The detection and tracking of multiple weak targets with time-varying and unknown number has become a hot spot and challenging problem in marine radar application. This paper integrates δ-generalized labeled multi-Bernoulli (δ-GLMB) density and labeled multi-Bernoulli (LMB) density, which are two important special cases of labeled random finite set, to propose an effective method for multiple weak target tracking with track-before-detect strategy based on Bayes framework including prediction and update step. The proposed method can deal with multi-target tracking problem with tractable computational complexity due to prediction step with dynamic grouping procedure albeit with slightly precision loss compared with standard δ-GLMB filter. In general, the conjugate prior property based GLMB does not hold on for generic observation model in update step. In order to solve this problem, this paper applies a tractable principled approximation involving GLMB density, which was firstly proposed in the literature, to make whole Bayes filtering with labeled RFS to perform iteratively for each group with TBD model. Finally, the numerical simulation results of Swerling type 1 target tracking in K-distributed sea clutter illustrate that the proposed method is superior to the methods including standard δ-GLMB-TBD filter, LMB-TBD filter and DP-TBD algorithm in comprehensive performance.

具有时变和未知数的多个弱目标的检测和跟踪已成为海洋雷达应用中的热点和挑战性问题。本文将标记广义有限集的两个重要特例δ-广义标记多伯努利（δ - GLMB）密度和标记多伯努利（LMB）密度相结合，提出了一种有效的跟踪多目标弱跟踪的方法贝叶斯框架的检测前策略包括预测和更新步骤。所提出的方法可以解决由于预测步骤采用动态分组过程而导致计算复杂度较易处理的多目标跟踪问题，尽管与标准δ相比其精度损失较小-GLMB过滤器。通常，在更新步骤中，基于共轭先验属性的GLMB对于通用观察模型不成立。为了解决这个问题，本文采用了文献中首次提出的涉及GLMB密度的可处理的原理逼近，以使带有标记的RFS的整个贝叶斯滤波能够对每个带有TBD模型的组进行迭代。最后，在K分布海杂波中进行Swerling 1类目标跟踪的数值模拟结果表明，该方法在综合性能上优于标准的δ -GLMB-TBD滤波，LMB-TBD滤波和DP-TBD算法。