One of the main tasks involving the extended target tracking is how to partition the measurement set accurately and efficiently. In this article, an efficient graph-based partitioning algorithm is introduced for extended target tracking. To reduce the computational load and the interference of clutter on the measurement set partition, a measurement set preprocessing method based on density-based clustering algorithm is presented. An intuitive directed $k$-nearest neighbor ($k$NN) graph model based on graph theory is established to represent the relationship between different measurements in the measurement set that needs to be segmented. In the framework of directed $k$NN graph, a novel similarity metric based on shared nearest neighbor (SNN) is used, and a pairwise similarity that integrates the number of elements in the set of SNN and the closeness of data points is constructed. The spectral clustering algorithm is used to process the multiway cut in the directed $k$NN graph. The graph-based partitioning algorithm is applied to the extended target Gaussian mixture probability hypothesis density filter. Simulation results illustrate the advantages of our proposed graph-based partitioning algorithm in performance and computational efficiency.

中文翻译：

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一种基于图的高效分割算法在使用 GM-PHD 滤波器的扩展目标跟踪中的应用

涉及扩展目标跟踪的主要任务之一是如何准确有效地划分测量集。本文介绍了一种高效的基于图的分区算法，用于扩展目标跟踪。为了减少计算量和杂波对测量集划分的干扰，提出了一种基于密度聚类算法的测量集预处理方法。直觉导向$千$-最近的邻居 （$千$NN）建立基于图论的图模型来表示需要分割的测量集中不同测量之间的关系。在定向的框架内$千$NN 图，一种基于共享最近邻 (SNN) 的新颖相似度度量，并构建了一个成对相似度，该相似度将 SNN 集合中的元素数量和数据点的接近度相结合。谱聚类算法用于处理定向中的多路切割$千$神经网络图。将基于图的划分算法应用于扩展的目标高斯混合概率假设密度滤波器。仿真结果说明了我们提出的基于图的分区算法在性能和计算效率方面的优势。