基于Gabor原子的活立木茎体水分信号MP分解与重构
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摘要
针对活立木茎体水分时域信号呈现的非平稳、信息冗余特性,提出了一种基于Gabor原子的活立木茎体水分信号MP分解与重构方法。试验结果表明活立木茎体水分信号可以用Gabor原子库稀疏表示,靠前的原子反映信号的主要特征,靠后的原子反映信号的细微特征,原子数越多,稀疏信号越能更好地描述原始时域信号的特征。稀疏信号对比于原始时域信号,数据量明显减少,避免了信息冗余,达到了数据压缩的目的,为大量数据的存储节省了物理空间。在Gabor原子库过冗余的情况下,稀疏信号可以高质量的重构出原始时域信号,在主要特征数据点处重构误差较小,在细微特征数据点处重构误差较大。
Considering the nonstationarity and information redundancy of living tree stem moisture signals in time domain, an approach of MP decomposition and reconstruction of living tree stem moisture signals based on Gabor atoms was presented. The experimental results showed that living tree stem moisture signals can be represented sparsely by Gabor atom library. The front of atoms reflected the main features of signal and the back of atoms reflected the subtle features of signal. The more the number of atoms was, the more the sparse signal can better represent the features of original time-domain signal. Compared with the original signal in time domain, the sparse signal had many advantages. Firstly, the length of sparse signal was reduced significantly. Secondly, the sparse signal can avoid information redundancy. So the approach of representing signal sparsely can achieve the purpose of data compression and save physical space to store a large amount of data. Under the condition of Gabor atom library being redundant, original time-domain signal can be constructed with high quality from the sparse signal. And reconstructive errors at the main feature points were larger than it at the subtle feature points.
Considering the nonstationarity and information redundancy of living tree stem moisture signals in time domain, an approach of MP decomposition and reconstruction of living tree stem moisture signals based on Gabor atoms was presented. The experimental results showed that living tree stem moisture signals can be represented sparsely by Gabor atom library. The front of atoms reflected the main features of signal and the back of atoms reflected the subtle features of signal. The more the number of atoms was, the more the sparse signal can better represent the features of original time-domain signal. Compared with the original signal in time domain, the sparse signal had many advantages. Firstly, the length of sparse signal was reduced significantly. Secondly, the sparse signal can avoid information redundancy. So the approach of representing signal sparsely can achieve the purpose of data compression and save physical space to store a large amount of data. Under the condition of Gabor atom library being redundant, original time-domain signal can be constructed with high quality from the sparse signal. And reconstructive errors at the main feature points were larger than it at the subtle feature points.
引文
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[19] ZHANG G X. Time-frequency atom decomposition with quantum-inspired evolutionary algorithms[J]. Circuits, Systems and Signal Processing, 2009,29(2):209-233.
[20] 李松,汪圣利.基于BP神经网络的非线性滤波算法研究[J].电子测量技术,2018,41(12):34-39.
[21] 芮国胜, 王林, 田文飚. 一种基于基追踪压缩感知信号重构的改进算法[J]. 电子测量技术, 2010, 33(4):38-41.
[22] 赵燕东,王海兰,胡培金,等. 基于活立木介电特性的植物茎体含水量测量方法[J]. 林业科学,2010,46(11):179-183.
[23] 王海兰,鲍际平,赵燕东. 应用SWR技术研究垂柳茎体含水率[J]. 科技导报, 2009, 27(8):69-72.
[2] 戚利勇,高峰,谭豫之,等.基于归一化椭圆傅里叶描述子的黄瓜形状识别[J].农业机械学报,2011,42(8):164-167,142.
[3] 杨金显,陈超,李志鹏.基于小波卡尔曼混合算法的陀螺仪去噪方法[J].电子测量技术,2016,39(3):29-33,37.
[4] 韩永华,汪亚明,康锋,等.基于小波多分辨率分解的农田障碍物检测[J].农业机械学报,2013,44(6):215-221.
[5] 方纯.时频原子分解快速算法及其在雷达信号分析中的应用[D].成都:西南交通大学,2009.
[6] MALLAT S G, ZHANG Z F. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing,1993,41(12):3397-3145.
[7] 费晓琪,孟庆丰,何正嘉.基于冲击时频原子的匹配追踪信号分解及往复机械故障特征提取技术[J].振动与冲击,2003,22(2):26-29.
[8] 张文耀.基于匹配跟踪的低位率语音编码研究[D].北京:中国科学院研究生院软件研究所,2002.
[9] 王文延,曾庆宁,李琴.一种基于Matching pursuits时频分解算法的语音降噪方法[J].电声技术,2006(2):52-54.
[10] STARCK J, CANDES E, DONOHO D. The curvelet transform for image denoising[J].IEEE Transactions on Image Processing,2002,11(6):670-684.
[11] GRIBONVAL R, BACRY E. Harmonic decomposition of audio signals with matching pursuit[J]. IEEE Transactions on Signal Processing,2003,51(1):101-111.
[12] LOPEZ-RISUENO G, GRAJAL J, YESTE-OJEDA O. Automatic decomposition based radar complex signal interception[J]. IEE Proceedings Radar Sonar Navigation,2003,150(4):323-331.
[13] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Journal on Scientific Computing,2001,43(1):129-159.
[14] 王潇.MP和BP稀疏分解在盲源分离中的应用[D].成都:西南交通大学,2009.
[15] 尹忠科,王建英,邵君.基于原子库结构特性的信号稀疏分解[J].西南交通大学学报,2005,40(2):173-178.
[16] FRIEDMAN J H, STUETZLE W. Projection pursuit regression[J].Journal of the American Statistical Associations,1981,76(376):817-823.
[17] TEMLYAKOV V. Week greedy algorithm[J]. Advances in Computational Mathematics, 2000, 12(2/3):213-227.
[18] DAVIS G, MALLAT S, AVELLANEDA M. Adaptive greedy approximation[J]. Constructive Approximation, 1997, 13(1):57-98.
[19] ZHANG G X. Time-frequency atom decomposition with quantum-inspired evolutionary algorithms[J]. Circuits, Systems and Signal Processing, 2009,29(2):209-233.
[20] 李松,汪圣利.基于BP神经网络的非线性滤波算法研究[J].电子测量技术,2018,41(12):34-39.
[21] 芮国胜, 王林, 田文飚. 一种基于基追踪压缩感知信号重构的改进算法[J]. 电子测量技术, 2010, 33(4):38-41.
[22] 赵燕东,王海兰,胡培金,等. 基于活立木介电特性的植物茎体含水量测量方法[J]. 林业科学,2010,46(11):179-183.
[23] 王海兰,鲍际平,赵燕东. 应用SWR技术研究垂柳茎体含水率[J]. 科技导报, 2009, 27(8):69-72.