Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Information-based complexity and nonparametric worst-case system identification
Journal of Complexity - Special issue: invited articles dedicated to J. F. Traub on the occasion of his 60th birthday
Asymptotically efficient parameter estimation using quantized output observations
Automatica (Journal of IFAC)
Space and time complexities and sensor threshold selection in quantized identification
Automatica (Journal of IFAC)
On identification of FIR systems having quantized output data
Automatica (Journal of IFAC)
Robust distributed maximum likelihood estimation with dependent quantized data
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
This paper addresses the problem of set membership system identification with quantized measurements. Following the work developed for binary measurements, the problem of optimal input design with multiple sensor thresholds is tackled. For a FIR model of order n, the problem is decomposed into n static gain problems. The one-step optimal input problem is solved both for equispaced and generic sensor threshold distribution. Moreover, the N-step optimal input problem for the case of equispaced thresholds is addressed, and a solution is provided under a suitable assumption on the sensor range and resolution. The obtained results allow us to construct an upper bound on the time complexity of the FIR identification problem for the case of equispaced thresholds. Numerical application examples are reported to show the effectiveness of the proposed algorithms.