Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms

Area of Science:

• Signal Processing
• Radar Systems
• Optimization Algorithms

Background:

• Sparse Recovery (SR) Space-Time Adaptive Processing (STAP) offers excellent clutter suppression with limited samples.
• Nonlinear clutter in spatial-Doppler profiles causes off-grid effects, degrading SR-STAP performance.

Purpose of the Study:

• To investigate meshless search using meta-heuristic algorithms (MH) to eliminate off-grid effects in SR-STAP.
• To evaluate and improve the performance of SR-STAP in nonlinear clutter environments.

Main Methods:

• Applied Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) to SR-STAP.
• Developed an improved PSO-GWO-STAP algorithm by combining PSO and GWO for enhanced global optimization.
• Enhanced the fitness function using prior knowledge of clutter distribution.

Main Results:

• MH-STAP methods estimated clutter subspaces more accurately than traditional algorithms.
• PSO-STAP and GWO-STAP demonstrated superior clutter suppression compared to other MH-STAP methods.
• The improved PSO-GWO-STAP algorithm significantly outperformed single MH-STAP methods in solving the off-grid problem.

Conclusions:

• Meta-heuristic algorithms effectively mitigate off-grid effects in SR-STAP.
• The combined PSO-GWO approach with an improved fitness function provides superior clutter suppression performance.
• The proposed PSO-GWO-STAP method offers a robust solution for SR-STAP in challenging nonlinear clutter conditions.