  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.

Signal Processing