Particle filtering for quantized sensor information

The implication of quantized sensor information on filtering problems is studied. The Cramér-Rao lower bound (CRLB) is derived for estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter (KF) approaches can perform quite bad.