Accuracy enhanced microwave frequency measurement based on the machine learning technique.

Abstract

We propose and experimentally demonstrate a microwave frequency measurement system based on the photonic technique. An amplitude comparison function is constructed to perform frequency-to-power mapping based on a non-sliced broadband optical source. The results are fed into a machine learning module which can be utilized to minimize the differential mode noise of the system caused by the polarization fluctuation. The system is reconfigurable with adjustable measurement bandwidth by adjusting the dispersion group delay of the signals at orthogonal polarizations by a polarization division multiplexed emulator (PDME). In addition, the mapping relationship is reconstructed by stacking method. The results are fed into four machine learning models: support vector regressor (SVR), KNeighbors regressor (KNN), polynomial regressor (PR) and random forest regressor (RFR). The output of the four models then combined by adding them together using linear regression method. By fitting the relationship between frequency and microwave power ratio with machine learning method, the accuracy of microwave frequency measurement system is further improved. The results show that for a measurement system with a bandwidth of 2 GHz and 4 GHz, the maximum error and the average measurement errors are all reduced. The results are promising for applications of modern radar and electronic warfare systems.