Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

• 成果类型: Article
• 署名作者: Piga, Dario; Mejari, Manas; Forgione, Marco
• 署名单位: Universita della Svizzera Italiana
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3122385
• 发表日期: 2022
• 页码: 5471-5478
• 关键词: maximum likelihood estimation; Bayes methods; computational modeling; Quantization (signal); Finite impulse response filters; data models; STANDARDS; Bayesian inference; Maximum Likelihood; quantized data; System identification

摘要

Identification of dynamical systems from low-resolution quantized observations presents several challenges because of the limited amount of information available in the data and since proper algorithms have to be designed to handle the error due to quantization. In this article, we consider identification of infinite impulse response models from quantized outputs. Algorithms both for maximum-likelihood estimation and Bayesian inference are developed. Finally, a particle-filter approach is presented for recursive reconstruction of the latent nonquantized outputs from past quantized observations.