根据信息增益动态选择摄像头或激光雷达，提升海上单船追踪的稳定性与连续性。
Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking
- 基于熵减原理，实时选择最有效的感知模态进行融合
- 近场依赖激光雷达，远场由摄像头补位，追踪连续性显著提升
- 适用于海岸固定平台的抗干扰、省资源海洋监控场景
从固定海岸平台实现鲁棒的单船追踪面临模态特异性退化问题：摄像头受光照和视觉杂乱影响，激光雷达在远距离及间歇回波下性能下降。本文提出一种支持测量级相机-激光雷达融合的粒子滤波追踪器，结合基于信息增益（熵减）的自适应感知策略，在每个融合时间窗内选择最具信息量的传感模态。该方法在塞浦路斯海事与海洋研究所智能码头测试场（阿伊亚纳帕港）的真实海事部署中验证，采用岸基3D激光雷达与高处固定摄像头，追踪配备地面真实位置的刚性充气艇。对比了激光雷达仅用、摄像头仅用、全传感器及自适应配置。结果表明：激光雷达在近场精度占优，摄像头在激光雷达失效时维持更长距离覆盖，而自适应策略通过基于信息增益的模态切换，在准确率与连续性之间取得良好平衡。因此，该自适应配置为鲁棒且资源敏感的海洋监视提供了实用的传感器选择基准。
原文摘要 · Abstract (English)
Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns. We present a particle-filter tracker that supports sequential measurement-level camera-LiDAR fusion and an information-gain (entropy-reduction) adaptive sensing policy that selects the most informative sensing modality at each fusion time bin. The approach is validated in a real maritime deployment at the Cyprus Marine and Maritime Institute Smart Marina Testbed (Ayia Napa Marina, Cyprus), using a shore-mounted 3D LiDAR and an elevated fixed camera to track a rigid inflatable boat with onboard GNSS ground truth. We compare LiDAR-only, camera-only, All sensors, and adaptive configurations. Results show LiDAR dominates near-field accuracy, the camera sustains longer-range coverage when LiDAR becomes unavailable, and the adaptive policy achieves a favorable accuracy-continuity trade-off by switching modalities based on information gain. The adaptive configuration therefore provides a practical sensor-selection baseline for resilient and resource-aware maritime surveillance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。