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The metrication of LPI radar waveforms based on the asymptotic spectral distribution of wigner matrices

机译：基于维格纳矩阵的渐近谱分布的LPI雷达波形度量

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摘要

This paper presents an effective metric to evaluate different kinds of low probability of interception (LPI) waveforms. Based on the common view that white noise is the best LPI waveform, the method introduced in this paper first use the asymptotic spectral distribution of Wigner matrix as the property of white noise and use the spectral distribution of the normalized sample covariance matrix as the property of a specific waveform. Then, a numerical approximation of Kullback-Leibler divergence (NA-KLD) is deduced to measure the distance between the two distributions. The NA-KLD is regarded as the metrication to evaluate LPI waveforms. A lower value of NA-KLD represents a better LPI performance. Simulations show that the proposed NA-KLD is effective and robust to evaluate LPI radar waveforms.
机译：本文提出了一种有效的度量标准，可以评估各种不同类型的低截获率（LPI）波形。基于白噪声是最佳LPI波形的普遍观点，本文介绍的方法首先将Wigner矩阵的渐近频谱分布作为白噪声的属性，并使用归一化样本协方差矩阵的频谱分布作为B噪声的属性。一个特定的波形。然后，推导了Kullback-Leibler散度（NA-KLD）的数值近似值，以测量两个分布之间的距离。 NA-KLD被视为评估LPI波形的度量。较低的NA-KLD值表示较好的LPI性能。仿真表明，所提出的NA-KLD是评估LPI雷达波形的有效且鲁棒的方法。

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