Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs

Area of Science:

• Electrical Engineering
• Signal Processing
• Network Science

Background:

• Unconventional active jamming poses a significant threat to the operational integrity of wideband radar systems.
• Existing recognition methods struggle to effectively counter these advanced jamming techniques, necessitating novel approaches.

Purpose of the Study:

• To develop and validate an effective method for recognizing unconventional active jamming in wideband radar.
• To leverage graph theory and machine learning for robust jamming identification.

Main Methods:

• The proposed method utilizes the visibility graph algorithm to convert radar time series data into graph representations.
• Key graph features, including average degree, clustering coefficient, assortativity, and network entropy, are extracted.
• A random forests classifier is employed for the classification of jamming types.

Main Results:

• The visibility graph of a linear-frequency-modulation (LFM) signal is theoretically proven to be a regular graph.
• The extracted graph features provide a rational basis for distinguishing between different jamming scenarios.
• Experimental results demonstrate a jamming recognition probability exceeding 90% at a jamming-to-noise ratio (JNR) above 0 dB.

Conclusions:

• The proposed visibility graph-based method offers a promising solution for identifying unconventional active radar jamming.
• The technique provides high accuracy and robustness, even under challenging signal conditions.
• This research contributes to enhancing the resilience of wideband radar systems against electronic warfare threats.