# Cognitive Fusion of ZC Sequences and Time-Frequency Images for Out-of-Distribution Detection of Drone Signals

arXiv: https://arxiv.org/abs/2601.18326 · HTML: https://mlsanity.com/paper/2601.18326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https://mlsanity.com/paper/1802.04865/) (2018)
- [PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures](https://mlsanity.com/paper/2112.05135/) (2021)
- [MobileNetV4 -- Universal Models for the Mobile Ecosystem](https://mlsanity.com/paper/2404.10518/) (2024)
- [LaRE2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2403.17465/) (2024)
- [OpenMix: Exploring Outlier Samples for Misclassification Detection](https://mlsanity.com/paper/2303.17093/) (2023)
- [On the Estimation of Image-matching Uncertainty in Visual Place Recognition](https://mlsanity.com/paper/2404.00546/) (2024)
- [Gradient-Regularized Out-of-Distribution Detection](https://mlsanity.com/paper/2404.12368/) (2024)
- [A Survey on Detection, Classification, and Tracking of UAVs using Radar and Communications Systems](https://mlsanity.com/paper/2402.05909/) (2024)
- [Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial](https://mlsanity.com/paper/2403.16477/) (2024)
- [Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification](https://mlsanity.com/paper/2505.20866/) (2025)
- [6G Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2310.20242/) (2023)
- [Joint Communication and Sensing in OTFS-based UAV Networks](https://mlsanity.com/paper/2311.17742/) (2023)
- [RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://mlsanity.com/paper/2503.09033/) (2025)
- [Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images](https://mlsanity.com/paper/2504.02844/) (2025)

## Abstract (arXiv)

We propose a drone signal out-of-distribution detection (OODD) algorithm based on the cognitive fusion of Zadoff-Chu (ZC) sequences and time-frequency images (TFI). ZC sequences are identified by analyzing the communication protocols of DJI drones, while TFI capture the time-frequency characteristics of drone signals with unknown or non-standard communication protocols. Both modalities are used jointly to enable OODD in the drone remote identification (RID) task. Specifically, ZC sequence features and TFI features are generated from the received radio frequency signals, which are then processed through dedicated feature extraction module to enhance and align them. The resultant multi-modal features undergo multi-modal feature interaction, single-modal feature fusion, and multi-modal feature fusion to produce features that integrate and complement information across modalities. Discrimination scores are computed from the fused features along both spatial and channel dimensions to capture time-frequency characteristic differences dictated by the communication protocols, and these scores will be transformed into adaptive attention weights. The weighted features are then passed through a Softmax function to produce the signal classification results. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms and achieves 1.7% and 7.5% improvements in RID and OODD metrics, respectively. The proposed algorithm also performs strong robustness under varying flight conditions and across different drone types.