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首页> 外文期刊>IEEE Transactions on Information Theory >Blind Gain and Phase Calibration via Sparse Spectral Methods
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Blind Gain and Phase Calibration via Sparse Spectral Methods

机译：通过稀疏光谱方法进行盲增益和相位校准

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摘要

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the illposedness of BGPC. Recently, there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.
机译：盲增益和相位校准（BGPC）是一个双线性反问题，涉及确定传感系统的未知增益和相位以及未知信号。 BGPC出现在许多应用中，例如逆渲染中的盲反照率估计，合成孔径雷达自动聚焦和传感器阵列自动校准。在某些情况下，未知信号中的稀疏结构可减轻BGPC的不适性。最近，人们对对BGPC的解决方案重新产生了兴趣，并仔细分析了误差范围。在本文中，我们将BGPC公式化为特征值/特征向量问题，并提出通过幂次迭代或在稀疏或联合稀疏情况下通过截断幂次迭代来解决它。在某些假设下，未知增益，相位和未知信号可以同时恢复。数值实验表明，幂迭代算法不仅在我们的主要结果所预测的体制中起作用，而且还在理论分析受到限制的体制中起作用。我们还表明，在对抗性条件下（例如，嘈杂的测量或不良的初始估计），我们的BGPC功率迭代算法与竞争算法相比具有优势。

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