让多传感器跟踪更智能，自动适应不同设备的感知差异。
Context-Aware Sensor Modeling for Asynchronous Multi-Sensor Tracking in Stone Soup
- 引入DetectorContext抽象，让检测概率随状态动态变化
- 在异步雷达-激光雷达数据上提升HOTA和GOSPA指标
- 无需修改原有算法，适合真实复杂环境跟踪任务
真实世界中的多传感器跟踪面临异步采样、部分覆盖及检测性能差异问题。尽管概率跟踪方法允许检测概率与杂波强度依赖于状态和感知上下文，但多数实际框架仍采用全局均匀可观测假设。在多速率与部分重叠传感条件下，该简化导致高频率传感器的重复漏检会削弱仅被低频传感器观测到的目标轨迹，从而影响融合性能。本文提出DetectorContext，作为开源多目标跟踪框架Stone Soup的抽象模块，可在假设生成阶段动态评估状态相关的检测概率与杂波强度。该抽象可无缝集成至现有概率追踪器，无需修改其更新方程。在异步雷达-激光雷达数据上的实验表明，上下文感知建模恢复了稳定融合，显著提升了HOTA与GOSPA性能，且未增加误报轨迹。
原文摘要 · Abstract (English)
Multi-sensor tracking in the real world involves asynchronous sensors with partial coverage and heterogeneous detection performance. Although probabilistic tracking methods permit detection probability and clutter intensity to depend on state and sensing context, many practical frameworks enforce globally uniform observability assumptions. Under multi-rate and partially overlapping sensing, this simplification causes repeated non-detections from high-rate sensors to erode tracks visible only to low-rate sensors, potentially degrading fusion performance. We introduce DetectorContext, an abstraction for the open-source multi-target tracking framework Stone Soup. DetectorContext exposes detection probability and clutter intensity as state-dependent functions evaluated during hypothesis formation. The abstraction integrates with existing probabilistic trackers without modifying their update equations. Experiments on asynchronous radar-lidar data demonstrate that context-aware modeling restores stable fusion and significantly improves HOTA and GOSPA performance without increasing false tracks.
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