Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD...

Area of Science:

• Radar signal processing
• Target detection and classification
• Aerospace engineering

Background:

• Low-altitude, slow, and small (LSS) targets like birds and UAVs present low observability challenges.
• Radar observations are often degraded by strong motion clutter from sources such as aircraft and vehicles.
• Accurate discrimination between birds and UAVs is vital for air traffic safety and monitoring.

Purpose of the Study:

• To develop a robust method for distinguishing between birds and UAVs in the presence of significant motion clutter.
• To overcome limitations of traditional clutter suppression techniques that may cause residual clutter or target signal loss.
• To enhance the reliability of LSS target monitoring in complex radar environments.

Main Methods:

• Proposed a frequency correlation dual-SVD (singular value decomposition) reconstruction method to suppress motion clutter.
• Exploited spectral correlation of clutter versus weak scattering of bird/UAV targets.
• Extracted micro-motion features including sum of normalized large eigenvalues and time-frequency energy entropy.
• Employed the kernel fuzzy c-means algorithm for target classification.

Main Results:

• The proposed method effectively suppresses strong motion clutter while preserving target micro-motion characteristics.
• Demonstrated avoidance of residual clutter and target loss, unlike conventional SVD-based methods.
• Achieved accurate classification of bird and UAV targets based on extracted micro-motion features.
• Validated the method's effectiveness using both simulated and experimental radar data.

Conclusions:

• The frequency correlation dual-SVD method offers a significant advancement in LSS target discrimination under clutter interference.
• The approach provides a reliable solution for differentiating birds and UAVs, crucial for aviation safety.
• The preserved micro-motion signatures are key discriminators for accurate classification in challenging radar scenarios.