This article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

中文翻译：

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使用互信息优化多基地雷达成像的波形

本文解决了在杂乱环境中成像目标的雷达波形设计问题。对于稀疏和随机的传感器位置，考虑使用多基地雷达场景。目标脉冲响应使用埋地金属矿的模拟频率响应建模，杂波使用复合高斯 (CG) 分布建模。我们探索了关于基于 CG 杂波的互信息 (MI) 标准的新型波形设计技术。第一种方法呈现的波形利用了投影到稀疏基础上时场景反射率函数的 CG 分布。这与称为目标特定方法的第二种方法进行了比较，该方法使用目标和杂波频率响应的知识来优化匹配的照明波形。在这两种情况下，Taguchi 和粒子群优化求解器用于基于 MI 的波形设计优化。为了验证和比较优化波形的有效性，使用稀疏驱动的正则化雷达成像方法估计得到的场景反射率函数。我们的实验结果表明，与传统的线性调频波形相比，这两种波形优化技术的图像重建性能明显更好，并且特定于目标的方法还抑制了场景中的杂波信息。使用稀疏驱动的正则化雷达成像方法估计得到的场景反射率函数。我们的实验结果表明，与传统的线性调频波形相比，这两种波形优化技术的图像重建性能明显更好，并且特定于目标的方法还抑制了场景中的杂波信息。使用稀疏驱动的正则化雷达成像方法估计得到的场景反射率函数。我们的实验结果表明，与传统的线性调频波形相比，这两种波形优化技术的图像重建性能明显更好，并且特定于目标的方法还抑制了场景中的杂波信息。