We analyze likelihood-based identification of systems that are linear in the parameters from quantized output data; in particular, we propose a method to find approximate maximum-likelihood and maximum-a-posteriori solutions. The method consists of appropriate least-squares projections of the middle point of the active quantization intervals. We show that this approximation maximizes a variational approximation of the likelihood and we provide an upper bound for the approximation error. In a simulation study, we compare the proposed method with the true maximum-likelihood estimate of a finite impulse response model.
|Number of pages||6|
|Publication status||Published - 1 Jan 2018|
|Event||18th IFAC Symposium on System Identification (SYSID 2018) - Stockholm, Sweden|
Duration: 9 Jul 2018 → 11 Jul 2018
- Least-squares approximation
- Maximum-likelihood estimators
- quantized signals