Multifractal analysis of sea clutter and target detection based on the Wavelet Leaders method

2016 ◽  
Author(s):  
Caiping Xi ◽  
Shuning Zhang ◽  
Gang Xiong ◽  
Huichang Zhao
Keyword(s):  
Target Detection ◽  
Multifractal Analysis ◽  
Sea Clutter ◽  
Wavelet Leaders ◽  
Leaders Method

scholarly journals Multifractal Analysis of Hydrologic Data Using Wavelet Methods and Fluctuation Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Tongzhou Zhao ◽  
Liang Wu ◽  
Dehua Li ◽  
Yiming Ding
Keyword(s):  
Time Series ◽  
Water Level ◽  
Multifractal Analysis ◽  
Simulation Models ◽  
Northern China ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Wavelet Leaders ◽  
Detailed Characteristic ◽  
Wavelet Methods

We study the multifractal properties of water level with a high-frequency and massive time series using wavelet methods (estimation of Hurst exponents, multiscale diagram, and wavelet leaders for multifractal analysis (WLMF)) and multifractal detrended fluctuation analysis (MF-DFA). The dataset contains more than two million records from 10 observation sites at a northern China river. The multiscale behaviour is observed in this time series, which indicates the multifractality. This multifractality is detected via multiscale diagram. Then we focus on the multifractal analysis using MF-DFA and WLMF. The two methods give the same conclusion that at most sites the records satisfy the generalized binomial multifractal model, which is robust for different times (morning, afternoon, and evening). The variation in the detailed characteristic parameters of the multifractal model indicates that both human activities and tributaries influence the multifractality. Our work is useful for building simulation models of the water level of local rivers with many observation sites.

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