闪烁噪声统计特性未知情况下的鲁棒广义标签多伯努利滤波器  

Robust Generalized Labeled Multi⁃Bernoulli Filter for Multi⁃Target Tracking with Unknown Statistical Characteristics of Glint Noise

作　　者：侯利明 连峰[1] 谭顺成 徐从安 HOU Li-ming;LIAN Feng;TAN Shun-cheng;XU Cong-an(Institute of Integrated Automation,School of Automation Science and Engineering,Xi’an Jiaotong University,Xi’an,Shaanxi 710049,China;Nanjing Research Institute of Electronics Technology,Nanjing,Jiangsu 210039,China;Institute of Information Fusion,Naval Aviation University,Yantai,Shandong 264001,China)

机构地区：[1]西安交通大学自动化科学与工程学院综合自动化研究所,陕西西安710049 [2]南京电子技术研究所,江苏南京210039 [3]海军航空大学信息融合研究所,山东烟台264001

出　　处：《电子学报》2021年第7期1346-1353,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61671462,No.61473217);国家公派留学基金(No.201906280272);陕西省自然科学基础研究计划(No.2020JQ⁃073)。

摘　　要：为了解决闪烁噪声统计特性未知情况下的多目标跟踪问题,提出一种鲁棒广义标签多伯努利(Generalized Labeled Multi⁃Bernoulli,GLMB)滤波器.该滤波器采用均值未知且时变的多维Student’s t分布对统计特性未知的闪烁噪声进行建模.它放宽了闪烁噪声均值为零的限制性假设,可以自适应地处理闪烁噪声均值未知且时变条件下的多目标跟踪问题.本文在GLMB滤波框架下,利用变分贝叶斯方法对增广状态中的参数进行变分迭代,并通过最小化Kullback⁃Leibler散度得到边缘似然函数的近似解.仿真结果表明,在闪烁噪声统计特性未知的情况下,所提滤波器能有效地对多目标进行跟踪.A robust generalized labeled multi⁃Bernoulli(GLMB)filter is presented to perform multi⁃target tracking(MTT)with unknown statistical characteristics of glint noise.The glint noise with unknown statistical characteristics is mod⁃eled as a multivariate Student’s t distribution with unknown and time⁃varying mean.The proposed filter relaxes the restric⁃tive assumption that the mean of glint noise is zero,and can effectively deal with the problem of MTT under the condition that the mean of glint noise is unknown and time⁃varying.The variational Bayesian approximation is applied in the GLMB filtering framework with the augmented state.The approximate solution of the marginal likelihood function can be obtained by minimizing the Kullback⁃Leibler divergence.The simulation results demonstrate that the proposed filter can effectively track multi⁃target when the statistics of glint noise is unknown.

关 键 词：随机有限集 多目标跟踪 闪烁噪声 统计特性未知 变分贝叶斯推断 广义标签多伯努利滤波器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]