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基于压缩传感的交通流量数据压缩方法

, PP. 113-119

Keywords: 智能交通系统,压缩传感,数据压缩,冗余字典,高斯投影,L1-合成算法

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Abstract:

为准确获得用于数据压缩的变换矩阵,引入了基于压缩传感的交通流量数据压缩方法,在数据压缩端无需考虑变换矩阵的选择问题,直接通过高斯投影实现高效数据压缩。首先验证了交通流量数据在经过K-SVD方法训练过的字典上能够实现稀疏表达;然后在数据压缩端,通过具有限制性等距条件的随机矩阵将原始高维数据投影到低维空间上,实现数据的高效快速压缩;最后在数据传输后,通过凸优化算法在交通信息处理端完成数据解压缩。以美国某高速公路线圈传感器采集到的交通流量数据,对本文方法进行了验证。试验证明该方法能够实现快速高效的压缩编码,当压缩比为41时,解压缩相对误差仅为0.0608。

References

[1]  蔡敦虎.多种小波基的图像去噪性能研究[D].武汉:武汉大学,2003. CAI Dun-hu. The research on the performance of manifold wavelet basis in image denoising[D]. Wuhan: Wuahan University, 2003.(in Chinese)
[2]  魏玉芬,梦艳君.图像编码压缩小波基的选择[J].装备制造技术,2009(4):49-50,61. WEI Yu-fen, MENG Yan-jun. The choice of orthogonal wavelets base in image compression[J]. Equipment Manufacturing Technology, 2009(4): 49-50, 61.(in Chinese)
[3]  赵志强,张 毅,胡坚明,等.基于PCA和ICA的交通流量数据压缩方法比较研究[J].公路交通科技,2008,25(11):109-118. ZHAO Zhi-qiang, ZHANG Yi, HU Jian-ming, et al. Comparative study of PCA and ICA based traffic flow compression[J]. Journal of Highway and Transportation Research and Development, 2008, 25(11): 109-118.(in Chinese)
[4]  QU Li, HU Jian-ming, ZHANG Yi. A flow volumes data compression approach for traffic network based on principal component analysis[C]∥IEEE. Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference. Seattle: IEEE, 2007: 125-130.
[5]  耿彦斌,于 雷,武 旭,等.基于信号处理技术的ITS数据压缩方法与应用[J].土木工程学报,2006,39(11):107-113. GENG Yan-bin, YU Lei, WU Xu, et al. Signal-processing-based ITS data compression techniques and applications[J]. China Civil Engineering Journal, 2006, 39(11): 107-113.(in Chinese)
[6]  CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
[7]  DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[8]  CANDES E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.
[9]  金 坚,谷源涛,梅顺良.压缩采样技术及其应用[J].电子与信息学报,2010,32(2):470-475. JIN Jian, GU Yuan-tao, MEI Shun-liang. An introduction to compressive sampling and its applications[J]. Journal of Electronics and Information Technology, 2010, 32(2): 470-475.(in Chinese)
[10]  RAUHUT H, SCHNASS K, VAN DERGHEYNST P. Compressed sensing and redundant dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219.
[11]  CANDES E J, ELDAR Y C, NEEDELL D, et al. Com-pressed sensing with coherent and redundant dictionaries[J]. Applied and Computational Harmonic Analysis, 2011, 31(1): 59-73.
[12]  AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[13]  徐 鹏.信号的稀疏分解及其在脑电信号处理中的应用研究[D].成都:电子科技大学,2006. XU Peng. Study on sparse decomposition of signal and its application in EEG processing[D]. Chengdu: University of Electronic Science and Technology of China, 2006.(in Chinese)
[14]  TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
[15]  练秋生,王成儒,孔令富.心电图压缩算法中的小波基选择[J].计算机工程与应用,2002,38(18):6-8. LIAN Qiu-sheng, WANG Cheng-ru, KONG Ling-fu. The choice of wavelet in electrocardiogram compression algorithm[J]. Computer Engineering and Applications, 2002, 38(18): 6-8.(in Chinese)
[16]  COIFMAN R R, WICKERHAUSER M V. Entropy-based algorithms for best basis selection[J]. IEEE Transactions on Information Theory, 1992, 38(2): 713-718.

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