Title :
AR time-series identification using quantized observations
Author :
Figwer, Jaroslaw
Author_Institution :
Inst. of Autom. Control, Silesian Univ. of Technol., Gliwice, Poland
Abstract :
In the paper an approach to AR time-series identification based on observations obtained using data acquisition system equipped with a quantizer having saturation is presented. In the presented approach AR time-series model identification is replaced by an ARMA time-series model identification that returns parameters of AR time-series. A focus on model identification for ultra low- and ultra high-power AR time-series is given. The presented discussion is illustrated by a simulation case study showing properties of the presented approach.
Keywords :
autoregressive moving average processes; quantisation (signal); signal detection; time series; AR time-series identification; ARMA time-series model identification; data acquisition system; quantized observation; ultra high-power AR time-series; ultra low-power AR time-series; Data acquisition; Data models; Estimation; Quantization (signal); Random processes; Standards; White noise;
Conference_Titel :
Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On
Conference_Location :
Miedzyzdroje
Print_ISBN :
978-1-4799-5082-9
DOI :
10.1109/MMAR.2014.6957357