Measurement variance ignorant target motion analysis

The paper is devoted to Bayesian target motion analysis (TMA) for the case when the variance of additive white zero-mean Gaussian measurement noise is unknown. Two Rao-Blackwellised particle filters for TMA are developed, which jointly estimate the target state and the measurement variance. The error performance of the two particle filters is compared against the theoretical Cramer-Rao lower bound. The bound suggests the error in target state estimation is not affected by the ignorance of the measurement noise variance. Both developed TMA algorithms reach this theoretical bound, however, one is significantly faster.