博碩士論文 110553021 詳細資訊




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姓名 彭品皓(Pin-Hao Peng)  查詢紙本館藏   畢業系所 通訊工程學系在職專班
論文名稱 以交互多模型演算法使用飛彈之非線性雷達量測訊號攔截閃躲目標
(Intercepting the Maneuvering Target for a Missile Using the IMM Algorithm with Nonlinear Radar Measurement)
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摘要(中) 本論文研究在提升飛彈雷達尋標器對於機動可規避性移動能力目標的追蹤表現,比較不同運動模型下雷達追蹤的均方根誤差(Root Mean Square Error,RMSE)。本文運用交互多模型擴展式卡爾曼濾波器(Interacting Multiple Model Extended Kalman Filter,IMMEKF),處理非線性雷達量測訊號,再透過座標轉彎、常速度、等加速度,三種運動模型機率混合得出最佳估計值,攔截目標。
本文模擬軟體程式使用Matlab,建立三維模擬環境,相同參數設定下比較使用三種運動模型、二種運動模型、一種運動模型追蹤機動可規避性移動目標,透過模擬結果得知,本文所提導引控制方式有提升追蹤攔截機動可規避性移動目標的效果。
摘要(英) This paper aims to improve the tracking performance of missile radar seekers against maneuverable evasive targets. Root Mean Square Error (RMSE) of radar tracking under different motion models was compared. The Interacting Multiple Model Extended Kalman Filter (IMMEKF) was used to process nonlinear radar measurement signals. The best estimate was obtained by mixing three motion models: coordinated turn, constant velocity, and constant acceleration, and intercepting the target.
Matlab was used to build a three-dimensional simulation environment, and three, two, and one motion model were compared under the same parameter setting to track maneuverable evasive targets. Simulation results show that the proposed guidance control method improves the effectiveness of tracking and intercepting maneuverable evasive targets.
關鍵字(中) ★ 交互多模型演算法
★ 雷達
★ 飛彈攔截
★ 轉向率
關鍵字(英)
論文目次 摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 V
表目錄 VII
符號彙編 VIII
第一章 緒論 1
1.1研究動機及方法 1
1.2章節架構 3
第二章 系統場景 4
2.1系統方程式 4
2.2運動模型 4
2.3雷達量測訊號模型方程式 7
2.4擴展式卡爾曼濾波器 10
2.5交互多模型擴展式卡爾曼濾波器[14] 14
第三章 本文所提之目標追蹤演算法 20
3.1飛彈導引控制攔截目標幾何模型 20
3.2雷達定點觀測目標 22
3.3設置虛擬點導引控制飛彈攔截目標 23
3.4未知轉向率估計[18][19] 26
第四章 模擬比較與結果分析 27
4.1某直升機場景參數設定 27
4.1.1雷達定點 30
4.1.2設置虛擬點飛彈雷達追蹤 36
4.2某戰機場景參數設定 42
4.2.1雷達定點 45
4.2.2設置虛擬點飛彈雷達追蹤 51
4.3探討轉向率估計比較 57
第五章 結論與未來研究發展 59
文獻參考 60
參考文獻 [1]Rafael Yanushevsky, Modern Missile Guidance, CRC Press, 2008.
[2]Becker, K.,“Closed-form Solution of Pure Proportional Navigation,”IEEE Trans. Aerosp. Electron. Syst, vol. 26, no.3, pp. 526-532, May.1990.
[3]馮仰靚,「具加速度時間延遲之飛彈的強健導引律設計」,國立交通大學,民國101年。
[4]李孟軒,「使用擴展型卡爾曼濾波器處理非線性雷達量測訊號進行攔截飛彈導引」,國立中央大學,民國110年。
[5]W. Fan and Y. Li, “Accuracy analysis of sigma-point Kalman filters,” in Proc. Chin. Control Decis. Conf, Guilin, China, pp. 2883–2888, Jun. 2009.
[6]J. Shen, Y. Liu, S. Wang, and Z. Sun, “Evaluation of unscented Kalman filter and extended Kalman filter for radar tracking data filtering,” in Proc. Eur. Model. Symp, pp.190–194, October, 2014.
[7]B. Allotta, L. Chisci, R. Costanzi, F. Fanelli, C. Fantacci, E. Meli, A. Ridolfi, A. Caiti, F. D. Corato, and D. Fenucci, “A comparison between EKF-based and UKF-based navigation algorithms for AUVs localization,” in Proc. IEEE OCEANS Conf, Genoa, Italy, pp. 1–5, May 2015.
[8]Elgamel S A and Soraghan J. “Target tracking enhancement using a Kalman filter in the presence of interference”. IEEE International Geoscience and Remote Sensing Symposium,pp. 681 – 684, July. 2009.
[9]Phil Kim, Kalman Filter for Beginners with MATLAB Examples, A-JIN Publishing, 2010.
[10]Dah.Chung Chang, “Maneuvering Target Tracking with Colored Noise,” IEEE Transactions on Aerospace and Electronic systems vol. 32, NO.4 October, 1996.
[11]Y. Bar-Shalom, K. C. Chang, and H. A. P. Blom, “Tracking a maneuvering target using input estimation versus the interacting multiple model algorithms,”IEEE Trans. Aerosp. Electron. Syst, vol. 25, no. 2, pp. 296–300, Mar. 1989.
[12]魏周賢,「在直視性與非直視性混合環境下使用雙層交互多模型演算法追蹤機動性的目標物」,國立中央大學,民國102年。
[13]S.-T. Park and J.G. Lee, "Improved Kalman filter design for three-dimensional radar tracking," IEEE Transactions on Aerospace and Electronic Systems, vol. 37, no. 2, pp. 727-739, April .2001.
[14]D.-C. Chang and M.-W. Fang, “Bearing-only maneuvering mobile tracking with nonlinear filtering algorithms in wireless sensor networks,”IEEE Systems Journal, vol. 8, no. 1, pp. 160–170,Mar. 2014.
[15]M. Efe and D. P. Atherton, “Maneuvering target tracking using adaptive turn rate models in the interacting multiple model algorithm,”in Proc. of the 35th IEEE Decision and Control, vol. 3, pp. 3151–3156 , Dec. 1996.
[16]J. Mochnac, S. Marchevsky, and P. Kocan, “Bayesian filtering techniques: Kalman and extended Kalman filter basics,” in Proc. Int. Conf. Radioelektronika, pp. 119–122, April. 2009.
[17]R. Adnan, F. A. Ruslan, A. M. Samad and Z. Md Zain, “Extended Kalman Filter (EKF) prediction of flood water level, ” in IEEE Control and System Graduate Research Colloquium, Shah Alam, Selangor, pp. 171-174, July 2012.
[18]S. M. George, S. S. Selvi, C. R. Raghunath and A. S. Pillai, "Improved target tracking using feedback in Kalman smoother for targets moving in co-ordinated turn with unknown turn rate," International Conference on Circuits, Communication, Control and Computing, Bangalore, India, pp. 281-284, November. 2014.
[19]M. Eltoukhy, M. O. Ahmad and M. N. S. Swamy, "An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target," in IEEE Access, vol. 8, pp. 94176-94189, May. 2020.
[20]W. R. Wu, “Target racking with glint noise,” IEEE Trans. Aerosp. Electron. Syst, vol. 29, no. 1, pp. 174–185, Jan. 1993.
指導教授 張大中(Dah-Chung Chang) 審核日期 2023-8-14
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