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Efficient Sensing for Compressive Estimation of Frequency of a Real Sinusoid
IEEE Transactions on Aerospace and Electronic Systems ( IF 4.4 ) Pub Date : 2021-02-01 , DOI: 10.1109/taes.2020.3015322
Hui Cao , Yiu Tong Chan , Hing Cheung So

Linear least squares (LS) frequency estimators are popular because they are closed-form and easy to implement. However, they are applicable to compressive frequency estimation only after reconstruction. This is because compressive sensing (CS) breaks up the temporal order of the original sinusoidal samples. This correspondence proposes an efficient sensing scheme to obtain CS samples. They are sums of the Nyquist-rate samples of the signal. There is no need for matrix multiplications nor the Random-Modulator-Preintegrator. A modified LS estimator is able to estimate frequency directly from the CS samples, without reconstruction. This estimator has accuracy that matches the theoretical lower bound, as shown by two examples.

中文翻译:

实际正弦曲线频率压缩估计的有效传感

线性最小二乘 (LS) 频率估计器很受欢迎,因为它们是封闭形式且易于实现。然而,它们仅适用于重构后的压缩频率估计。这是因为压缩感知 (CS) 破坏了原始正弦样本的时间顺序。这种对应提出了一种有效的传感方案来获得 CS 样本。它们是信号的奈奎斯特速率样本的总和。不需要矩阵乘法,也不需要随机调制器预积分器。修改后的 LS 估计器能够直接从 CS 样本估计频率,而无需重建。如两个示例所示,该估计器具有与理论下限相匹配的准确度。
更新日期:2021-02-01
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