中文摘要：

为在分布式目标跟踪中交换局部似然函数的信息,研究常见的分布式目标跟踪方法,提出一种基于信念传播的分布式粒子滤波方法（DPF-BP）。在有限次的迭代中,计算图的最大直径。为避免网络评估的分歧性,在计算评估之前运用一致性最大化,将节点及迭代次数表示成函数形式,经过标准化和估值计算后重采样替换。仿真实验结果表明,与标准信念一致方法、随机流言方法和都市信念一致方法（MBC）相比,在相同配置下,DPF-BP方法的均方根误差指标较优,在环形网络中运用DPF-MBC方法较好,而在树状网络中运用DPF-BP方法最佳。

英文摘要：

In order to exchange information of partial likelihood function in a distributed target tracking, several common distributed target tracking methods are studied, and a Distributed Particle Filter method based on Belief Propagation （DPF-BP） is proposed. The maximum diameter of the graph is calculated in a limited number of iterations. In order to avoid difference in network assessment, consistency maximization is used before assessing and nodes and the number of iterations are expressed as a function. After standardization and valuation calculations, the replacement is re- sampled. Simulation experimental results show that, compared with Standard Belief Consensus （SBC）, Randomized Gossip （RG） and Metropolis Belief Consensus （ MBC）, under the condition of the same configuration, DPF-BP is excellent at RootMean Square Error（RMSE）. In addition, DPF-MBC is best in the circular network, and DPF-BP is best in the tree network.