Feature-aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and misassociations. In this paper, we develop a feature-aided multiple hypothesis tracker for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell _1$-norm to formulate the sparsity of the corruption induced by misassociations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program. With the estimated feature signal, refiltering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm.

中文翻译：

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用于联合多目标跟踪和特征提取的改进多假设跟踪器

与标准多目标跟踪 (MTT) 算法相比，特征辅助跟踪通常可以提高跟踪性能。然而，在许多应用中，目标的特征信号由稀疏的傅立叶域信号组成。它在时域中快速且非线性地变化，并且特征测量被漏检和错误关联破坏。在本文中，我们开发了一种特征辅助的多假设跟踪器，用于密集目标环境中的联合 MTT 和特征提取。我们使用原子范数约束来制定特征信号的稀疏性，并使用 $\ell_1$-norm 来制定由错误关联引起的腐败的稀疏性。在稀疏表示的基础上，通过求解一个半定规划来估计特征信号。用估计的特征信号，执行重新过滤以估计目标的运动学状态，其中关联利用运动学和特征信息。仿真结果用于说明所提出算法的性能。