摘要
为解决存在数据关联不确定、检测不确定和杂波情况下的多目标跟踪问题,提出了一种新的多目标贝叶斯滤波器.代替维持多目标状态的联合后验密度,所提出的贝叶斯滤波器联合传递各个目标状态的边缘分布和它们的存在概率.为了处理目标运动和传感器测量模型中的非线性,利用无迹变换技术提出了一种非线性高斯条件下边缘分布贝叶斯滤波器的近似实现算法.仿真实验结果表明,与PHD(Probability Hypothesis Density)滤波器相比,所提出的滤波器具有更好的多目标跟踪能力.
To resolve the problem for multi-target tracking in the presence of association uncertainty,detection uncertainty and clutter,we derive and present a novel multi-target Bayesian filter.Instead of maintaining the joint posterior density of the multi-target state,the proposed Bayesian filter jointly propagates the marginal distribution for each target and their existence probabilities. We also develop an approximation implementation algorithm of the marginal distribution Bayesian （MDB）filter for a nonlinear Gaussian system where the unscented transform technique is employed to deal with the nonlinearities of target dynamic and measure-ment models.The simulation results demonstrate that the proposed filter achieves better tracking performance of multiple targets than the probability hypothesis density （PHD）filter.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第9期1689-1695,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61271107
No.61301074)
深圳基础研究项目(No.JCYJ20140418095735618)
国防预研基金(No.9140C800501140C80340)
关键词
多目标跟踪
贝叶斯滤波器
非线性模型
边缘分布
multi-target tracking
Bayesian filter
nonlinear models
marginal distributions
