Skip to Main Content
Kalman filtering theory is applied to yield an optimal causal demodulator for pulse-amplitude-modulated (PAM) signals in the presence of white Gaussian noise. The discrete-time data (or sampled continuous-time data) are assumed to be either a stationary or non-stationary Gaussian stochastic process, in general nonwhite. Optimal demodulation with delay is also achieved by application of Kalman filtering theory. The resulting demodulators (fixed-lag smoothers) are readily constructed and their performance represents in many cases a significant improvement over that for the optimal demodulator without delay. The fixed-lag smoothing results are in contrast to those for amplitude-modulated signals (AM) where only approximate fixed-lag smoothing is possible, and this with considerable design effort. The performance of the optimal PAM demodulator is shown to be equivalent to that of an optimal discrete filter for the discrete data.