β-γ濾波器(Federated Alpha-Beta-Gamma Filter)運用於多感測器系統中，在特定區域實施目標之追蹤監控。該濾波器架構則由感測器、區域處理器與全域處理器所組合而成，為解決分散式資訊融合問題的方法之一。在感測器方面，感測器在球面座標系(Spherical Coordinate System)中量測機動目標之距離、方位與俯仰角，轉換至參考直角座標系(Reference Cartesian Coordinate System)。區域處裡器方面，使用解耦濾波技術及利用機動指標(Maneuver Index)以獲得α-β-γ濾波增益，於視線直角座標系(Line-of-Sight Cartesian Coordinate System)中進行濾波，並將結果轉換至參考直角座標系中應用。系統處理雜訊以嚴謹之矩陣上界選定而得。全域處理器組合區域處理器之輸出，係藉由權重最小平方估計器(Weighted Least Squares Estimator)完成，並與最大概似估計器(Maximum Likelihood Estimator) 及共變異匹配法(Covariance Matching Method)等資訊融合方法，透過電腦模擬驗證方法並分析效能。經蒙地卡羅(Monte Carlo)電腦模擬結果，驗證所採用的聯邦α-β-γ濾波器，可減少計算量的負荷，提升濾波器的運算速度，且在全域處理器中使用之資訊融合演算法，無論在位置、速度及加速度之估算誤差收歛上，均有優異的性能，相較於感測器層平均效能分別提升了約77.08%、60.10%及32.31%；相較於區域處理器層平均效能分別提升了約47.91%、30.33%及12.71%，驗證本研究所提出之資訊融合演算法，可明顯改善追蹤精確度。

A federated alpha-beta-gamma filter is developed for utilization in multi-sensor systems tracking a maneuvering target over the certain area. Filter architecture that consists of sensors, local processors and global processor is employed to describe the distributed fusion problem. The sensor filtering algorithm utilized in the Reference Cartesian Coordinate System is presented for target tracking when the sensor measures range, bearing, and elevation angle in the Spherical Coordinate System. Each local processor uses decoupling technique to develop the tracking index to obtain the alpha-beta-gamma filter gain and the corresponding covariance formulations that are recursively computed in the Line-of-Sight Cartesian Coordinate System and then transformed for use in the Reference Cartesian Coordinate system. Common process noise correlations are handled by the factor which is selected by a conservative matrix upper bound. The global processor combines local processor outputs via weighted least square estimator. The resulting filter has computational advantage over traditional maximum likelihood estimator. The results of computer simulations are presented for the performance comparison of proposed filter, traditional maximum likelihood estimator, and covariance matching method. With comparing the reference values of the sensor-level, the Averaged Root Mean Square Error (ARMSE) of position, velocity, and acceleration were found about 77.08%, 60.10%, and 32.31% improved. Also, the performance indexes of position, velocity, and acceleration with the local-processor were found to be larger (about 47.91%, 30.33%, and 12.71%) than with the global-processor, respectively.

指導教授推薦書 i
論文口試委員審定書 ii
博碩士論文電子檔案上網授權書 iv
誌謝 v
摘 要 vi
Abstract vii
目 錄 viii
圖目錄 x
表目錄 xii
符號與縮寫 xiii
第一章　緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 3
第二章 文獻探討 5
2.1多感測器資訊融合簡介 5
2.2卡爾曼濾波演算法 8
2.3簡單解耦α-β-γ濾波器 13
2.4最大概似估計器 15
2.5共變異匹配法 16
2.6聯邦濾波器 17
第三章 研究方法 20
3.1感測器層目標追蹤理論 20
3.1.1目標動態模型 20
3.1.2感測器量測模式 21
3.2區域處理器層之建立 24
3.3簡單解耦α-β-γ濾波器演算法 27
3.4全域處理器層之建立 28
第四章 模擬結果與分析 30
4.1 Ｕ型模擬測試場景 31
4.1.1Ｕ型測試場景以四個感測器模擬 32
4.1.2Ｕ型測試場景以五個感測器模擬 38
4.2 圓型模擬測試場景 44
4.2.1圓型測試場景以四個感測器模擬 45
4.2.2圓型測試場景以五個感測器模擬 51
4.3模擬結果分析 57
第五章 結論與建議 62
5.1結論 62
5.2未來研究方向 62
參考文獻 64
攻讀碩士期間發表之學術論文 67
附錄 68

[1]A. Mitch, and J. K. Aggarwal, “Multiple Sensor Integration/Fusion Through Image Processing：A Review,” Optical Engineering, 25(3), pp. 380-386, 1986.
[2]A. V. Bal Krishnan, “Kalman Filtering Theory, Optimization Software Inc,” Publications Division, New York, 1984.
[3]B. Edde, “Radar Principles, Technology, Applications,” Prentice Hall, pp. 653-662, 1993.
[4]C. B. Chang, and J. A. Tabaczynski, “Application of State Estimation to Target Tracking,” IEEE Transactions on Automatic Control, Vol. AC-29, NO.2, pp. 98-109, 1984.
[5]F. Daum, and R. J. Fitzgerald, “Decoupled Kalman Filter for Phased Array Radar Tracking,” IEEE Transactions on Automatic Control, Vol. AC-28, NO.3, pp. 269-283, 1983.
[6]F. E. White, A. N. Steinberg, and C. L. Bowman, “Revision to the JDL DATA Fusion Model. In Sensor Fusion: Architectures, Algorithms, and Applications.” Proceedings of the SPIE., pp.430-441, 1999.
[7]H. Chen, T. Kirubarajan, and Y. Bar-Shalom, “Performance limits of track-to-track fusion versus centralized estimation: theory and application.” IEEE Transactions on Aerospace and Electronic Systems, 2003.
[8]J. A. Rocker, and C. D. McGill, “Comparison of two-sensor tracking methods based on state-vector fusion and measurement fusion,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-24, No. 4, pp. 447-449, 1988.
[9]J. B. Pearson, and E. B. Stear, “Kalman Filter Applications in Airborne Radar Tracking,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-10, NO.3, pp. 319-329, 1974.
[10]J. R. Raol, and G. Girija, "Sensor data fusion algorithms using square-root information filtering," IEE Proceedings-Radar, Sonar and Navigation, 149, pp. 89-96, 2002.
[11]K. C. Chang, R. K. Saha, and Y. Bar-Shalom, “On Optimal Track-to-Track Fusion,” IEEE Transactions on Aerospace and Electronic System, Vol. 33, No.4, 1997.
[12]K. V. Ram Chandra, and V. S. Srinivasan, “Steady State Results for The x y z Kalman Tracking Filter,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-13, pp. 419-423, 1974.
[13]L. W. Fong, "Distributed Data Fusion Algorithms for Tracking a Maneuvering Target," The 10th International Conference on Information Fusion, 2007.
[14]L. W. Fong, “Multi-sensor Data Fusion via Federated Dual-Band Filter,” IEEE International Conference on Networking, Sensing and Control (ICNSC 08), 2008.
[15]L. W. Fong, “Multisensor Fusion via Alpha-Beta-Gamma Filtering with Covariance Matching Method,” Journal of Aeronautics, Astronautics and Aviation, Series A, Vol.38, No.4, pp.241 – 250, 2006.
[16]L. W. Fong, and C. C. Wang, “Distributed Data Fusion via Federated Alpha-Beta-Gamma Filter,” IEEE International Conference on Industrial Technology (ICIT 08), 2008.
[17]L. W. Fong, and P. F. Fu,“Multisensor Track Fusion Algorithms：Covariance Matching Method versus Bayesian-based Estimation,” Proceedings of 2006 CACS Automatic Control Conference, 2006.
[18]L. W. Fong, and Z. Z. Chen, “Multisensor Track Fusion Using IMM Filters with Covariance Matching Method,” 2006 AASRC/CCAS Joint Conference, 2006.
[19]L. W. Fong, “Multi-sensor Track-to-Track Fusion Using Simplified Maximum Likelihood Estimator for Maneuvering Target Tracking,” IEEE International Symposium on Industrial Electronics (ISIE 2007), 2007.
[20]L. Y. Pao, “Distributed Multisensor Fusion,” Navigation and Control Conference, Pt. 1 (A94-29961 10-63), pp. 1-3, 1994.
[21]M. S. Grewal and A. P. Andrews, “Kalman Filtering: Theory and Practice Using MATLAB,” Wiley-Interscience. 2001.
[22]N. A. Carlson, “Federated Square Root Filter for Decentralized Processes,” IEEE Transactions on Aerospace and Electronic Systems, vol. 26, no. 3, pp. 517-525, 1990.
[23]P. R. Kalata, “The tracking index: A generalized parameter for α-β and α-β-γ target trackers,” IEEE Transactions on Aerospace and Electronic Systems, pp.174-182, 1984.
[24]Q. Gann, and C. J. Harris, “Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-37, NO.1, pp. 273-280, 2001.
[25]R. A. Singer, “Estimating optimum tracking filter performance for manned maneuvering targets,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-6,Jul.,pp. 473-483, 1970.
[26]S. J. Julier, and J. K. Uhlmann, “A New Extension of the Kalman Filter to Nonlinear Systems,” SPIE, Vol.3068, pp.182-193, 1997.
[27]S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “A New Method for the Nonlinear Transformation of Means and Covariance in Filters and Estimators,” IEEE Trans. On AC, Vol.45, No.3, pp.477-482, 2000.
[28]T. L. Song, J. Y. Ahn, and C. Park, “Suboptimal Filter Design with pseudo-measurements for Target Tracking,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 24, NO.1, pp. 28-39, 1988.
[29]Y. Bar-Shalom, “On the Track-to-Track Correlation Problems,” IEEE Transactions on Automatic Control, Vol, AC-26, NO.2, 1981.
[30]Y. Bar-Shalom, and T. E. Fortmann, "Tracking and Data Association", NY: Academic Press, 1988.
[31]Y. Bar-Shalom, and X. R. Li, “Estimation and tracking: principles, techniques, and software.” Norwood, MA, Artech House, Chap. 6, pp. 284-287, 1993.
[32]何友、修建娟、張晶煒、關欣，“雷達數據處理及應用”， 北京電子工業出版社，2000年。
[33]崔永俊、張俊艷，“單兵定位導航系統數據融合方法研究”，華北工學院學報，第26卷第2期，2005年。
[34]傅珮芬“植基於簡單解耦濾波結合最優線性估計器之資訊融合研究”碩士論文，育達商業技術學院，臺灣，2007年。
[35]馮力威、王千竹，“植基於 ALPHA-BETA-GAMMA 濾波器之分散式資訊融合演算法比較研究”，第15屆三軍官校基礎學術研討會，2008年。
[36]馮力威、莊謙亮、范承佑、陳中治、傅珮芬，“解耦交互式多模式與加權最小平方估計器應用於資料融合之研究”，育達商業技術學院2007資訊管理及應用國際研討會，pp.147-156，2007年。
[37]馮力威、莊謙亮、傅珮芬，“運用多無線通訊地標台解耦資訊融合技術於行動載具定位估測之研究”，樹德科技大學2007資通技術管理與應用會議，2007年。
[38]馮力威、陳正雄、樓壁卿、莊謙亮、江冠臻，“簡單解耦雷達追蹤濾波器之設計與分析”，第十二屆國防管理暨實務研討會，2002年。
[39]劉國良、張迎春、孫增圻，“聯邦濾波器的濾波穏定性研究”，中國慣性技術學報，第12卷第6期，2004年。
[40]韓崇昭、朱洪豔、段戰勝，“多源信息融合”，清華大學出版社，2006年。