Machine Learning-Based Batch Processing for Calibration of Model and Noise Parameters

초록

Non-Gaussian or non-whiteness of noise sources often occurs in many digital avionics systems. Incorrect modeling of the system degrades the performance of parametric model-based estimators and controllers. To calibrate the model and noise parameters, this paper proposes a machine learning-based batch processing approach. We first mathematically formulate a state augmentation system containing three types of noise: color noise, state-dependent noise, and correlation noise. Next, we define accessible process and measurement residuals to create the training data set. Finally, we propose offline batch processing that recursively utilizes a machine learning technique to calibrate the model and noise parameters. Simulation results under various conditions validate the calibration performance of the proposed approach.

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