應用於FMCW雷達偵測器之高階統計和小波估計降雜訊研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：3.149.214.32

姓名

劉恆瑞(Heng-Rui Liu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

應用於FMCW雷達偵測器之高階統計和小波估計降雜訊研究
(Novel Denoising Techniques Assisted from Higher-Order Statistics and Wavelet Estimation in FMCW Radar Detector)

相關論文

★ 運用SIFT特徵進行光學影像目標識別	★ 語音關鍵詞辨識擷取系統
★ 適用於筆記型電腦之WiMAX天線研究	★ 應用於凱氏天線X頻段之低雜訊放大器設計
★ 適用於802.11a/b/g WLAN USB dongle曲折型單極天線設計改良	★ 應用於行動裝置上的雙頻(GPS/BT)天線
★ SDH設備單體潛伏性障礙效能分析與維運技術	★ 無風扇嵌入式觸控液晶平板系統小型化之設計
★ 自動化RFID海關通關系統設計	★ 發展軟體演算實現線性調頻連續波雷達測距系統之設計
★ 近場通訊之智慧倉儲管理	★ 在Android 平台上實現NFC 室內定位
★ Android應用程式開發之電子化設備巡檢	★ 鏈路預算估測預期台灣衛星通訊的發展
★ 在中上衰落通道中分集結合技術之二階統計特性	★ 先進長程演進系統中載波聚合技術的初始同步

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限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。

摘要(中)

調頻連續波雷達FMCW (Frequency Modulated Continuous Wave Radar)已廣泛的被運用在汽車雷達系統中，由於其訊號相互之間的干擾會使得無法有效的偵測出真實目標，因此對於目標物的偵測是很重要的。而在雷達訊號偵測中，由於各種干擾強度是隨機產生的，如果使用傳統的設計採用固定誤警率CFAR(Constant False-Alarm Rate)算法進行偵測，在這種情況下固定臨界值的方式難以保證其偵測效能，會受到多目標物的環境與雜波(clutter)的影響。
因此本論文研究將使用到高階統計(Higher-Order Statistics)的特性，以及使用改善的小波估計(Wavelet Estimation)，把蒐集到的包含真實訊號與雜訊訊號的數據透過連續小波轉換進行降雜訊來調整小波臨界值，找到合適的訊號屬性參數，將雜訊從訊號中分離出來，過過模擬去解決多點雜訊能量問題，達到能夠有效的分辨出偵測目標與降雜訊的效果。

摘要(英)

The Frequency Modulated Continuous Wave (FMCW) Radar has been widely used in automotive radar systems, since the interference between their signals will make it impossible to effectively detection the real target, it is very important to detect the target. In radar signal detection, since various interference intensity are randomly generated, if the traditional design uses the CFAR algorithm for detection, in this case, the method of constant threshold value is difficult to ensure its detection performance, and it will be affected by the environment and clutter of multi-target objects.
In this theis, we use the characteristics of HOS and the use of improved wavelet estimation to reduce the noise of the collected data including real signal and noise signal through continuous wavelet transform to adjust the wavelet threshold, find the appropriate signal attribute parameters, separate the noise from the signal, and solve the problem of multi-point noise energy through simulation, so as to effectively distinguish the detection target and denoise .

關鍵字(中)

★ 調頻連續波
★ 啁啾訊號波形
★ 連續小波轉換
★ 高階統計
★ 降雜訊

關鍵字(英)

★ FMCW
★ Chirp
★ Continuous Wavelet Transform(CWT)
★ Higher Order Statistics(HOS)
★ Denoising

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 論文大綱 3
第二章 FMCW系統架構 4
2.1 系統描述 4
2.2 FMCW雷達原理 6
2.3 雷達方程式 7
2.4 Chirp波形介紹 8
第三章 CFAR固定誤警率偵測法 10
3.1 CFAR介紹 10
3.2 CFAR算法 11
3.2.1 CA-CFAR 16
3.2.2 GO-CFAR 19
3.2.3 OS-CFAR 20
3.2.4 SO-CFAR 22
3.3 小結 23
3.3.1 CA-CFAR算法模擬 24
3.3.2 GO-CFAR算法模擬 25
3.3.3 OS-CFAR算法模擬 26
3.3.4 SO-CFAR算法模擬 27
第四章小波估測分析 29
4.1 系統與通道模型理論背景 30
4.2 連續小波轉換法 31
4.3 小波估測法 32
4.4 降雜訊演算法處理 34
第五章研究方法與模擬結果 36
5.1 超聲波雷達偵測器實測 36
5.2 模擬結果與討論 41
第六章結論與未來展望 51
參考文獻 52

參考文獻

[1] A. Maio, F. Gini, and L. Patton, Waveform Design and Diversity for Advanced Radar Systems, 1st ed., IET Radar and Sonar Navigation, 2011.
[2] F. Folster, H. Rohling, and H. Lubbert, “An automotive radar systems and network based on 77GHz FMCW sensors”, in Proc. of IEEE Int. Radar Conf., 2005, pp. 871–876.
[3] H. Rohling and E. Lissel, “77 GHz Radar Sensor for Car Application,” Proceedings of the International Radar Conference, pp.373-379, May. 1995.
[4] D.D. Li, S.C. Luo, C. Pero, X. Wu, and R.M. Knox, “Millimeter-wave FMCW/monopulse radar front-end for automotive applications,” IEEE MTT-S International Microwave Symposium Digest., pp.277-280, June. 1999.
[5] A. G. Stove, “Linear FMCW radar techniques,” IEE Proc. F, vol. 139, no. 5, pp. 343-350, Oct. 1992.
[6] H. Rohling, “Radar CFAR thresholding in clutter and multiple target situations,” IEEE Trans. Aerosp. Electron. Syst., vol. AES–19, no. 4, pp. 608–621, 1983.
[7] Yusuke Kitsukawa, Masashi Mitsumoto, Hiroyuki Mizutani, Noriyuki Fukui and Chiharu Miyazaki Mitsubishi Electric Corporation, “An Interference Suppression Method by Transmission Chirp Waveform with Random Repetition Interval in Fast-Chirp FMCW Radar”, Oct 2019.
[8] Dipl.-Ing. (FH) Christian Wolff, Frequency Modulated Continuous Wave Radar,2018.
[9] Mostafa Alizadeh, FMCW Radar System, Jul , 2019.
[10] Patrick Flandrin “Time-frequency and chirps”, Ecole Normale Sup′erieure de Lyon, Laboratoire de Physique (Umr 5672 Cnrs),46 all′ee d′Italie, 69364 Lyon Cedex 07, France.
[11] Ivo Bizon Franco de Almeida, Marwa Chafii , Ahmad Nimr and Gerhard Fettweis, “Alternative Chirp Spread Spectrum Techniques for LPWANs”,Jun.2021.
[12] Ana Belen Martinez, Atul Kumar, Marwa Chafii, Gerhard Fettweis, “A Chirp-Based Frequency Synchronization Approach for Flat Fading Channels”,Apr.2021.
[13] G. M Hatem , J. W Abdul Sadah , T. R. Saeedc G. M Hatema , J. W Abdul Sadahb , T. R. Saeed, “Comparative Study of Various CFAR Algorithms for NonHomogenous Environments”, IOP Conf. Ser.: Mater. Sci. Eng. 433 012080, 2018.
[14] Saeed, T.; Hatem, G.; Abdul Sadah, J.; Ziboon, H. “Design and Implementation of a New CFAR Based on Weighted and Statistical Algorithms”. Preprints 2020, 2020030397 (doi: 10.20944/preprints202003.0397.v1).
[15] P. P. Gandhi and S. A. Kassam, "Analysis of CFAR processors in nonhomogeneous background," in IEEE Transactions on Aerospace and Electronic Systems, vol. 24, no. 4, pp. 427-445, July. 1988, doi: 10.1109/7.7185.
[16] Rajan Srinivasan, “Importance Sampling Applications in Communications and Detection”, Springer-Verlag Berlin Heidelberg 2002.
[17] M. A. Richards, J. A. Scheer, and W. A. Holm, “Principles of Modern Radar”, 1st ed. SciTech Publishing, 2010.
[18] M. A. Richards, “Fundamentals of Radar Signal Processing”, 2nd ed.McGraw-Hill, 2014.
[19] A. Jalil, H. Yousaf and M. I. Baig, "Analysis of CFAR techniques," 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, 2016, pp. 654-659.
[20] Zhen Jia, “Multi-target CFAR Detection of a Digital Phased Array Radar System”, 2019 J. Phys.: Conf. Ser. 1314 012011.
[21] Jen J, 2011 A study of CFAR Implementation Cost and Performance Tradeoffs in Heterogeneous Environments, Thesis, California State Polytechnic University, Pomona.
[22] Vincent Y Li F and Miller K 2009 Target Detection in Radar: Current Status and Future Possibilities the Journal of Navigation 50 pp 303-313.
[23] Moder Safir Shabit et al 2012 CFAR Detectors Employed by Radar Sensor Systems 12th International Conference on Control, Automation and Systems.
[24] Dejan Ivkovic, Milenko Andrić, B.M. Zrnic “Detection of Very Close Targets by Fusion CFAR Detectors “January 2016,Scientific Technical Review 66(3):50-57.
[25] Lei Zhao, Wexian Liu, Xin Wu and Jeffrey S Fu, “A Novel Approach for CFAR Processor Design”, 2001 IEEE Radar Conference, pp. 284-288.
[26] César Torres-Huitzil, Rene Cumplido-Parra, Santos López-Estrada, “Design and Implementation of a CFAR Processor for Target Detection”, Computer Science Department, INAOE, Apdo. Postal 51 & 216 Tonantzintla, Puebla, México,2004.
[27] A. Melebari, A. Melebari, W. Alomar, M. Y. A. Gaffar, R. De Wind and J. Cilliers, "The effect of windowing on the performance of the CA-CFAR and OS-CFAR algorithms," 2015 IEEE Radar Conference, Johannesburg, 2015, pp. 249-254.
[28] Hansen, V.G and Sawyers, J 1980 Delectability Loss Due to ′Greatest Of Selection in a CellAveraging CFAR IEEE Transactions on Aerospace and Electronic Systems 16 pp 115-118.
[29] M. Sahal, Z. A. Said, R. Y. Putra, R. E. Abdul Kadir and A. A. Firmansyah, "Comparison of CFAR Methods on Multiple Targets in Sea Clutter Using SPX-Radar-Simulator," 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2020, pp. 260-265, doi: 10.1109/ISITIA49792.2020.9163697.
[30] Herrera, R. H., J. B. Tary, M. van der Baan, and D. W. Eaton, 2015,Body wave separation in the time-frequency domain: IEEE Geoscience and Remote Sensing Letters, 12, 364–368, doi: 10.1109/LGRS.2014.2342033.
[31] Gabor, D., 1946, Theory of communication. Part 1: The analysis of information:
Journal of the Institution of Electrical Engineers — Part III: Radio and Communication Engineering, 93, 429–441, doi: 10.1049/ji-3-2.1946.0074.
[32] I. Daubechies, J. Lu, and H.-T. Wu, “Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool,” Appl. Comput.Harmon. Anal., vol. 30, no. 2, pp. 243–261, Mar. 2011.
[33] I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA, USA: SIAM, 1992, ser. CBMS-NSF Regional Conference Series in Applied Mathematics.
[34] S. Mostafa Mousavi and Charles A. Langston, Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block Thresholding. Bulletin of the Seismological Society of America, Vol. 106, No. 4, pp. 1380–1393, August. 2016, doi: 10.1785/0120150345.
[35] S. Mostafa Mousavi and Charles A. Langston, Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data, March 2017Geophysics 82(4):1-58,
DOI:10.1190/geo2016-0433.1.
[36] DeCarlo, L. T. (1997). On the meaning and use of kurtosis, Psychol. Meth. 2,
292–307.
[37] Tsolis, G., and T. Xenos (2011). Signal denoising using empirical mode
decomposition and higher order statistics, Int. J. Signal Process. Image Process. Pattern Recogn. 4, 91–106.
[38] G. Thakur, E. Brevdo, N. S. Fuckar, and H.-T. Wu, “The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications,” Signal Process., vol. 93, no. 5, pp. 1079–1094, May. 2013.
[39] D. L. Donoho, "De-noising by soft-thresholding," in IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627, May 1995, doi: 10.1109/18.382009.

指導教授

林嘉慶(Jia-Chin Lin)

審核日期

2022-7-21

推文