Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking
arXiv:2603.08457v2 Announce Type: replace Abstract: 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
Overview
arXiv:2603.08457v2 Announce Type: replace Abstract: 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.
Source
Originally published at arxiv.org.
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Source: https://arxiv.org/abs/2603.08457
