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Logistic regression prediction based on fractal characteristics of sea surface targets

机译：基于海面目标分形特征的Logistic回归预测

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摘要

Sea clutter is a kind of signal with low signal-to-noise ratio. Fractional Fourier transform is used to gather energy. Fractional Brownian motion model is introduced to model sea clutter. Based on the measured data of IPIX radar, high-order multi-fractal parameters are calculated. Fitting the multi-fractal parameters, choosing the steepest interval and calculating its slope. At the same time, choosing the fractal parameters with scale -30 under HH and VV polarization. The small targets are 0 and 1, respectively. The slope and high-scale fractal parameters of different sea conditions are normalized, and the normalized data are predicted by logistic regression. The simulation results show that FRFT can aggregate the energy of sea clutter signal. Logistic regression model can predict the fractal parameter data of sea clutter FRFT domain, and the prediction accuracy reaches 83.42%.
机译：海杂波是一种低信噪比的信号。分数阶傅立叶变换用于收集能量。引入了分数布朗运动模型来模拟海浪杂波。根据IPIX雷达的实测数据，计算出高阶多分形参数。拟合多重分形参数，选择最陡的间隔并计算其斜率。同时，在HH和VV极化下选择标度为-30的分形参数。小目标分别为0和1。对不同海况的坡度和高阶分形参数进行了归一化，并通过逻辑回归预测了归一化的数据。仿真结果表明，FRFT可以聚集海杂波信号的能量。 Logistic回归模型可以预测海杂波FRFT域的分形参数数据，预测精度达到83.42％。

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