• 沒有找到結果。

應用最佳估測原理於雷達追?系統之研究 胡國昌、陳雍宗

N/A
N/A
Protected

Academic year: 2022

Share "應用最佳估測原理於雷達追?系統之研究 胡國昌、陳雍宗"

Copied!
3
0
0

加載中.... (立即查看全文)

全文

(1)

應用最佳估測原理於雷達追?系統之研究 胡國昌、陳雍宗

E-mail: [email protected]

摘 要

在雷達追蹤系統中,如何有效的掌握目標運動狀態是非常重要的,其中以變速度檢測技術以及資料結合技術為影響目標追 蹤成效以及準確率的重要關鍵。變速度檢測技術在於當目標以變加速度運動時,此時會造成量測誤差變大,需要另外給予 濾波器補償參數,以免誤差過大造成追蹤失敗。雷達在做多目標追蹤時,為了正確的耦合目標的估計值以及量測值,需要 以資料結合技術加以判斷,以減少目標追蹤所造成的誤差。除此之外,在雷達追蹤系統中,為了得到最佳的估測結果,本 論文提出應用最佳估測原理於雷達追?系統做目標追蹤,以增加追?系統的調適性及反應速度。藉此輔助目標追蹤,增加雷 達追蹤目標的判斷能力,以求得更精確的量測結果。

關鍵詞 : 變速度檢測技術、資料結合技術、最佳估測原理 目錄

封面內頁 簽名頁 授權書.........................iii 中文摘要............

............iv 英文摘要........................v 誌謝.........

.................vi 目錄..........................vii 圖目錄...

......................ix 表目錄.........................xi 第 一章 緒論......................1 1.1 研究動機..................1 1.2 研究背景及目的...............1 1.3 研究方法..................3 1.4 論文架構

..................4 第二章 最佳估測原理..................5 2.1 前言..

..................5 2.2 數學模式..................6 2.3 卡門濾波器...

..............8 2.4 擴展式卡門濾波器..............10 第三章 應用類神經網路於資 料相關結合理論........13 3.1 前言....................13 3.2 多目標追蹤程序...

............14 3.2.1 追蹤起始................14 3.2.2 追蹤更新..........

......15 3.2.3 追蹤移除................16 3.3 掃瞄區域(Gate)預測............

.17 3.4 資料相關結合技術..............19 第四章 資料相關結合技術..............

..23 4.1 前言....................23 4.2 變速度追蹤理論...............23 4.3 適應性多模預估器..............26 第五章 模擬結果分析.................

.30 5.1 電腦模擬..................30 5.2 電腦模擬結果分析..............31 第 六章 結論......................44 參考文獻.....................

...45 圖目錄 圖2.1 卡門濾波器示意圖.................5 圖3.1 多目標系統流程圖.......

..........14 圖3.2 追蹤初始相互關係圖................15 圖3.3 執行追蹤預估示意圖.

...............16 圖3.4 整體追蹤程序流程圖................17 圖3.5 目標追蹤掃 瞄區域預測示意圖............17 圖3.6 CHNN目標軌跡與量測值示意圖...........22 圖4.1 適應性多模預估器示意圖..............27 圖5.1 ㄧ目標追蹤模擬結果(演算法ㄧ) ..........

.32 圖5.2 ㄧ目標追蹤模擬結果(演算法二) ..........32 圖5.3 ㄧ目標追蹤模擬結果(演算法三) .......

...33 圖5.4 ㄧ目標追蹤位置及速度誤差(演算法ㄧ) ........33 圖5.5 ㄧ目標追蹤位置及速度誤差(演算法二) .

.......34 圖5.6 ㄧ目標追蹤位置及速度誤差(演算法三) ........34 圖5.7 二目標追蹤模擬結果(演算法ㄧ)

...........35 圖5.8 二目標追蹤模擬結果(演算法二) ...........35 圖5.9 二目標追蹤模擬結果(

演算法三) ..........36 圖5.10 二目標追蹤位置及速度誤差(演算法ㄧ) .......36 圖5.11 二目標追蹤位 置及速度誤差(演算法二) .......37 圖5.12 二目標追蹤位置及速度誤差(演算法三) .......37 圖5.13 四目標 追蹤模擬結果(演算法ㄧ) ..........38 圖5.14 四目標追蹤模擬結果(演算法二) ..........38 圖5.15 四目標追蹤模擬結果(演算法三) ..........39 圖5.16 四目標追蹤位置及速度誤差(演算法ㄧ) .......39 圖5.17 四目標追蹤位置及速度誤差(演算法二) .......40 圖5.18 四目標追蹤位置及速度誤差(演算法三) .....

..40 表目錄 表5.1 追蹤目標初始值..................41 表5.2 目標變速度設定表.......

..........41 表5.3 ㄧ目標追蹤模擬結果(演算法ㄧ) ...........41 表5.4 ㄧ目標追蹤模擬結果(演 算法二) ...........41 表5.5 ㄧ目標追蹤模擬結果(演算法三) ...........42 表5.6 二目標追蹤模

(2)

擬結果(演算法ㄧ) ...........42 表5.7 二目標追蹤模擬結果(演算法二) ...........42 表5.8 二目 標追蹤模擬結果(演算法三) ...........42 表5.9 四目標追蹤模擬結果(演算法ㄧ) ...........43 表5.10 四目標追蹤模擬結果(演算法二) ..........43 表5.11 四目標追蹤模擬結果(演算法三) ........

..43 參考文獻

[1]Y. Bar-Shalom and T.E. Fortmann, “Tracking and Data Association,” Academic Press, INC. 1989.

[2]K. Mehrotra and P.R. Mahapatra, “A Jerk Model for Tracking Highly Maneuvering Targets,” IEEE Trans. Aerosp. Electron. Syst., Vol.

AES-33, pp. 1094-1106, 1997.

[3]H. Lee and I.J. Tahk, “Generalized Input-Estimation Technique for Tracking Maneuvering Targets,” IEEE Trans. Aerosp. Electron. Syst., Vol. AES-35, pp. 1388-1403, 1999.

[4]M. R. Morelande and S. Challa, “Manoeuvering Target Tracking in Clutter using Particle Filters,” IEEE Trans. Aerosp. Electron. Syst., Vol.

AES-41, pp.252-270, 2005.

[5]A. Howard and H. Seraji, “Multi-Sensor Terrain Classification for Safe Spacecraft Landing,” IEEE Trans. Aerosp. Electron. Syst., Vol. 40, No.4, pp. 1122-1131, 2004.

[6]R.E. Bethel and G.J. Paras, “A PDF Multisensor Multitarget Tracker,” IEEE Trans. Aerosp. Electron. Syst., Vol. AES-34, pp.153-169, 1998.

[7]M. Kalandros and L.Y. Pao, “Multisensor Covariance Control Strategies for Reducing Bias Effects in Interacting Target Scenarios,” IEEE Trans. Aerosp. Electron. Syst., Vol. AES-41, pp. 153-172, 2005.

[8]P. Swerling, “Radar Probability of Detection for Some Additional Fluctuating Target Cases,” IEEE Trans. Aerosp. Electron. Syst., Vol.

AES-33, pp. 698-709, 1997.

[9]P.D. Hanlon and P.S. Maybeck, “Interrelation Ship of Single-Filter and Multiple-Model Adaptive Algorithms,” IEEE Trans. Aerosp.

Electron. Syst., Vol. AES-34, pp. 934-946, 1998.

[10]E. Conte, M. Lops, and G. Ricci, “Adaptive Detection Schemes in Compound-Gaussian Clutter,” IEEE Trans. Aerosp. Electron. Syst., Vol.

AES-34, pp. 1058-1069, 1998.

[11]D.J. Kershaw and R.J. Evans, “Waveform Selective Probabilistic Data Association,” IEEE Trans. Aerosp. Electron. Syst., Vol. AES-33, pp.1180-1189, 1997.

[12]P.D. Hanlon and P.S. Maybeck, “Interrelationship of Single-Filter and Multiple-Model Adaptive Algorithms,” IEEE Trans. Aerosp.

Electron. Syst., Vol. AES-34, pp. 934-947, 1998.

[13]S.T. Park and J.G. Lee, “Design of a Practical Tracking Algorithm with Radar Measurements,” IEEE Trans. Aerosp. Electron. Syst., Vol.

AES-34, pp. 1337-1345, 1998.

[14]E. Mazor, J. Dayan, A. Averbuch and Y. Bar-Shalom, “Interacting Multiple Model Methods in Target Tracking: A Survey,” IEEE Trans.

Aerosp. Electron. Syst., Vol. AES-34, pp. 103-124, 1998.

[15]Weixian Liu, Yilong Lu and J.S. Fu, “Data fusion of multi-radar system by using genetic algorithm”, Aerospace and Electronic Systems, IEEE Transactions on, Vol. 38, Issue: 2, April 2002.

[16]Martin Janik, Eva Miklovicova, and Marian Mrosko, “Predictive Control of Nonlinear Systems,” ICIC Express Letters, Volume 2, Issue 3, September 2008, pp.239-244.

[17]N. Karim Kemih, Malek Benslama Merabtine, and Filali Salim, “Generalized Predictive Control Using Conjugate Gradient Method Applied,” ICIC Express Letters, Volume 1, Issue 2, December 2007, 99-104.

[18]Yi-Nung Chung, Dend-Jyi Juang, Tsung-Chun Hsu, Chi-Hsian Chang, Maw-Rong Yang, and Shun-Peng Hsu, 2008,“An Extended Multiple-Model Estimator Radar Maneuvering Target Tracking,” Journal of Aeronautics Astronautics and Aviation Series A, Vol. 40, No. 2, pp.

99-104.

[19]D. McErlean, and S. Narayanan, ”Distributed detection and tracking in sensor networks,” Signals, Systems and Computers, 2002.

Conference Record of the Thirty-Sixth Asilomar Conference on, Vol. 2, 3-6 Nov. 2002.

[20]N. Mort, and P. Prajitno, “A multi-sensor data fusion-based target tracking system,” Industrial Technology, 2002. IEEE ICIT '02. 2002 IEEE International Conference on, Vol. 1, 11-14 Dec. 2002.

[21]X.B. Jin and Y.X. Sun, “Optimal estimation for multi-sensor data fusion system with correlated measurement noise,” Signal Processing, 2002 6th International Conference on, Vol. 2, 26-30 Aug. 2002.

[22]K. A. Fisher and P. S. Maybeck, “Multiple Adaptive Estimation with Filter Spawning,” IEEE Trans. Aerosp. Electron. Syst,. Vol. 38, No.3, pp. 755-768, 2002.

[23]P.C. Chung, C.T. Tsai, E.L. Chen, and Y.N. Sun, ”Olygonal Approximation Using A Competitive Hopfield Neural Network,”, Pattern Recognition, Vol. 27, No. 11, pp. 1505-1512, 1994.

[24]S. Kumar, ”Neural Networks: A Classroom Approach,” Mc Graw Hill, 2005.

(3)

[25]A.F. James and M.S. David, ”Neural Networks: Algorithms, Applications, and Programming Techniques,” Addison Wesley, 1991.

[26]K.C. Chang, C.Y. Chong, and Y. Bar-Shalom, ”Joint Probabilistic Data and Association Distributed Sensor Networks,” IEEE Trans.

Automa. Contr., Vol. AC-31, pp. 889-897, 1989 [27]E. Emre and J. Seo, ”A Unifying Approach to Multi-Target Tracking,” IEEE Trans.

Aerosp. Electron. Syst., Vol. 25, pp. 520-528, 1989.

[28]N. Okello and B. Ristic, ”Maximum Likelihood Registration for Multiple Dissimilar Sensors,” IEEE Trans. Aerosp. Electron. Syst., Vol. 39, No. 3, pp. 1074-1083, 2003.

[29]S.S. Blackman, ”Multiple Hypothesis Tracking for Multiple Target Tracking,” IEEE Aerosp. Electron. Syst. Magazine., Vol. 19, pp. 5-18, 2004.

[30]C. Hue, J.P. Le Cadre, and P. Perez, ”Sequential Monte Carlo methods for multiple target tracking and data fusion,” IEEE Trans. on Signal Processing, Vol. 50, pp. 309-325, 2002.

[31]D. Sengupta and R.A. Iltis, ”Neural Solution to the Multitarget Tracking Data Association Problem,” IEEE Trans. Aerosp. Electron. Syst., Vol. 25, pp. 86-108, 1989.

[32]B. Zhou and N.K. Bose, ”A Comprehensive Analysis of Neural Solution to the Multi-target Tracking Data Association Problem,” IEEE Trans. Aerosp. Electron. Syst., Vol. 29, pp. 260-263, 1993.

[33]Yi-Nung Chung, Pao-Hua Chou, and Maw-Rong Yang, 2007, “Mulitple-Target Tracking with Competitive Hopfield Neural Network-based Data Association”, IEEE Trans. Aerosp. Electron. Syst. Vol. 43, No. 3, pp. 1180-1188.

[34]Yi-Nung Chung, Hsin-Ta Chen, Pao-Hua Chou, and Maw-Rong Yang, 2007, “An Improved Estimator Using Multiple Sensor Data Fusion for Radar Maneuvering Target Tracking Systems”, Journal of The Chinese Institute of Engineers, Vol. 30, No. 2, pp. 203-210.

[35]Chengjian Lin, Yongcheng Liu, and Chiyung Lee, “An Efficient Neural Fuzzy Network Based on Immune Particle Swarm Optimization for Prediction and Control Applications,” International Journal of Innovative Computing, Information, and Control, Vol. 4, No. 7, 2008, pp.

1171-1172.

[36]Hiroki Tamura and Koichi Tanno, “Midpoint-Validation Method of Neural Networks for Pattern Classification Problems,” International Journal of Innovative Computing, Information, and Control, Vol. 4, No. 10, 2008, pp. 2475-2482.

參考文獻

相關文件

[13] Chun-Yi Wang, Chi-Chung Lee and Ming-Cheng Lee, “An Enhanced Dynamic Framed Slotted ALOHA Anti-Collision Method for Mobile RFID Tag Identification,” Journal

時值知識經濟時代的來臨,台灣已加入了 WTO ( World Trade Organization,WTO ),企業面臨劇變之環境及廣闊的物料採購市 場,若能善用「知識管理」( Knowledge

由於醫療業導入 ISO 9000 品保系統的「資歷」相當資淺,僅有 三年多的年資 11 ,因此,對於 ISO 9000 品保系統應用於醫療業之相關 研究實在少之又少,本研究嘗試以通過

Sharma (1999), “An Intergrated Machine Vision Based System for Solving the Non-Covex Cutting Stock Problem Using Genetic Algorithms,” Journal of Manufacturing Systems, Vol..

Menz-Ru Huang, Ruey-Gwo Chung, Tung-Shou Chen, Hsuan-Yi Cheng, Yung-Ching Lin (2007), “An Analysis of Government Subsidies Enterprise Training Based On CDC Algorithm,”

and Feng-Tsai Lin, “Analysis of the Transient Ground Surface Displacements Subject to a Point Sink in a Poroelastic Half Space,” Chung Hua Journal of Science and Engineering,

Chang, Shih -Chia, Yang, Chen-Lung , and Sheu, Chwen, “Manufacturing Flexibility and Business Strategy: An Empirical Study of Small and Medium Sizes Firms,” International

Chen, 2008, “Advanced Process Control of Metal Sputter Deposition Using a Time Series Analysis,” International Journal of Advanced Manufacturing Technology, 36(5), pp. Lee, 2007,