Parsimonious System Identification from Quantized Observations
Parsimonious System Identification from Quantized Observations
复制标题
DOI：
10.1109/cdc45484.2021.9683192
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Omar M. Sleem;C. Lagoa
中科院分区：
文献类型：
--
作者：
Omar M. Sleem;C. Lagoa
量化作为模拟和数字环境之间的接口起着重要的作用。由于量子化是一个多到少的映射，它是一个非线性的不可逆过程。这就使得传统的系统辨识方法不再适用，而且还与量化噪声信号相关。在这项工作中，我们提出了一种方法，当只有量化测量的输出是可观察的简约系统识别。更确切地说，我们开发了一种算法，旨在确定一个低阶系统，该系统是兼容的先验信息的系统和收集的量化输出信息。此外，所提出的方法可以使用，即使只有碎片信息的量化输出是可用的。所提出的算法依赖于ADMM的方法，以crowp准范数优化。数值结果突出了所提出的方法的性能相比，在稀疏的诱导解决方案的最小化的最小化。
Quantization plays an important role as an inter-face between analog and digital environments. Since quantization is a many to few mapping, it is a non-linear irreversible process. This made, in addition of the quantization noise signal dependency, the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious system identification when only quantized measurements of the output are observable. More precisely, we develop an algorithm that aims at identifying a low order system that is compatible with a priori information on the system and the collected quantized output information. Moreover, the proposed approach can be used even if only fragmented information on the quantized output is available. The proposed algorithm relies on an ADMM approach to ℓp quasi-norm optimization. Numerical results highlight the performance of the proposed approach when compared to the ℓ1 minimization in terms of the sparsity of the induced solution.