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Incremental Learning-Based Open-Set Classification of Unknown UAVs via RF Signal Semantics

Julie Liu, Irshad A. Meer, Cicek Cavdar, Mustafa Ozger

Abstract

The proliferation of civilian and commercial unmanned aerial vehicles (UAVs) has heightened the demand for reliable radio frequency (RF)-based drone identification systems that can operate under dynamic and uncertain airspace conditions. Most existing RF-based recognition methods adopt a closed-set assumption, where all UAV types are known during training. Such an assumption becomes unrealistic in practical deployments, as new or unknown UAVs frequently emerge, leading to overconfident misclassifications and inefficient retraining cycles. To address these challenges, this paper proposes a unified incremental open-set learning framework for RF-based UAV recognition that enables both novel class discovery and incremental adaptation. The framework first performs open-set recognition to separate unknown signals from known classes in the semantic feature space, followed by an unsupervised clustering module that discovers new UAV categories by selecting between K-Means and Gaussian Mixture Models (GMM) based on composite validity scores. Subsequently, a lightweight incremental learning module integrates the newly discovered classes through a memory-bounded replay mechanism that mitigates catastrophic forgetting. Experiments on a real-world UAV RF dataset comprising 24 classes (18 known and 6 unknown) show effective open-set detection, promising clustering performance under the evaluated noise settings, and stable incremental adaptation with minimal storage cost, supporting the potential of the proposed framework for open-world UAV recognition.

Incremental Learning-Based Open-Set Classification of Unknown UAVs via RF Signal Semantics

Abstract

The proliferation of civilian and commercial unmanned aerial vehicles (UAVs) has heightened the demand for reliable radio frequency (RF)-based drone identification systems that can operate under dynamic and uncertain airspace conditions. Most existing RF-based recognition methods adopt a closed-set assumption, where all UAV types are known during training. Such an assumption becomes unrealistic in practical deployments, as new or unknown UAVs frequently emerge, leading to overconfident misclassifications and inefficient retraining cycles. To address these challenges, this paper proposes a unified incremental open-set learning framework for RF-based UAV recognition that enables both novel class discovery and incremental adaptation. The framework first performs open-set recognition to separate unknown signals from known classes in the semantic feature space, followed by an unsupervised clustering module that discovers new UAV categories by selecting between K-Means and Gaussian Mixture Models (GMM) based on composite validity scores. Subsequently, a lightweight incremental learning module integrates the newly discovered classes through a memory-bounded replay mechanism that mitigates catastrophic forgetting. Experiments on a real-world UAV RF dataset comprising 24 classes (18 known and 6 unknown) show effective open-set detection, promising clustering performance under the evaluated noise settings, and stable incremental adaptation with minimal storage cost, supporting the potential of the proposed framework for open-world UAV recognition.
Paper Structure (15 sections, 8 equations, 3 figures, 3 tables, 2 algorithms)

This paper contains 15 sections, 8 equations, 3 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Problem Definition
  4. Proposed Methodology
  5. Semantic Embedding Learning
  6. Open-Set Recognition
  7. Clustering for Novel Class Discovery
  8. Incremental Learning with Lightweight Replay
  9. Results and Discussion
  10. Experimental Setup
  11. Open-Set Recognition Performance
  12. Clustering-Based Novel Class Discovery
  13. Incremental Learning Performance
  14. Discussion and Limitations
  15. Conclusion

Figures (3)

  • Figure 1: An overview of the proposed open-world RF-based UAV recognition framework integrating open-set recognition, novel class discovery, and incremental learning with replay.
  • Figure 2: Normalized confusion matrix under open-set testing.
  • Figure 3: t-SNE visualization of (a) predicted clusters and (b) true unknown classes. Strong correspondence validates semantic consistency of clustering.