Chirp estimation means identifying how the frequency of a signal changes over time. For simple signals, this may be done using classical signal-processing tools such as spectrograms or time-frequency analysis.

However, in complex RF and photonic systems, chirp estimation can become more difficult. The measured signal may include noise, nonlinear distortion, filtering effects, feedback dynamics, amplitude variation, and multiple coupled physical processes. In these cases, the chirp information may not appear as a simple clean ridge in a time-frequency plot.

Machine learning can help because it can learn relationships between multiple measured signals and the underlying frequency behavior of the system. Instead of relying only on one signal representation, a model can use several internal signals, such as photodetector output, RF amplifier output, filtered RF voltage, or optical power, to estimate the chirp more accurately.

In FDML-OEO systems, this idea is especially useful. The chirped RF behavior is produced