SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification

2026-01-19

AI Summary

This work addresses the challenge of composite interference recognition in GNSS systems operating under complex electromagnetic environments, where conventional single-domain approaches struggle to simultaneously capture the multi-scale characteristics of transient burst and continuous global signals. To overcome this limitation, the authors propose SKANet, a cognitive dual-stream network that fuses time-frequency images and power spectral density representations. The architecture integrates selective kernel (SK) modules, asymmetric convolution blocks (ACB), and Squeeze-and-Excitation (SE) attention mechanisms to enable cross-modal adaptive feature fusion and dynamic receptive field adjustment. Evaluated on a dataset comprising 405,000 samples, the proposed method achieves an overall accuracy of 96.99% and demonstrates significantly enhanced robustness and precision in classifying composite interference, particularly under low interference-to-noise ratios.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

As the electromagnetic environment becomes increasingly complex, Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference. Although Deep Learning (DL) effectively identifies basic interference, classifying compound interference remains difficult due to the superposition of diverse jamming sources. Existing single-domain approaches often suffer from performance degradation because transient burst signals and continuous global signals require conflicting feature extraction scales. We propose the Selective Kernel and Asymmetric convolution Network(SKANet), a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD). Distinct from conventional fusion methods that rely on static receptive fields, the proposed architecture incorporates a Multi-Branch Selective Kernel (SK) module combined with Asymmetric Convolution Blocks (ACBs). This mechanism enables the network to dynamically adjust its receptive fields, acting as an adaptive filter that simultaneously captures micro-scale transient features and macro-scale spectral trends within entangled compound signals. To complement this spatial-temporal adaptation, a Squeeze-and-Excitation (SE) mechanism is integrated at the fusion stage to adaptively recalibrate the contribution of heterogeneous features from each modality. Evaluations on a dataset of 405,000 samples demonstrate that SKANet achieves an overall accuracy of 96.99\%, exhibiting superior robustness for compound jamming classification, particularly under low Jamming-to-Noise Ratio (JNR) regimes.

Problem

• compound GNSS interference
• jamming classification
• time-frequency analysis
• adaptive fusion
• deep learning

Innovation

• Selective Kernel
• Asymmetric Convolution
• Dual-Stream Architecture
• Adaptive Modality Fusion
• Compound GNSS Interference