Self-calibrating phase interferometry using random forest regression, neural network, or other machine learning model

A method comprising:

• receiving one or more incoming signals at multiple antennas;
• providing antenna measurements associated with the one or more incoming signals to a trained machine learning model, the antenna measurements comprising phase measurements associated with the one or more incoming signals;
• and
• processing the antenna measurements using the trained machine learning model to generate a prediction of an angle of arrival associated with the one or more incoming signals;
• wherein the trained machine learning model is trained to generate the prediction of the angle of arrival even while compensating for phase errors affecting the antenna measurements.

A non-transitory machine-readable medium containing instructions that when executed cause at least one processor to:

• obtain antenna measurements associated with one or more incoming signals received at multiple antennas, the antenna measurements comprising phase measurements associated with the one or more incoming signals;
• provide the antenna measurements to a trained machine learning model;
• and
• process the antenna measurements using the trained machine learning model to generate a prediction of an angle of arrival associated with the one or more incoming signals;
• wherein the trained machine learning model is trained to generate the prediction of the angle of arrival even while compensating for phase errors affecting the antenna measurements.