適用於頻域雷達回波訊號壓縮之可適性區塊量化器設計

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姓名

尤泓澍(Hung-Shu Yu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

適用於頻域雷達回波訊號壓縮之可適性區塊量化器設計
(Design of Block Adaptive Quantizer for Radar Echo Signal Compression in Frequency Domain)

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至系統瀏覽論文 (2028-8-31以後開放)

摘要(中)

隨著地表遙測與探勘的需求，雷達影像的解析度日益提升，意味著需要傳送的雷達影像資料量也隨之增加，然而太空中的衛星與地面上的基地站之間的通道傳輸容量有限，為了解決此問題帶來的影響，影像資料壓縮的技術成為了不可或缺的一環。本論文主要探討適用於合成孔徑雷達(Synthetic Aperture Radar, SAR)的影像資料壓縮技術，稱為可適性區塊量化器(Block Adaptive Quantizer, BAQ)，並進一步深入研究於頻域上的BAQ 影像資料壓縮演算法，稱之為快速傅立葉轉換之可適性區塊量化(Block Adaptive Quantizer with Fast Fourier Transform, FFT-BAQ)；相較於傳統時域上的BAQ 系統，FFT-BAQ 會將雷達影像資料轉到頻域上，且只需要壓縮並傳輸頻寬內的資料，因此比起傳統時域上的BAQ演算法FFT-BAQ 能節省更多的資料量，而本論文會基於FFT-BAQ 的概念，設計改良的FFTBAQ系統架構，此架構在原本的FFT-BAQ 系統上加入並整合零填充(Zero padding) 、漢明窗(Hamming window)與循環重複(Cyclic duplicate)的訊號處理動作，藉此達到最佳的量化壓縮效能。接著本論文會提出一個基於記憶體之可重構式多重路徑延遲交換FFT 架構，提供給我們提出的改良後的頻域FFT-BAQ 系統架構使用，FFT 架構中的可重構之處理單元(Processing element)可支援Radix-4、Radix-8、Radix-4^2、Radix-3、Radix-3^2與Radix-5 共六種不同模式的運算，除此之外，藉由使用位移器與加法器取代處理單元內瑣碎旋轉因子(trivial twiddle factor)乘法器，可以降低架構所需要的硬體資源，最後會使用硬體描述
語言將可重構的多重路徑延遲交換處理單元架構實現，其操作頻率最快可執行到200MHz。

摘要(英)

With the demand for high-resolution images, the data quantity of radar echo signals becomes large. However, the channel capacity between satellites in the space and base stations on the ground is limited. In order to solve this problem, data compression is necessary.In this thesis, data compression technique suitable for synthetic aperture radar is studied, including conventional block adaptive quantizer (BAQ) in the time domain, and data compression in frequency domain, called block adaptive quantizer with fast Fourier transform(FFT-BAQ). Since FFT-BAQ only compresses and transmits the data inside the chirp bandwidth, fewer data are transmitted. Based on the concept of FFT-BAQ, we design an improved FFT-BAQ architecture, which integrates zero padding, Hamming window and cyclic extension to the original FFT-BAQ system to enhance the quantization performance when practical FFT hardware accelerator is used. Then, a memory-based reconfigurable multi-path delay commutator FFT architecture is designed for our improved frequency-domain FFT-BAQ system. The reconfigurable processing element can support six different modes including Radix-4、Radix-8、Radix-4^2、Radix-3、Radix-3^2 and Radix-5. In addition, the trivial twiddle-factor multipliers of the reconfigurable PE are replaced by shifters and adders, so the hardware resources required by the FFT architecture is reduced. Finally, we use the hardware description language to implement the reconfigurable multi-path delay commutator processing element ,which can operate at a maximum operating frequency of 200 MHz.

關鍵字(中)

★ 可適性區塊量化器
★ 合成孔徑雷達
★ 資料壓縮
★ 快速傅立葉轉換

關鍵字(英)

★ block adaptive quantizer
★ synthetic aperture radar
★ data compression
★ fast Fourier transform

論文目次

摘要 ..................................... i
Abstract ..................................... ii
目錄 ..................................... iii
圖目錄 ..................................... vi
表目錄 ..................................... ix
第一章緒論 ..................................... 1
1.1 研究動機 ..................................... 1
1.2 研究方法 ..................................... 2
1.3 論文組織 ..................................... 3
第二章雷達回波資料壓縮技術 ..................................... 4
2.1 可適性區塊量化(Block Adaptive Quantization, BAQ) ..................................... 4
2.2 具快速傅立葉轉換之可適性區塊量化(Block Adaptive Quantization with Fast Fourier Transform, FFT-BAQ) ................. 6
2.2.1 非均勻位元分配(Non-uniform Bit Allocation, NBA) ..................................... 8
2.2.2 可適性加權法(Adaptive Weighted Method) ..................................... 9
2.3 空頻量化(Space-Frequency Quantization, SFQ) ..................................... 11
2.4 各文獻上頻域資料壓縮演算法規格與效能比較 ..................................... 13
第三章可適性區塊量化器設計 ..................................... 15
3.1 模擬資料分析 ..................................... 15
3.1.1 RADARSAR-1 雷達回波資料 ..................................... 15
3.1.2 超取樣(Oversampling)資料 ..................................... 19
3.2 基本BAQ 系統架構 ..................................... 21
3.2.1 Lloyd-Max 量化器(Quantizer) ..................................... 22
3.2.2 模擬結果 ..................................... 25
3.3 基本FFT-BAQ 系統架構 ..................................... 26
3.3.1 接收端資料還原 ..................................... 26
3.3.2 模擬結果 ..................................... 27
3.4 具降取樣濾波器之傳統BAQ 系統架構 ..................................... 29
3.4.1 有限脈衝響應(FIR)之濾波器 ..................................... 29
3.4.2 資料抽取與訊號頻譜混疊(Aliasing)效應 ..................................... 31
3.4.3 模擬結果 ..................................... 32
3.5 改良後的FFT-BAQ 系統架構 ..................................... 36
3.5.1 零填充與頻譜 ..................................... 37
3.5.2 循環重複與漢明窗 ....................................40
3.5.3 降取樣與IFFT ..................................... 45
3.5.4 模擬結果 ..................................... 46
3.6 BAQ 系統模擬之效能分析 ..................................... 47
第四章快速傅立葉轉換硬體設計與實現 ..................................... 49
4.1 基本FFT 設計架構介紹 ..................................... 49
4.1.1 多重路徑延遲交換(Multi-path Delay Commutator, MDC)架構 ................. 50
4.1.2 單一路徑延遲回授(Single-path Delay Feedback, SDF)架構 ....................... 51
4.1.3 基於內建記憶體(Memory-based)之架構 ..................................... 52
4.2 基於記憶體架構與多重路徑延遲交換處理單元之可重構設計 ......................... 53
4.2.1 不同基數之訊號流程圖 ...................................... 54
4.2.2 架構解析..................................... 55
4.2.3 可重構之處理單元設計與硬體資源共用 ..................................... 56
4.2.4 瑣碎旋轉因子乘法器 ..................................... 63
4.2.5 架構硬體資源分析 ..................................... 71
4.3 處理單元(Processing Element)實現與模擬結果 ..................................... 73
4.3.1 Bit-true model 與定點數模擬結果 ..................................... 73
4.3.2 硬體實作模擬結果 ...................................................................................... 74
第五章結論..................................... 77
參考文獻 ..................................... 78

參考文獻

[1] R. Kwok and W. T. K. Johnson, "Block adaptive quantization of Magellan SAR data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 27, no. 4, pp. 375-383, July 1989, doi: 10.1109/36.29557.
[2] 蔡譽良, "適用於和成孔徑雷達訊號壓縮之可適性區塊量化器設計," 碩士論文, 國立中央大學, 民國106 年6 月。
[3] J. Max, "Quantizing for minimum distortion," in IRE Transactions on Information Theory, vol. 6, no. 1, pp. 7-12, March 1960, doi: 10.1109/TIT.1960.1057548.
[4] U. Benz, K. Strodl and A. Moreira, "A comparison of several algorithms for SAR raw data compression," in IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 5, pp.1266-1276, Sept. 1995, doi: 10.1109/36.469491.
[5] J. Fischer, U. Benz and A. Moreira, "Efficient SAR raw data compression in frequency domain," IEEE 1999 International Geoscience and Remote Sensing Symposium.
IGARSS′99 (Cat. No.99CH36293), Hamburg, Germany, 1999, pp. 2261-2263 vol.4, doi:10.1109/IGARSS.1999.775095.
[6] H. Qi, Y. Deng, W. Yu and J. Chen, "Adaptive Frequency Domain Algorithm for SAR Raw Data Compression Based on Two Dimensional Look-Up Table," 8th European Conference on Synthetic Aperture Radar, Aachen, Germany, 2010, pp. 1-4.
[7] Ming Sun and Chunsheng Li, "An adaptively weighted method based algorithm for data compression of complex SAR images in frequency domain," Proceedings. 2005 IEEE
International Geoscience and Remote Sensing Symposium, 2005. IGARSS ′05., Seoul, 2005, pp. 4704-4707, doi: 10.1109/IGARSS.2005.1526721.
[8] Zixiang Xiong, K. Ramchandran and M. T. Orchard, "Space-frequency quantization for wavelet image coding," in IEEE Transactions on Image Processing, vol. 6, no. 5, pp. 677-
693, May 1997, doi: 10.1109/83.568925.
[9] D. Gleich, P. Planinsic, B. Gergic and Z. Cucej, "Progressive space frequency quantization for SAR data compression," in IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 1, pp. 3-10, Jan. 2002, doi: 10.1109/36.981344.
[10] I. G. Cumming and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data. Norwood, MA: Artech House, 2005.
[11] J. Löfgren and P. Nilsson, "On hardware implementation of radix 3 and radix 5 FFT kernels for LTE systems," 2011 NORCHIP, Lund, Sweden, 2011, pp. 1-4, doi: 10.1109/NORCHP.2011.6126703.
[12] W. -L. Tsai, S. -G. Chen and S. -J. Huang, "Reconfigurable Radix-2k×3 Feedforward FFT Architectures," 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 2019, pp. 1-5, doi: 10.1109/ISCAS.2019.8702346.
[13] X. -Y. Shih, H. -R. Chou and Y. -Q. Liu, "VLSI Design and Implementation of Reconfigurable 46-Mode Combined-Radix-Based FFT Hardware Architecture for 3GPP-LTE Applications,"
in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 1, pp. 118-129, Jan. 2018, doi: 10.1109/TCSI.2017.2725338.
[14] S. Liu and D. Liu, "A High-Flexible Low-Latency Memory-Based FFT Processor for 4G, WLAN, and Future 5G," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems,
vol. 27, no. 3, pp. 511-523, March 2019, doi: 10.1109/TVLSI.2018.2879675.
[15] Y. Guo, Z. Wang, Q. Hong, H. Luo, X. Qiu and L. Liang, "A 60-Mode High-Throughput Parallel-Processing FFT Processor for 5G/4G Applications," in IEEE Transactions on Very
Large Scale Integration (VLSI) Systems, vol. 31, no. 2, pp. 219-232, Feb. 2023, doi: 10.1109/TVLSI.2022.3227346.
[16] 林忠毅, "適用於平行處理及排程技術的無衝突定址法演算法之快速傅立葉轉換處理器設計, " 碩士論文, 國立中央大學, 民國98 年7 月。
[17] Shirshendu Roy, "An Efficient FPGA Implementation of 1024-Point FFT/IFFT Processor, "Digital System Design, Oct. 2021. https://digitalsystemdesign.in/fpga-implementation-of801024-point-fft-ifft-processor/.

指導教授

蔡佩芸(Pei-Yun Tsai)

審核日期

2023-8-11

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