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Bayesian sparse estimation of migrating targets for wideband radar

机译：宽带雷达迁移目标的贝叶斯稀疏估计

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摘要

Wideband radar systems are highly resolved in range, which is a desirable feature for mitigating clutter. However, due to a smaller range resolution cell, moving targets are prone to migrate along the range during the coherent processing interval (CPI). This range walk, if ignored, can lead to huge performance degradation in detection. Even if compensated, conventional processing may lead to high sidelobes preventing from a proper detection in case of a multitarget scenario. Turning to a compressed sensing framework, we present a Bayesian algorithm that gives a sparse representation of migrating targets in case of a wideband waveform. Particularly, it is shown that the target signature is the sub-Nyquist version of a virtually well-sampled two-dimensional (2D)-cisoid. A sparse-promoting prior allows then this cisoid to be reconstructed and represented by a single peak without sidelobes. Performance of the proposed algorithm is finally assessed by numerical simulations on synthetic and semiexperimental data. Results obtained are very encouraging and show that a nonambiguous detection mode may be obtained with a single pulse repetition frequency (PRF).
机译：宽带雷达系统在范围上具有很高的分辨率，这是缓解杂波的理想功能。但是，由于距离分辨率的像元较小，因此移动目标在相干处理间隔（CPI）期间易于沿距离迁移。如果忽略此范围移动，可能会导致检测性能大幅下降。即使得到补偿，常规处理也可能导致高旁瓣，从而在多目标场景下无法正确检测。转向压缩传感框架，我们提出了一种贝叶斯算法，该算法在宽带波形的情况下给出了迁移目标的稀疏表示。特别地，示出了目标签名是实际上采样良好的二维（2D）-类固醇的亚奈奎斯特版本。稀疏促进的先验则使该类周质得以重建并由没有旁瓣的单个峰表示。最后，通过对合成和半实验数据进行数值模拟来评估所提出算法的性能。所获得的结果令人鼓舞，并且表明可以使用单脉冲重复频率（PRF）获得无歧义的检测模式。

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