西安电子科技大学学报（自然科学版）2017，Vol.44Issue(2)：37-41,184,6.DOI:10.3969/j.issn.1001-2400.2017.02.007
模型参数未知时的CPHD多目标跟踪方法
CPHD multi-target tracking algorithm with unknown model parameters
摘要
Abstract
Since the multi-object tracking performance of the traditional method will decline with unknown model parameters, a CPHD target tracking algorithm is proposed to jointly estimate the detection probability and measurement noise covariance.Firstly, for model the unknown parameters of multiple targets tracking, the detection probability is considered as a variable in a distribution.The detection probability can be obtained by estimating the mean of the distribution.Then, the Variational Bayesian method is used to estimate the covariance of the measurement noise.Finally, the Gaussian implementation of this algorithm is presented.Simulation results show that the algorithm has good tracking performance under jointly unknown detection probability and the covariance of the measurement noise.关键词
检测概率/量测噪声协方差/变分贝叶斯/多目标跟踪Key words
detection probability/measurement noise covariance/variational Bayesian/multitarget tracking分类
信息技术与安全科学引用本文复制引用
李翠芸,王精毅,姬红兵,王荣..模型参数未知时的CPHD多目标跟踪方法[J].西安电子科技大学学报（自然科学版）,2017,44(2):37-41,184,6.基金项目
国家自然科学基金资助项目(61372003) （61372003）
国家自然科学基金青年基金资助项目(61301289) （61301289）
