臺灣博碩士論文加值系統

進行交通管制與管理策略的過程為利用即時的流量、車速等交通資訊來掌握道路使用狀況，使交通管理人員能針對交通情形來進行管制或策略規劃。為了獲得即時的交通資訊需要透過車輛偵測器進行道路監測，由偵測器所蒐集到的資訊，透過交通參數演算法轉換成有意義的交通資訊。
本研究以FMCW雷達偵測器得到的訊號作為研究資料來源，探討在車輛通過雷達偵測範圍時，如何把車輛對應之訊號予以偵測出來。在系統能準確判斷有無車後，才能針對後續交通參數(車速、道路擁擠度等)進行開發，因此車輛偵測在交通資訊中為一個重要的參數。
研究方法上，利用車輛的雷達回波特性與雜訊不同，來找出車輛通過的訊號特徵。以往研究對於車輛偵測方法大多以車輛反射的訊號能量來設定門檻，本研究在車輛偵測方法上採用以譜熵計算訊號能量，決定門檻設定與更新機制，並透過端點檢測法來進行車輛訊號偵測。
為了探討在不同信噪度下，偵測方法的準確度，加入了訊號模擬模式，以本研究方法與能量門檻法分別針對不同的信噪度進行辨識率測試。最後利用雷達偵測器實際蒐集的資料進行研究，驗證本研究方法是否有更好的車輛偵測結果。而根據模擬訊號實驗與雷達訊號分析的結果，本研究提出的譜熵門檻法，其車輛訊號辨識率皆比傳統能量門檻法好。

中文摘要.........................................................................................................................i
英文摘要........................................................................................................................ii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 研究內容 4
第二章 文獻回顧 6
2.1微波雷達偵測演算法相關文獻 6
2.2 端點檢測法相關文獻 9
2.3 資訊熵相關文獻 15
2.3.1 分帶譜熵 (Band-Partitioning Spectral Entropy, BSE) 16
2.3.2適應分帶譜熵(Adaptive Band-Partitioning Spectral Entropy, ABSE) 18
2.4 小結 19
第三章 雷達偵測器 20
3.1 雷達偵測器簡介 20
3.2 雷達偵測器硬體架構 20
3.3 雷達偵測器偵測原理 21
第四章 端點檢測譜熵分析模式 25
4.1 端點檢測簡介 25
4.2 端點檢測法參數設定 26
4.3 門檻設定與更新機制 28
4.4 車輛訊號的端點檢測模式 32
4.5 車輛訊號處理 34
第五章 模式驗證 38
5.1 雷達訊號模擬 38
5.2 模擬結果 41
5.3 實例分析 43
5.3.1 偵測環境介紹 43
5.3.2偵測器軟體 44
5.4 實例分析結果 46
第六章 結論與建議 50
6.1 結論 50
6.2 建議 50
參考文獻 52

[1] Bullington, K. and J. M., Fraser, "Engineering aspects of TASI", Bell System Technical Journal, pp.353-364, March 1959.
[2] EIS Inc., "RTMS User Manual", EIS Electronic Integrated Systems Inc., 2002.
[3] Fast , J. D.," Entropy, the significance of the concept of entropy and its application in science and technology", Macmillan,1982.
[4] Genderen, P., "Feature based plot classification using a Bayes algorithm", Proceedings of the 3rd European Radar Conference, pp. 41-44, 2006.
[5] Guo, Q., N. Li, and G. Ji, "A improved dual-threshold speech endpoint detection algorithm", ICCAE Computer and Automation Engineering, 2010.
[6] Haigh, J. A. and J. S. Mason, "Robust voice activity detection using cepstral features", IEEE Computer, Communication, Control and Power Engineering conference, 1993.
[7] Hussain, A., S.A. Samad and L.B. Fath, "Endpoint detection of speech signal using Neural Network", IEEE Trans on ASSP, pp.271-274, 2000.
[8] Jaynes , E.T., Information Theory and Statistical Mechanics II, Physical review, 1957.
[9] Jou, Y.J. and Y.K. Chen, "The online vehicle type classifier design for road-side radar detectors", AIP Conference Proceedings, vol. 1148, Issue 1, pp.462-465, 2009.
[10] Junqua, J.C., M. Brian and R. Ben, "A robust algorithm for word boundary detection in the presence of noise", IEEE Transactions on Speech and Audio Processing, vol. 2, no. 3, 1994.
[11] Lamel, F., R. Rabiner and E. Rosenberg, "An improved endpoint detector for isolated word recognition", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 29, no. 4, 1981.
[12] Leduc, G., "Road traffic data collection methods and application", Working papers on Energy, Transport and Climate Change N.1, 2008.
[13] Leung, S. W. and J. W. Minett, "A fuzzy shadow feature scheme for radar signal detection", International Conference of Information, Communication and Signal Processing, vol. 3, pp.1386-1388, 1997.
[14] Li, Y., Y. Fan, "A novel algorithm to robust speech endpoint detection in noisy environments", IEEE Industrial Electronics and Application, 2007.
[15] Li , Q., J. Zheng and A. Tsai, "Robust endpoint detection and energy normalization for real-time speech and speaker recognition," IEEE Transactions on speech and audio processing, vol.10, No.6,March 2002.
[16] Lin, C.T., G.D. Wu, "A robust word boundary detection algorithm for variable noise-level environment in cars", IEEE Transactions on Intelligent Transportation Systems, vol.3, No.1, March 2002.
[17] Malboubi, M., J. Akhlaghi, and M. Saraf, " The intelligent identification of air and sea targets in coastal radars", Proceedings of the 3rd European Radar Conference, pp. 17-20, 2006.
[18] McClellan , J. H., R. W. Schafer and M. A. Yoder, Signal Processing First, Pearson Prentice Hall, 2003.
[19] Musch, T., "A high precision 24 GHz FMCW-radar using a phase-slope signal processing algorithm", Proceedings of the 32rd European Microwave Conference, pp. 1-4,2002.
[20] Ney, H., "An optimization algorithm for determining the endpoints of isolated utterances", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 6, pp.720-723, 1981.
[21] Rabiner, R. and R. Sambur, "Voiced-unvoiced-silence detection using the Itakura LPC distance measure", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 2, pp.323-326, 1997.
[22] Shannon, C.E., "A mathematical theory of communication", University of Illinois Press, Urbana, 1949.
[23] Shen, J.L., J.W. Hung and L.S. Lee, "Robust entropy-based endpoint detection for speech recognition in noisy environments", presented at the ICSLP, 1998.
[24] Stove, A.G., "Linear FMCW radar techniques", IEEE Proceedings of Radar and Signal Processing, vol. 139, no. 5, pp. 343-350, 1992.
[25] Wu, B.F. and K. C. Wang, "Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments", IEEE Transactions on speech and audio processing, vol.13, No.5,March 2005.
[26] Wu , G.D. and C.T. Lin, "Word boundary detection with mel-scale frequency bank in noise environment", IEEE Transactions on Speech and Audio Processing, vol. 8, no. 5, 2000.
[27] Zhang, H., W. Yu and X. Sun, "A novel method for background suppression in millimeter-wave traffic radar sensor", Proceeding of the 11th International IEEE Conference in Intelligent Transportation Systems, pp. 699-704, 2008.
[28] Zhang, S., J. Fan, L. Shou and J. Dong, "A detection method of radar signal by wavelet transforms", Fuzzy Systems and Knowledge Discovery Forth International Conference, vol. 2, pp.710-714, 2007.
[29] 交通部統計處 - 97年重要交通統計指標分析，2009。
[30] 林大傑，車輛偵測器不完整資訊推估旅行時間之研究，逢甲大學交通工程與管理所，碩士論文，民96。
[31] 卓訓榮等，「自動化路況資訊偵測系統研發與示範(一)」，交通部運輸研究所委託，民96。