The project aims at defining, designing and validating a machine-learning-based method for the detection of radio-frequency anomalies, and for the identification of the associated root causes. The main purpose is to accelerate the diagnostic activity of domain experts in their analysis throughout the whole development and test phases of an antenna system.
Benefits
Classification performances
AIDA shows very good classification performances when testing antennas of the same operative band as the training dataset
Generalization approach
AIDA can receive as input data from antennas operating at different frequency bands or having different characteristic dimensions, thanks to the generalization approach implemented
Reliability verification
AIDA gives the user the possibility to check the reliability of an anomaly detection directly in the software front-end, computing a comparison between the measured pattern and the updated EM model pattern
Easy to implement algorithms
The implemented algorithms do not need experts’ knowledge on Artificial Intelligence to be configured, but only limited information and knowledge to define the relevant antennas anomalies to be considered
Anomaly quantification
AIDA provides anomaly quantification with good accuracy for specific anomaly classes
Features
AI learning method
AIDA determines the anomaly which characterizes a test input antenna using an AI model which has been trained with a fully supervised learning method
Generalisation to different platforms
From the input antenna raw patterns, derived quantities are computed in order to generalize the use of the software to similar antennas mounted on different platforms
Extensive database
Once the anomaly classification is concluded, a search for the most similar antenna at database to the antenna under test is made in order to compute the anomaly quantification
Generalisation to different antennas
Additional methods are implemented in order to manage data from antennas at different frequency bands and with different characteristic dimensions
Dynamic EM model
The anomaly classification and quantification output is usable by the user to update the EM model of the antenna
Diagnosis verification
Once the EM model is updated, AIDA gives the possibility to verify the diagnosis by comparing the updated patterns with the measured data