Context-Aware Sensor Modeling for Asynchronous Multi-Sensor Tracking in Stone Soup

Abstract

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.

Figures (5)

• Figure 1: Architectural comparison. Left: The tracker is configured with global detection probability $P_D$ and clutter intensity $\lambda$. Right: The detection reader emits a per-timestep DetectorContext that exposes state and scene dependent functionals $P_D({\boldsymbol{\mathbf{x}}};\ \mathcal{S})$ and $\lambda({\boldsymbol{\mathbf{z}}};\ \mathcal{S})$ queried by the tracker. The key difference is that observability is no longer configured as static tracker parameters but evaluated dynamically via the emitted DetectorContext.
• Figure 2: Effective sensing regions and blind sectors for the radar-lidar configuration.
• Figure 3: Scenario 1 ground-truth trajectories. Red markers show stationary objects (sea markers and islets). The ego-vessel remains fairly stationary at the position given by the black marker. The yellow target performs a close-range loop around the ego-vessel.
• Figure 4: Scenario 2 ground-truth trajectories. Red markers show stationary objects (sea markers and islets). The ego-vessel follows the black trajectory in formation with a cooperating USV (green trajectory).
• Figure 5: Representative tracking output for Scenario 1 over a 480 s interval. Ground truth trajectories are shown as dashed gray lines. Inlier target tracks are rendered in shades of blue, while clutter tracks are shown in red. The ego-vessel position and heading are indicated by the black marker. (a) JPDA maintains medium-range tracks but exhibits limited clutter suppression in lidar-dominated regions and fragments a target track within lidar range. (b) GM-PHD with uniform observability loses radar-supported tracks due to accumulated missed detections. (c) Context-aware GM-PHD restores track continuity across sensing regimes while effectively suppressing clutter.