Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection
It addresses distributed multi-object tracking for applications like sensor networks, but appears incremental as it builds on existing PMB and GCI methods.
This paper tackled the problem of distributed multi-object filtering by developing a distributed Poisson multi-Bernoulli filter using a generalized covariance intersection fusion rule, with experimental results showing benefits over other filters.
This paper presents the distributed Poisson multi-Bernoulli (PMB) filter based on the generalised covariance intersection (GCI) fusion rule for distributed multi-object filtering. Since the exact GCI fusion of two PMB densities is intractable, we derive a principled approximation. Specifically, we approximate the power of a PMB density as an unnormalised PMB density, which corresponds to an upper bound of the PMB density. Then, the GCI fusion rule corresponds to the normalised product of two unnormalised PMB densities. We show that the result is a Poisson multi-Bernoulli mixture (PMBM), which can be expressed in closed form. Future prediction and update steps in each filter preserve the PMBM form, which can be projected back to a PMB density before the next fusion step. Experimental results show the benefits of this approach compared to other distributed multi-object filters.