Work

UAV Recognition Confidence: A Key Evaluation Metric for UAV Recognition Performance

Year: 2026

Type: article

Abstract: Radar plays a pivotal role throughout the entire Counter-Unmanned Aerial Vehicle (C-UAV) process, and there is an urgent need for radar technologies capable of effectively detecting and recognizing non-cooperative Unmanned Aerial Vehicles (UAVs). However, the commonly emphasized UAV True Positive Ratio (TPR) fails to adequately reflect radar performance in environments with high bird density. Frequent bird activity leads to numerous false UAV alarms and unreliable recognition results. To address this issue, this paper introduces the concept of UAV Recognition Confidence (URC), a comprehensive metric that quantifies the credibility of UAV recognition by jointly considering recognition performance indicators and environmental factors. Simulations and field measurements employ a bird random walk model and real-time trajectory statistics to represent the dynamic population variations of birds. Both simulation and X-band radar experimental results verify that the proposed URC framework can effectively characterize the recognition capability of radar systems by capturing the complex interactions between the UAV and surrounding avian activities.

Source: Drones

DOI: 10.3390/drones10040239

Authors: Zixv Su, Jun Yan, Deren Li, Deyong Kong, Jiangkun Gong, Weitao Zong

Institutions: Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Hubei University Of Economics

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China