The mean spectral radius (MSR) which indicates data correlations is used as a test statistic in data-driven detection approaches based on random matrix theory (RMT). To further improve the detection performance of MSR-based detectors, a smoothing pre-processing method is proposed in this letter. By performing a smoothing pre-processing step on the original sampled data, the divergence between the distributions of the MSR under different hypotheses will be increased, effectively improving the detection probabilities. The AR(1) model is used to illustrate the effect of the smoothing pre-processing coefficients on detection performance. The optimum smoothing coefficients under different AR(1) coefficients and the change of detection probability under certain choices of smoothing coefficients are analyzed. It is verified that the proposed smoothing pre-processing method can effectively improve detection performance by the simulation of low-frequency oscillation detection in colored noise under low signal-to-noise ratio and experiments on floating small target detection in sea clutter using IPIX datasets.

中文翻译：

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一种改进的基于MSR的平滑预处理数据驱动检测方法

表示数据相关性的平均光谱半径（MSR）用作基于随机矩阵理论（RMT）的数据驱动检测方法中的测试统计量。为了进一步提高基于MSR的检测器的检测性能，在本文中提出了一种平滑预处理方法。通过对原始采样数据执行平滑预处理步骤，可以增加不同假设下MSR分布之间的差异，有效地提高了检测概率。AR（1）模型用于说明平滑预处理系数对检测性能的影响。分析了在不同AR（1）系数下的最佳平滑系数以及在某些平滑系数选择下的检测概率变化。