A Decision Support System for Air Traffic Definition and Identification
Year: 2025
Type: conference-paper
Abstract: Recent advancements in weapon and platform systems have necessitated the enhancement of surveillance, detection, tracking, and identification capabilities within military command and control (C2) frameworks. The operational success and defensive capacity against sophisticated threats critically depend on the timely and accurate acquisition of sensor data and intelligence. As operational complexities and mission demands escalate, the integration of multi-sensor systems, strategic planning capabilities, and unified C2 infrastructures, has emerged as a foundational requirement for informed and timely decision-making, even during non-combat periods. In this context, we propose a robust and integrated decision support system based on a multi-layered, multi-sensor architecture aimed at enhancing the precision of aircraft classification and identification. The system comprises two distinct layers. The lower layer focuses exclusively on radar sensor data, employing data fusion techniques to consolidate inputs from multiple radar systems. This serves to reduce visual clutter on the operator interface and enhance the interpretability of target information. The upper layer extends the data fusion process by incorporating sensor inputs from additional sources (ESM, E/O systems, and ADS-B). This enables inter-sensor communication and provides operators with enriched target information for reliable identification processes. Comparative analyses were conducted between the proposed system and C2 systems currently utilized within the Turkish defence infrastructure. Due to confidentiality restrictions, the evaluation was carried out using simulated datasets that closely approximate real-world conditions. The proposed system resulted in reduction in the standard deviation of operator-displayed data and an increase in data update frequency. These outcomes suggest that the system has the potential to contribute to a more stable, reliable, and responsive operational environment.
Authors: Hira Tuna Ünlü, Derya Dinler
Institution: University of Calabria
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7103999994
FWCI:
Cites:
Cited by:
Related to: