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研究生:吳家宇
研究生(外文):Wu, Jia-Yu
論文名稱:使用分數傅立葉轉換之低截獲率雷達訊號自動化偵測與辨識
論文名稱(外文):Automated LPI Radar Detection and Classification using Fractional Fourier Transform
指導教授:劉志尉
指導教授(外文):Liu, Chih-Wei
口試委員:陳紹基林大衛桑梓賢劉志尉
口試委員(外文):Chen, Sau-GeeLin, David W.Sang, Tzu-HsienLiu, Chih-Wei
口試日期:2020-11-03
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電子研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:英文
論文頁數:88
中文關鍵詞:低截獲率雷達雷達波型辨識分數傅立葉轉換深度學習模型
外文關鍵詞:Low Probability of Intercept (LPI) RadarRadar Waveform ClassificationFractional Fourier TransformDeep Learning Model
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近年來雷達系統已經成為熱門的感知設備,使用雷達我們可以偵測物體距離、速度與角度。在軍事用途上,雷達是我方進行敵方偵蒐的重要設備,除此之外,我方也必須有能力進行敵方雷達之辨識與分析,特別是在通訊電子戰中。由於雷達調變技術的進步,低截獲率(Low Probability of Intercept, LPI)雷達迅速取代傳統脈衝波(Pulse)雷達,當所接收的訊號屬於低截獲率雷達時,接收端必須有能力在極低的訊號雜訊比(Signal-to-Noise Ratio, SNR)下進行快速且準確的辨識與偵測。
本篇論文主要針對目前常見的12種低截獲率雷達進行分析且辨識[1],有別於文獻[1-12]中使用威格納分佈(Wigner-Ville Distribution, WVD)或是橋-威廉斯分佈(Choi-Williams Distribution, CWD)做頻譜分析,本文使用分數傅立葉轉換(Fractional Fourier Transform, FrFT)作為頻譜分析,FrFT在運算複雜度上相對於WVD與CWD有絕對的優勢,而且非常適合應用在LPI這類的訊號上,因此本文搭配FrFT對接收到的LPI訊號做特徵擷取,並經由設計之深度神經網路進行雷達訊號分類。實驗數據顯示在接收訊號SNR為-10dB下,整體系統辨識準確度可以維持在94.5%,相較於[10]在SNR為-6dB下,準確率為93.6%,在相同準確率下,我們提出的演算法大約提升了4 dB的效能。
最後,我們也針對分數傅立葉轉換的快速演算法進行硬體設計,並在可程式化邏輯陣列(Field Programmable Gate Array, FPGA)中實現,在TSMC 90奈米CMOS高臨界電壓製成單元庫(cell library),系統工作時脈最高可操作在400 MHz,其面積為 31 mm2,消耗功率為245 mW(@工作電壓1.0V),系統所需總延遲為26 us。
Nowadays, the radar system has become the popular sensing device. Radar plays a key factor for enemy detection in the use of the military. On the contrary, our side must have an ability to detect and classify the radar signal from the enemy, especially in the electronic warfare (EW). Due to the improvement of the digital modulation in radar waveforms, traditional pulse radars have been rapidly replaced by the Low Probability of Intercept (LPI) radars. Thus, a fast and efficient LPI radar waveform intercept receiver under ultra-low signal to noise ratio (SNR) is the important function in the future.
In this thesis, we will focus on the recognition of twelve common LPI radar waveforms [1]. Different from the previous works [1-12] using the Wigner-Ville Distribution (WVD) or Choi-Williams Distribution (CWD) as the spectrum analysis, this work chooses Fractional Fourier Transform (FrFT). Since it has a lower computational complexity than others and a good aggregation for LPI radar signals. First, the intercepted signal is transformed into FrFT domain and we keep the most important features in the FrFT domain. Then the pre-trained deep neural network model is used to complete the recognition. According to simulation results, the classification accuracy of the proposed system is 94.5% at the SNR of -10 dB. Compared with Ref [10], it only maintains the accuracy at 93.6% at the SNR of -6 dB. Our algorithm has 4 dB improvement on the same accuracy.
Finally, the implementation of the fast algorithm of FrFT is also presented. The latency of the system is 26 us, the total area is 31 mm2, and the power consumption is 245 mW (@1.0V) in TSMC 90 nm CMOS technology with high-Vt standard cell library.
摘要 i
ABSTRACT ii
致 謝 iii
Contents iv
List of Figures vi
List of Tables ix
Chapter 1 Introduction 1
1.1 Low Probability of Intercept (LPI) Radar 1
1.2 Non-cooperative Intercept Receiver 3
1.3 Commercially Available LPI radars and Non-cooperative Intercept Receiver 7
1.4 Motivation and Principal Contribution 10
1.5 Thesis Organization 12
Chapter 2 Background 13
2.1 Time-Frequency Analysis 13
2.1.1 Linear Time-Frequency Analysis 13
2.1.2 Quadratic (Bilinear) Time-Frequency Analysis 18
2.1.3 Fractional Fourier Transform (FrFT) 22
2.1.4 Comparison of Time-Frequency Analysis 25
2.2 LPI Radar Signals 25
2.2.1 Liner Frequency Modulation (LFM) 27
2.2.2 Frequency Shift Keying (FSK) 29
2.2.3 Phase Shift Keying (PSK) 30
2.3 Previous Works 34
2.3.1 Multilayer Perceptron (MLP) Based Method 35
2.3.2 Convolutional Neural Network (CNN) Based Method 37
2.3.3 Single-shot Multi-box Detector (SSD) Based Method 39
Chapter 3 Proposed Classification System 41
3.1 System Block Diagram 41
3.2 Feature Extraction Based on FrFT 42
3.2.1 Feature Extraction in FrFT Domain 42
3.2.2 Proposed Feature Extraction Flow Chart Based on FrFT 44
3.3 Classifier 50
3.4 Comparison of Computational Complexity with Previous Works 53
Chapter 4 Simulation Results 54
4.1 Performance Comparison with FrFT 56
4.2 Performance Comparison with [10] 57
4.3 Performance Comparison with [12] 60
4.4 Extraction of Modulation Parameters 62
4.5 Robustness Testing 64
4.5.1 Multipath Channel Effect 64
4.5.2 Number of Training Samples 65
Chapter 5 FrFT Hardware Implementation 67
5.1 Fast FrFT 67
5.2 Efficient Polyphase Hardware Implementation 70
5.2.1 Polyphase Decomposion 70
5.2.2 FIR Filter 72
5.2.3 Chirp Generator 72
5.3 4K-point 2-epoch FFT 73
5.3.1 2-epoch FFT Algorithm 73
5.3.2 Proposed 4K-point 2-epoch FFT 75
5.4 Implementation Results 79
Chapter 6 Conclusion and Future Work 83
Reference 85
[1] P. E. Pace, Detecting and Classifying Low Probability of Intercept Radar. Norwood, MA, USA: Artech House, 2009.
[2] J. Lundén and V. Koivunen, ``Automatic radar waveform recognition,'' IEEE J. Sel. Topics Signal Process., vol. 1, no. 1, pp. 124_136, Jun. 2007.
[3] Y. Liu, P. Xiao, H. Wu and W. Xiao, "LPI radar signal detection based on radial integration of choi-williams time-frequency image", Journal of Systems Engineering and Electronics, vol. 26, no. 5, pp. 973-981, 2015.
[4] T. Ravi Kishore and K. D. Rao, "Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms," in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 2, pp. 901-914, April 2017, doi: 10.1109/TAES.2017.2667142.
[5] C. Wang, J. Wang and X. Zhang, "Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 2437-2441, doi: 10.1109/ICASSP.2017.7952594.
[6] M. Zhang, M. Diao, L. Gao and L. Liu, "Neural networks for radar waveform recognition", Symmetry, vol. 9, no. 5, pp. 75, May 2017.
[7] L. Gao, X. Zhang, J. Gao, and S. You, "Fusion image based radar signal feature extraction and modulation recognition,'' IEEE Access, vol. 7, pp. 13135_13148, 2019.
[8] Z. Huang, Z. Ma and G. Huang, "Radar Waveform Recognition Based on Multiple Autocorrelation Images," in IEEE Access, vol. 7, pp. 98653-98668, 2019.
[9] M. Zhang, M. Diao and L. Guo, "Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition," in IEEE Access, vol. 5, pp. 11074-11082, 2017, doi: 10.1109/ACCESS.2017.2716191.
[10] S. Kong, M. Kim, L. M. Hoang and E. Kim, "Automatic LPI Radar Waveform Recognition Using CNN," in IEEE Access, vol. 6, pp. 4207-4219, 2018, doi: 10.1109/ACCESS.2017.2788942.
[11] Q. Guo, X. Yu and G. Ruan, "LPI radar waveform recognition based on deep convolutional neural network transfer learning", Symmetry, vol. 11, no. 4, pp. 540, Apr. 2019.
[12] L. M. Hoang, M. Kim and S. Kong, "Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier," in IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3516-3530, 1 July1, 2019, doi: 10.1109/TSP.2019.2918983.
[13] Gross, F. B., and Connor, J., “Comparison of detectability of radar compression waveforms in classic passive receivers,” IEEE Trans. on Aerospace and Electronic Systems, Voltt . 43, No. 2, pp. 789—795, April, 2007.
[14] E. E. Azzouz and A. K. Nandi, "Automatic identification of digital modulations", Signal Processing, vol. 47, no. 1, pp. 55-69, Nov. 1995.
[15] Y. Pu, W. Jin, M. Zhu and L. Hu, "Classification of Radar Emitter Signals Using Cascade Feature Extractions and Hierarchical Decision Technique," International Conference on Signal Processing, Beijing, 2006.
[16] Heinbach, R. Painter and P. E. Pace, Commercially Available Low Probability of Intercept Radars and Non-Cooperative Elint Receiver Capabilities, Monterey:Naval Postgraduate School, 2014.
[17] H. Ozaktas, O. Arikan, M. Kutay, and G. Bozdagt, “Digital computation of the fractional Fourier transform,” IEEE Trans. Signal Process., vol. 44, no. 9, pp. 2141–2150, 1996.
[18] Tao, G. Liang and X. H. Zhao, "An efficient FPGA-based implementation of fractional Fourier transform algorithm", J. Signal Process. Syst., vol. 60, no. 1, pp. 47-58, Jul. 2010.
[19] M. V. N. V. Prasad, K. C. Ray and A. S. Dhar, "FPGA implementation of discrete fractional Fourier transform," International Conference on Signal Processing and Communications (SPCOM), Bangalore, 2010.
[20] A. V. Oppenheim and R. W. Schafer, Discrete Time Signal Processing, 2nd ed., Prentice Hall, 1999.
[21] E. P. Wigner, “On the quantum correction for thermodynamic equilibrium,” Physics Review, Vol. 40, pp. 749—759, 1932.
[22] J. Ville, “Theorie et applications de la notion de signal analytique,” Cables et Transmission, Vol 2A, pp. 61—74, 1948.
[23] E. Sejdic, I. Djurovic and L. Stankovic, "Fractional Fourier transform as a signal processing tool: An overview of recent developments", Signal Process., vol. 91, no. 6, pp. 1351-1369, June 2011.
[24] E. Chassande-Mottin and A. Pai, “Discrete time and frequency Wigner-Ville distribution: Moyal’s formula and aliasing,” IEEE Signal Process. Lett., vol. 12, no. 7, pp. 508–511, Jul. 2005.
[25] D. T. Barry, “Fast calculation of the Choi-Williams time-frequency distribution,” IEEE Trans. on Signal Processing, Vol. 40, No. 2 pp. 450—455, Feb. 1992.
[26] N. Levanon and E. Mozeson, Radar signals. New Yorkm, NY, USA: Wiley, 2004.
[27] W. Liu et al., “SSD: Single shot multibox detector,” in Proc. Eur. Conf. Comput. Vision, 2016, pp. 21–37.
[28] W. Cheney and D. Kincaid, Numerical Mathematics and Computing, Cole Publishing Company, Brooks, 3 edition, 1994.
[29] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, pp. 1735–80, 12 1997.
[30] B. K. Jennison, "Detection of polyphase pulse compression waveforms using the radon-ambiguity transform", IEEE Transactions on Aerospace and Electronic Systems, vol. 39, no. 1, pp. 335-343, Jan. 2003.
[31] He, Chen, et al. "A pipelined memory-efficient architecture for ultra-long variable-size FFT processors." International Conference on Computer Science and Information Technology. IEEE, 2008.
[32] C. Chan, H. Lin and C. Liu, "High-Throughput 64K-point FFT Processor for THz Imaging Radar System," International Symposium on VLSI Design, Automation and Test (VLSI-DAT), Hsinchu, Taiwan, 2019.
[33] H. -K. Lin, P. -H. Lin and C. -W. Liu, "Design of a High-Throughput and Area-Efficient Ultra-Long FFT Processor," International Symposium on VLSI Design, Automation and Test (VLSI-DAT), Hsinchu, Taiwan, 2020.
[34] N. Le Ba and T. T. H. Kim, "An Area Efficient 1024-Point Low Power Radix-2 2 FFT Processor With Feed-Forward Multiple Delay Commutators", IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 10, pp. 3291-3299, 2018.
[35] S. Liu and D. Liu, "A high-flexible low-latency memory-based FFT processor for 4G WLAN and future 5G", IEEE Transactions on Very Large Scale Integration Systems, vol. 27, no. 3, pp. 511-523, Mar. 2019.
[36] X. Zhang, L. Zuo, W. Huang and J. Guo, "Efficient method for the field-programmable gate arrays calculation of Wigner-Ville distribution," in IET Signal Processing, vol. 13, no. 6, pp. 589-595, August 2019.
[37] S. Mopuri and A. Acharyya, "Low complexity VLSI Architecture Design methodology for Wigner Ville Distribution," in IEEE Transactions on Circuits and Systems II: Express Briefs, May 2020.
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