Abstract-In this paper, we consider the problem of Distributed Multi-sensor Multi-target Tracking (DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple hypothesis tracking and track-to-track fusion. However, there are two difficulties with these approaches. First, the computational costs of these algorithms can scale factorially with the number of hypotheses. Second, consistent optimal fusion, which does not double count information, can only be guaranteed for highly constrained network architectures which largely undermine the benefits of distributed fusion. In this paper, we develop a consistent approach for DMMT by combining a generalized version of Covariance Intersection, based on Exponential Mixture Densities (EMDs), with Random Finite Sets (RFS). We first derive explicit formulae for the use of EMDs with RFSs. From this, we develop expressions for the probability hypothesis density filters. This approach supports DMMT in arbitrary network topologies through local communications and computations. We implement this approach using Sequential Monte Carlo techniques and demonstrate its performance in simulations. Index Terms-Multi-object filtering, PHD, CPHD, multi-sensor fusion, distributed fusion, exponential mixture density, covariance intersection, multi-sensor multi-target tracking, wireless sensor networks.
In REF , Üney et al. derived a distributed fusion of PHD, CPHD and Bernoulli filters by combining a generalized version of covariance intersection.
Distributed Fusion of PHD Filters Via Exponential Mixture Densities
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing
Computer Science