1. A computer-implemented process for tracking an object state over time using particle filter sensor fusion and a plurality of logical sensor modules, comprising using a computer to perform the following process actions for each iteration of the tracking process: inputting an object state estimate into a fuser module from each logical sensor module as determined by an object state tracker of that sensor module, wherein the object state estimates are in the form of a Gaussian distribution; using the fuser module to combine the object state estimate distributions to form a proposal distribution, and then sampling the proposal distribution to produce a series of particles which are provided to each of the logical sensor modules; using an object state verifier of each logical sensor module to estimate the likelihood of each provided particle; inputting the likelihood estimates from each logical sensor into the fuser module and computing a combined likelihood model for the particles; using the fuser module to compute a weight for each particle from the combined likelihood model, the proposal distribution, an object dynamics model which models the changes in the object state over time, and the weight associated with a corresponding particle in the last tracking iteration; and using the fuser module to compute a final estimate of the object state for the current tracking iteration using the particles and the particle weights, wherein said final object state estimate takes the form of a distribution.