Recursive Bayesian state estimation from Doppler-shift measurements

The problem is recursive Bayesian estimation of position and velocity of a moving object using asynchronous measurements of Doppler-shift frequencies at several separate locations. By adopting a stochastic dynamic target motion model and assuming that the frequency of the emitting tone is known, the paper develops the theoretical Carmer-Rao lower bound for the estimation error as a good indicator of target state observability. Furthermore, a particle filter for the recursive target state estimation is developed and its error performance compared to the theoretical CRLB. Initialisation of the particle filter using Doppler-shift measurement presents itself as a serious challenge.