DISTRIBUTED STATE ESTIMATION FOR HIDDEN MARKOV MODELS WITH DYNAMIC QUANTIZATION AND RATE ALLOCATION

摘要

This paper considers state estimation of hidden Markov models by sensor networks. By employing feedback from the fusion center to the sensor nodes, a dynamic quantization scheme is proposed and analyzed by a stochastic control approach. Dynamic rate allocation is also considered.