1. A method of estimating the navigational state of a system, the navigational state characterized by random variables of a state space model, the state space model specifying a time evolution of the system and its relationship to sensor observations, comprising: acquiring observation data produced by measurement sensors that provide noisy information about the navigational state of the system; and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system, the probabilistic inference system implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations to achieve for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.