System Identification with Quantized Observations

The ability to identify systems in which only quantized output observations are available is often extremely important in real-world situations. Due to sensor limitations, signal quantization, or coding for communications, many older methodologies fall short in building dynamic models from these observations. This work presents new methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to systems and control design, communication and computer networks, signal processing, sensor networks, mobile agents, data fusion, remote sensing, telemedicine, and any field in which noise-corrupted quantized data need to be processed.