 |
|
|
|
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：8 、訪客IP：3.137.199.162
姓名 郭進民(Jin-Min Kuo) 查詢紙本館藏 畢業系所 太空科學研究所 論文名稱 合成孔徑雷達之移動目標物速度估測研究
(The estimator for Estimating Target Velocity with SAR)相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
- 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) 移動目標物會改變合成孔徑雷達(Synthetic aperture radar, SAR)回波係數，造成移動目標物影像模糊及成像位置偏移。所以如能估測回波係數或移動目標物速度，就得以正確補償回波訊號相位變化，提昇影像品質。本文將分成兩個主題，一是直接由雷達回波訊號估測其地物速度，另一是由船跡影像估測船速。
合成孔徑雷達訊號是一種都卜勒訊號(doppler signals), 其系數為速度的函數，雖可藉由直接微分法所得之相位差，推得目標物速度，但此法也將使得雜訊變異量(variance)快速增加。主題一討論利用有限微分(finite difference)法壓抑雜訊變異量，使得在較高雜訊比且取樣數量不大於延遲函數(delay parameters)時仍可高於Cramer-Rao Lower bounds (CRLB)的標準，但都卜勒系數的可測範圍也將減小，文中另提出相位展開法(phase unwrapping)克服此缺點。本法只有一維的加減與平均等運算也不需初始值，相較於其它方法，本法的運算量較少，且在資料量相對較少時有較好的抗雜訊效能。
移動船隻在SAR影像中會產生多尺度的V形尾跡，其隱含船速、船向及船隻尺寸等資訊。但SAR影像往往伴隨有高雜訊，使其資訊不易取出。主題二提出以小波轉換(Wavelet transform)結合Radon轉換的混合方法，增強船跡邊緣以利粹取開角資訊。本法首先藉由小波轉換產生一組多尺度影像，每一尺度影像均包括垂直、水平及對角等三張小波轉換之正交基底的高通系數影像，再藉由空間相交法(spatial correlator)累乘不同尺度影像，減抑雜訊、增強V形尾跡邊緣，此有利於V形尾跡邊緣的偵獲。第二步驟則藉由Radon轉換法估測V形尾跡開角。相較於僅直接使用Radon轉換法估測V形尾跡開角，本法可更有效減抑雜訊的影響。摘要(英) A moving target will change the coefficients of the synthetic aperture radar (SAR) chirp signals. Vice versa, the velocity could be inferred from the SAR images or signals. This paper proposes two algorithms for estimating a moving target’s velocity in the SAR image or signal. Topic-1 describes the velocity estimation of a moving target in the SAR signals; Topic-2 describes that in the SAR images.
Topic-1 describes a new approach for estimating the Doppler coefficients from a finite number of noisy discrete-time observations, which are functions of the speed variation of target/radar. The approach adopts the finite difference method to estimate the chirp signal coefficients. It is motivated with the concept of the HAF. But the finite difference method directly calculates the phase of the signal. The finite difference method, with respect to the HAF, replaces the correlation operations with addition and average operations. That reduces the computation load. A closed-form expression is derived that describes the relationship between the phase differences and the chirp signal parameters. The difference method could derive the phase differences, but cause the deterioration of the signal variance. The finite difference method is a good way to reduce the noise deviations, but the measurable spans will become smaller. The measurable span could be improved by adopting a phase unwrapping method, as proposed in Topic-1. Unwrapping the phase differences could recover the chirp signal coefficients from bias estimates. The maximum measurable span of the coefficients will be significantly larger. The statistical analysis for the finite difference estimation proves that the variance could attain the Cramer-Rao lower bounds in higher SNR. In conclusion, our algorithm can reduce the computational complexity and remove the effect of the signal amplitude variation.
A moving ship on SAR image produces multiscale wake with a characteristic linear V-shaped pattern. Detection of the wake can provide substantial information about the ship, such as its size, direction and speed of movement. In general, ship-generated wakes in SAR images are associated with high sea clutter, which causes the deterioration of detection performance. Topic-2 presents a hybrid method that combines the wavelet technique and the Radon transforms technique to detect the ship wake. The wavelet technique is first applied to generate a set of multiscale images. An orthogonal basis function is adopted so that three high-pass images in horizontal, vertical and diagonal direction are generated for each resolution scale. Then a spatial correlator is applied among the moduli of different scale, where modulus images are formed from three high-pass images. The output of the correlation process is shown to be highly representative at ship wake edges. Comparisons with other methods indicate the superior performance of the present approach in that not only are the wakes detected but also the V-shaped pattern is well pre-served. The second stage of the method involves the application of the Radon transform technique to estimate the V-opening angle from the detected ship wakes. Compared with a direct Radon transform, the greater effectiveness of the proposed scheme is demonstrated in a terms of efficiency as well as reliability for ship wake detection in noisy backgrounds.關鍵字(中) ★ 小波轉換
★ 船跡
★ 相位展開法
★ 有限微分
★ 都卜勒系數
★ 速度估測骯
★ 合成孔徑雷達
★ 空間相交法關鍵字(英) ★ finite difference
★ doppler coefficients
★ Synthetic aperture radar
★ SAR
★ phas論文目次 Chapter 1 Introduction………………………………………………………….….1
1.1 Estimator in SAR signals……………………………………………….……1
1.2 Estimator in SAR images…………………………………………….……...3
1.3 Organization of the dissertation………………………………………..……4
Chapter 2 Synthetic aperture radar…………………………………………….…...8
2.1 Synthetic aperture…………………………………………………….……..8
2.1.1 Concept of synthetic aperture…………….…………………………….8
2.1.2 Target/radar range variation…………….………………………...…….9
2.2 Synthetic aperture radar processing……………...…………………………12
2.2.1 Matched filter……………...…………………………………………..12
2.2.2 Range compression……………...…………………………………….15
2.2.3 Azimuth compression…………………………………………...…….16
Chapter 3 Moving target velocity estimation in SAR signals………………...…..18
3.1 Estimation method for chirp signal coefficients ………… …………….….18
3.1.1 Finite difference estimation……… ……………………………….…..18
3.1.2 Enlarge measurable span……………………………….……….….….26
3.1.2.1 Two- delay parameters finite difference……… …………….……26
3.1.2.2 Phase unwrapping………………………………………………....27
3.2 Measurable span of the velocity……………………………………………28
3.3 Statistical analysis of finite difference algorithm…… ……………..…...…31
3.3.1 High SNR approximation…………………..…..……………...………31
3.3.2 Mean square error of finite difference algorithm…………… ………..33
3.4 Simulations……… …..…………………………………………………….38
Chapter 4 Moving target velocity estimation in SAR images…………………….47
4.1 Ship Wake System…………………………………………..……………..47
4.2 Ship Wake Detection via the Wavelet Technique………………..………..49
4.2.1 Basics of Wavelet Transform……………………………..…….……..49
4.2.2 Spatial correlator based on modulus…………………..…..……….….51
4.2.3 Detection Results: Simulated images………………………………….52
4.3 Estimate the V-opening angle………………………………………….…..65
4.3.1 Radon Transform Technique as an Angle Estimator………………….65
4.3.2 Simulation results………………………………………………….…..67
4.3.3 Real SAR image test……………………………………………….….71
Chapter 5 Conclusions and Further research...................………………….……..73
5.1 Estimator in SAR signals...........………………….………………………..73
5.1.1 Summary and Main Contribution...........………………….…………...73
5.1.2 Suggestion for Future Research...........………………….…………….73
5.2 Estimation Method in SAR images...........………………….……….……..74
References ………………………………............………………….……….……..75參考文獻 [1] Raney, R.K.; 1971, “Synthetic aperture imaging radar and moving target,” IEEE Trans. Aerospace and Electronic Systems, Vol. AES-7, pp. 499-505, 1971.
[2] Freeman, A.; and Currie A., “Synthetic aperture radar (SAR) images of moving targets,” GEC J. Res., vol. 5, no.2, pp. 106-115, 1987.
[3] Werness, S.; Carrara, W.; Joyce, L., “Moving target imaging algorithm for SAR data,” IEEE Trans. Aerospace and Electronic Systems, vol. AES-26, pp.57-67, 1990.
[4] Chen, Hern-Chung; McGillem, Clare D., “Target motion compensation by spectrum shifting in synthetic aperture radar,” IEEE Trans. Aerospace and Electronic Systems, Vol. 28, pp 895-901, 1992.
[5] Soumekh, Mehrdad, Fourier Array Imaging, Englewood Cliffs, N.J : PTR Prentice-Hall, 1994.
[6] Barbarossa, S.; Farina, A., “ Detection and imaging of moving objects with synthetic aperture radar. 2. Joint time- frequency analysis by Wigner-Ville distribution,” IEE Proceedings-F, vol. 139, pp. 89-97, 1992.
[7] Peleg, S.; Porat, B., “Linear FM signal parameter estimation from discrete-time observations,” IEEE Transactions on Aerospace and Electronic Systems, vol. 27, pp. 607-616, 1991.
[8] Besson, O.; Ghogho, N.; Swami, A., “Parameter estimation for random amplitude chirp signals,”IEEE Transactions on Signal Processing, vol. 47, pp 3208 –3219, 1999.
[9]J. D. Lyden, R.R. Hammond, P.R. Lyzenga, and R.A. Shuchman, “Synthetic Aper-ture Radar imaging of surface ship wakes,” J. Geophys. Res., vol. 93, no.c10, pp.12293-12303, 1988.“.
[10]K. Oumansoun, “Multifrequency SAR observation of a ship wake”, IEEE Proc.-Radar, Sonar Naving. Vol. 143, pp 275-280, 1996.
[11]N. R. Stapleton, “Ship wakes in radar imagery” , Int. J. Remote Sensing, vol.18, No.6, pp.1381-1386, 1997.
[12]J. K. E. Tunaley, Eric H. Buller, K. H. Wu and Maria T. Rey, “The simulation of the SAR images of a ship wake,” IEEE Trans. Geosci. Remote Sensing, vol.29, pp.149-155, 1991.
[13]M. T. Rey et al., “Application of Radon transform techniques to edge detection in Seasat-A SAR images,” IEEE Trans. Geosci. Remote Sensing, vol 28, pp.553-560, 1990.
[14]A. C. Copeland, “Localized Radon transform-based detection of ship wakes in SAR images,” IEEE Trans. Geosci. Remote Sensing, vol.33, pp.35-45, 1995.
[15]L. M. Murphy, “Linear feature detection and enhancement in noisy images via the Radon transform,” Pattern Recognition Letters, vol.4, pp.279-284, 1986.
[16]S. Mallat, “Singularity detection and processing with wavelets,” IEEE Trans. Acoustics, Speech, Signal Processing, vol. ASSP-37, No.12, pp.2091-2110, Dec. 1989.
[17] Ulaby, Fawwaz T., Moore, Richard K., Fung, Adrian K., “Microwave remote sensing”, Volume 1, pp. 44-47, London, Addison-Wesley, 1981.
[18] Patrick, Fitch J., Synthetic Aperture Radar, New York : Springer-Verlag, 1988.
[19] Lee, Jong-Sen; Hoppel, Karl W, “Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no 5, pp 1017-1027, 1994.
[20] Djuric, Petar M., “Parameter estimation of chirp signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38 n 12 pp. 2118-2126, 1990.
[21] Slocumb, B.J.; Kitchen, J., “A polynomial phase parameter estimation-phase unwrapping algorithm,” ICASSP-94: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 129 –132, 1994.
[22] Itoh, K, “Analysis of the Phase Unwrapping Algorithm,” Applied Optics, vol. 21, Iss 14, pp. 2470-2470, 1982.
[23] Ghiglia, Dennis C.; Pritt, Mark D., Two-Dimensional Phase Unwrapping, New York: John Wiley & Sons, 1998.
[24]Steven A. Tretter, “Estimating frequency of a noisy sinusoid by linear regression”, IEEE Transactions on Information Theory, v IT-31 n 6 p 832-835., 1985.
[25]Steven M. Kay, “Fundamentals of statistical signal processing: estimation theory”, Prentice Hall, 1993.
[26]M. Ghogho, A. K. Nandi, and A. Swami, “Cramer-Rao lower bounds and parameters estimation for random amplitude phase modulated signals”, in Proc. ICASSP, Phoenix, AZ, PP. 1577-1580, Mar. 1999.
[27]Peleg, S.; Porat, B, “The Cramer-Rao lower bound for signals with constant amplitude and polynomial phase,” IEEE Transactions on Signal Processing, vol. 39, pp. 49 –752, 1991
[28]S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Patern Anal. Machine Intell.,vol. PAMI-14, no. 7, pp.710-732, 1992.
[29]Y. Xu., J. B. Weaver, D. M. Healey, and J. Lu, “Wavelet transform domain filters: a spatially selective noise filtration technique ,” IEEE Trans.Image Processing, vol.3, no.6,pp.747-758, Nov.1995.
[30]S. Mallat, “A theory for multiresolution signal decomposition: The wavelet rep-resentation,” IEEE Trans. Patern Anal. Machine Intell.,vol.11, no. 674-693, July, 1989
[31]I. Daubechies, “Orthonormal Bases of compactly suppoted wavelets,” Communi-cations on Pure and Applied mathematics, vol.41, pp.909-996, Nov. 1988.
[32]J. M. Blackledge, Quantitative Coherent Imaging, Academic Press, 1985
[33]K. S. Chen, “Observations of oceanic signatures from airborne synthetic aperture radar over west coast of Taiwan during GlobeSAR Campaign 1993,” submitted to International Journal of Remote Sensing, 1999.指導教授 陳錕山(K. S. Chen) 審核日期 2002-7-16 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit