Distributed Fusion of PHD Filters Via Exponential Mixture Densities

作者：Ueney Murat; Clark Daniel E; Julier Simon J

来源：IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 521-531.

DOI：10.1109/JSTSP.2013.2257162

• Multi-object filtering
• PHD
• CPHD
• multi-sensor fusion
• distributed fusion
• exponential mixture density
• covariance intersection
• multi-sensor multi-target tracking
• wireless sensor networks

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.

• 出版日期2013-6