Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking

TL;DR

This paper introduces an adaptive sensor selection method for maritime vessel tracking that dynamically chooses between camera and LiDAR sensors based on information gain, improving robustness and resource efficiency.

Contribution

It presents a novel entropy-driven adaptive sensing policy integrated into a multi-sensor particle filter for resilient vessel tracking in maritime environments.

Findings

LiDAR provides high accuracy at close range.

Camera extends coverage at longer ranges.

Adaptive sensing balances accuracy and continuity effectively.

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 heterogeneous multi-sensor fusion particle-filter tracker that incorporates an information-gain (entropy-reduction) adaptive sensing policy to select the most informative configuration at each fusion time bin. The approach is validated in a real maritime deployment at the CMMI 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. By avoiding continuous multi-stream processing, the adaptive configuration provides a practical baseline for resilient and resource-aware maritime surveillance.