Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness.
@article{arxiv.2405.03783,
title = {Merging Parameter Estimation and Classification Using LASSO},
author = {Le Wang and Ying Wang and Yu Qiu and Mian Li and Håkan Hjalmarsson},
journal= {arXiv preprint arXiv:2405.03783},
year = {2024}
}