Particle filter for joint estimation of multi-object dynamic state and multi-sensor bias

The paper formulates the problem of sequential Bayesian estimation of a compound state consisting of a multi-object dynamic state and a multi-sensor bias. The compound state is modelled by a doubly stochastic point process, where the multi-object bias is a parent, whereas the multi-object state is the offspring point process. The prediction and the update steps for the first-order moment of the posterior density of the doubly-stochastic point process can be expressed analytically. The implementation, however, in general has to be done numerically. The paper presents a particle filter implementation illustrated in the context of multi-target tracking using range-azimuth measuring sensors with unknown biases.