State Estimation with Coarsely Quantized, High-Data-Rate Measurements

This paper treats the problem of estimating a signal corrupted by noise that is sampled and quantized at a high data rate. Local and global processors are proposed to achieve data compression that permits near optimal extraction of information. Two techniquesmaximum likelihood and minimum transform chi square, which are in the class of best asymptotically normal estimators- are investigated for the local processor. Simulation results are presented to demonstrate the feasibility of the approach.