关于目标跟踪中多径数据关联的置信传播收敛性
On the Convergence of Belief Propagation for Multipath Data Association in Target Tracking
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中文总结 AI 辅助
研究目标跟踪中多径数据关联的置信传播收敛性，通过提供收敛证明，证明其收敛到唯一不动点，仿真展示了BP在MPDA中的收敛行为及良好的精度-效率权衡。
中文摘要 AI 辅助
置信传播（BP）广泛用于目标跟踪中的数据关联。现有的BP用于数据关联的收敛分析仅处理目标与测量之间的双向对应关系，即每个目标每次扫描最多产生一个测量。多径数据关联（MPDA）允许单个目标通过不同传播路径产生多个测量，在目标、路径和测量之间创建了三向对应关系，目前尚未提供完整的收敛证明。我们为MPDA中的BP更新提供了这样一个证明，证明其收敛到唯一的不动点。仿真说明了BP在MPDA中的收敛行为，并展示了相对于多检测多假设跟踪器的单扫描和双扫描变体而言良好的精度-效率权衡。
英文摘要
Belief propagation (BP) is widely used for data association (DA) in target tracking. Existing convergence analyses of BP for DA address only the two-way correspondence between targets and measurements, where each target generates at most one measurement per scan. Multipath DA (MPDA) allows a single target to produce multiple measurements via distinct propagation paths, creating a three-way correspondence among targets, paths, and measurements, for which a complete convergence proof has not yet been provided. We provide such a proof for the BP updates in MPDA, establishing convergence to a unique fixed point. Simulations illustrate the convergence behavior of BP in MPDA and demonstrate a favorable accuracy--efficiency trade-off relative to both single-scan and two-scan variants of the multiple-detection multiple-hypothesis tracker.
发表机构
- School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
机构由 AI 辅助整理，请以论文原文为准。
