Distributed Fusion of PHD Filters Via Exponential Mixture Densities
Distributed Fusion of PHD Filters Via Exponential Mixture Densities
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DOI：
10.1109/jstsp.2013.2257162
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发表时间：
2013-06-01
影响因子：
7.5
通讯作者：
Julier, Simon J.
中科院分区：
文献类型：
--
作者：
Ueney, Murat;Clark, Daniel E.;Julier, Simon J.
本文研究了网络化融合系统中的分布式多传感器多目标跟踪问题。现有的许多DMMT方法使用多假设跟踪和航迹到航迹融合。然而，这些方法有两个困难。首先，这些算法的计算成本可以随着假设的数量而按阶乘方式扩展。其次，一致的最优融合不会重复计算信息，只有高度受限的网络体系结构才能保证一致的最佳融合，这在很大程度上破坏了分布式融合的好处。本文提出了一种基于指数混合密度(EMDS)的广义协方差交(EMDS)与随机有限集(RFS)相结合的DMMT方法。我们首先推导出EMDS与RFSS配合使用的显式公式。由此，我们推导出概率假设密度滤子的表达式。该方法通过本地通信和计算支持任意网络拓扑中的DMMT。我们使用顺序蒙特卡罗技术实现了这种方法，并在模拟中展示了它的性能。
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.