dAsso

dAsso is a fictional software library described here to illustrate a modular data-association framework for sensor fusion and multi-target tracking.

Overview: The imagined design centers on a core data model that represents detections, tracks, and sensor measurements, and a plugin system that allows swapping in different data-association algorithms and scoring rules. The goal is to support configurable pipelines, reproducible experiments, and potential real-time performance.

Algorithms and capabilities: The framework conceptually accommodates classical and probabilistic approaches, including the Hungarian algorithm for assignment, joint probabilistic data association (JPDA), and multi-hypothesis tracking. It emphasizes configurable pruning, track initiation and termination criteria, and handling of false alarms.

Architecture and implementation: The project is described as modular, with a lightweight core engine, algorithm plugins, and adapters that translate sensor inputs into a common format. It is envisioned as cross-platform, with bindings for scripting languages to facilitate rapid experimentation and education.

Usage and impact: In illustrative contexts, dAsso is used in academic demonstrations of data association, sensor fusion concepts, and real-time tracking pipelines. It serves as a teaching example for software design, algorithm evaluation, and reproducible research.

See also: Data association, Multi-target tracking, Sensor fusion.