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Sparse self-calibration by map method for MIMO radar imaging

机译:MIMO雷达成像的地图稀疏自校准

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Multiple-input multiple-output (MIMO) radar is expected to achieve good inversion performance by utilizing space diversity technology. However, traditional imaging methods often fail owing to the practical constraints that the available transmitters and receivers are very few and the number of snapshots is very limited. More seriously, the unavoidable position errors of the transmitters and the receivers would further deteriorate the imaging results. In this paper, by exploiting the sparse priority of the target, the sparse self-calibration by maximum a posterior probability method (SSC-MAP) is proposed to provide high resolution image and realize accurate position calibration at the same time. Numerical simulations verify the effectiveness of the proposed method.
机译:多输入多输出(MIMO)雷达有望通过利用空间分集技术实现良好的反演性能。然而,由于实际的限制,可用的发送器和接收器非常少,并且快照的数量非常有限,因此传统的成像方法经常会失败。更严重的是,发射器和接收器不可避免的位置误差将进一步使成像结果恶化。本文通过利用目标的稀疏优先级,提出了一种基于最大后验概率的稀疏自标定方法(SSC-MAP),以提供高分辨率的图像并同时实现精确的位置标定。数值模拟验证了该方法的有效性。

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