A formulation of multitarget tracking as an incomplete data problem
A formulation of multitarget tracking as an incomplete data problem
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DOI：
10.1109/7.625121
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发表时间：
1997-10
影响因子：
4.4
通讯作者：
H. Gauvrit;J. Le Cadre;France C Jauffret;Dcn Ingénierie;Sud France
中科院分区：
文献类型：
--
作者：
H. Gauvrit;J. Le Cadre;France C Jauffret;Dcn Ingénierie;Sud France
传统的多假设跟踪方法依赖于枚举所有的测量分配给跟踪。修剪和门控用于仅保留最可能的假设，以大幅限制可行关联的集合。主要风险是消除正确的测量序列。概率多假设跟踪（PMHT）方法已经由Streit和Luginbuhl开发，以减少“强”分配的缺点。PMHT方法是在一般混合物密度的角度。期望最大化（EM）算法是估计混合参数的基本成分。这种方法，然后扩展和应用于多目标跟踪的非线性测量模型在被动声纳的角度。
Traditional multihypothesis tracking methods rely upon an enumeration of all the assignments of measurements to tracks. Pruning and gating are used to retain only the most likely hypotheses in order to drastically limit the set of feasible associations. The main risk is to eliminate correct measurement sequences. The probabilistic multiple hypothesis tracking (PMHT) method has been developed by Streit and Luginbuhl in order to reduce the drawbacks of "strong" assignments. The PMHT method is presented in a general mixture densities perspective. The Expectation-Maximization (EM) algorithm is the basic ingredient for estimating mixture parameters. This approach is then extended and applied to multitarget tracking for nonlinear measurement models in the passive sonar perspective.