论文标题
使用ADAM Learning算法的CHIRP脉冲的内部校准过程
Internal Calibration Process Using Chirp Pulses with Application of the Adam Learning Algorithm
论文作者
论文摘要
我们提出了使用CHIRP脉冲的新内部校准过程。我们的方法用于减轻热漂移，这是不必要的变化，通常发生在高功率放大器和低噪声放大器等活动元素中。所提出的方法从两个不同方面具有优势：校准信号和算法。关于校准信号，我们的方法不包含附加信号源，因为通常用于遥感的Chirp脉冲被用作校准信号。此外，我们的方法解决了分析正弦信号作为校准信号时发生的相移的歧义问题。关于算法，与常规的梯度下降不同，亚当学习算法避免了朝错误的方向学习。 使用我们的方法，成功获取了接收信号的数学形式。与常规梯度下降算法相比，我们的方法显示出更好的有效性。补偿后，增益和相位的最大差异分别为0.06 dB和2.42度。
We propose a new internal calibration process using chirp pulses. Our method is utilized to mitigate thermal drift, which is unwanted changes and usually occurs in active elements such as a high power amplifier and low noise amplifier. The proposed method has advantages from two distinct aspects: calibration signal and algorithm. In respect to the calibration signal, our method does not contain an additional signal source because chirp pulses, which are normally used for remote sensing, are used as calibration signals. Moreover, our methods solve the ambiguity problem of analyzing a phase shift which occurs when sinusoidal signals are used as calibration signals. In regards to the algorithm, the Adam learning algorithm avoids learning in the wrong direction, unlike the conventional gradient descent. Using our method, mathematical forms of received signals are acquired successfully. Our method shows better effectivity compared to the conventional gradient descent algorithm. After compensation, the maximum differences of gain and phase become 0.06 dB and 2.42 degrees, respectively.
