A BP Method for Track-Before-Detect
A BP Method for Track-Before-Detect
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DOI：
10.1109/lsp.2023.3296874
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发表时间：
2023-07
影响因子：
3.9
通讯作者：
Mingchao Liang;Thomas Kropfreiter;Florian Meyer
中科院分区：
文献类型：
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作者：
Mingchao Liang;Thomas Kropfreiter;Florian Meyer
追踪未知数量的低可观测物体是出了名的具有挑战性。这封信提出了一种基于检测前跟踪（TBD）方法的顺序贝叶斯估计方法。在 TBD 中，跟踪算法直接使用原始传感器测量结果，无需任何预处理。我们提出的方法基于一种新的统计模型，该模型为原始传感器测量的每个数据单元引入了新的对象假设。它允许对象交互并为多个数据单元做出贡献。基于表示我们的统计模型的因子图，我们推导了 TBD 所提出的置信传播 (BP) 方法的消息传递方程。对某些 BP 消息应用近似，以降低计算复杂性并提高可扩展性。在模拟实验中，我们提出的基于 BP 的 TBD 方法优于其他两种最先进的 TBD 方法。
Tracking an unknown number of low-observable objects is notoriously challenging. This letter proposes a sequential Bayesian estimation method based on the track-before-detect (TBD) approach. In TBD, raw sensor measurements are directly used by the tracking algorithm without any preprocessing. Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements. It allows objects to interact and contribute to more than one data cell. Based on the factor graph representing our statistical model, we derive the message passing equations of the proposed belief propagation (BP) method for TBD. Approximations are applied to certain BP messages to reduce computational complexity and improve scalability. In a simulation experiment, our proposed BP-based TBD method outperforms two other state-of-the-art TBD methods.