| ID | 原文 | 译文 |
| 53877 | 该算法使用秩1更新维护算法所需的逆矩阵并计算波束加权值的线性约束部分，使用迭代梯度方法更新波束加权值的自适应部分，将两部分按照约束缩放后相加得到波束加权值。 | The algorithm used rank-1 updating to maintain the required inverse matrix, calculated the linear constraint part of the weight, and used the iterative gradient method to update the adaptive part of the weight. The two parts are scaled and added according to the constraint to obtain the weight. |
| 53878 | 数值仿真表明该算法收敛快速，与原始算法有着几乎相同的性能。 | Numerical simulation shows that the algorithm converges quickly and has almost the same performance as the original LCRCB. |
| 53879 | 交互式多模型算法(IMM)和基于模糊控制的交互多模型算法(FIMM)是实际中常用的目标跟踪算法，然而其模型集合固定， | The interacting multiple model algorithm(IMM) and fuzzy systems-based interacting multiple model algorithm(FIMM) are practical maneuvering target tracking algorithms with fixed model sets. |
| 53880 | 当需要大量模型覆盖目标机动时，会导致计算量激增，且过多模型可能带来不必要的模型竞争，降低跟踪性能。 | When a large number of models are needed to cover all possible maneuver cases, it will lead to a surge in computation, and may even lead to unnecessary model competition, thus reducing the tracking performance. |
| 53881 | 针对这一缺陷，提出了一种基于模糊控制的改进自适应IMM算法(FAIMM)，采用一种模型概率的非线性映射处理方法实时筛选模型子集，剔除无用模型，增加有用模型的权重， | In view of this defect, an improved fuzzy-logic adaptive IMM algorithm(FAIMM) is proposed, which adopts a nonlinear mapping method of model probabilities to screen the subset of models in real time, eliminate useless models, and increase the weight of useful models. |
| 53882 | 并通过模糊推理机制自动调整过程噪声水平，使得算法对不同的目标机动模式具有更强的自适应能力。 | Besides, it can adjust the process noise level automatically through a fuzzy system, so that the algorithm has stronger adaptive ability to different target maneuvering modes. |
| 53883 | 仿真结果表明，提出的算法跟踪性能优于IMM算法以及FIMM算法，能够更好地匹配目标的机动模式。 | Simulation results show that the tracking performance of the proposed algorithm is better than that of IMM and FIMM, and can match the target maneuvering mode better. |
| 53884 | 针对扩展目标高斯混合概率假设密度(extended target Gaussian mixture probability hypothesis density，ET-GM-PHD)滤波器中的量测集划分问题，提出了一种改进的密度峰值聚类(improved density peak clustering，IDPC)量测集划分算法。 | This paper proposes an improved density peak clustering(IDPC) measurement set partition algorithm to solve the problem of measurement set partition in the extended target Gaussian mixture probability hypothesis density(ET-GM-PHD) filter. |
| 53885 | 首先，使用IDPC算法去除局部密度较低的杂波量测，以获得最有可能的目标生成的量测集。 | Firstly, the IDPC algorithm removes the low local density clutter-generated measurements to obtain the most likely target-generated measurements. |
| 53886 | 其次，将剩余的量测集聚类以获得空间上紧密联系的聚类簇和簇的聚类中心。 | Secondly, cluster the remaining measurements is able to obtain spatially close clusters and cluster centers. |