Adaptive Beamforming Based on Flamingo Search Algorithm with Early-Stop Strategy

Adaptive beamforming (ABF) can improve the signal-to-interference-plus-noise ratio (SINR) of radar systems through the suppression of interference and by maintaining the desired signal. However, unavoidable array defects will cause significant performance degradation in real scenarios because of sensor position error. To address this challenge, an effective ABF based on the flamingo search algorithm (FSA) is established, referred to as FSABF. The strong global search capability and fast convergence of FSA are exploited to optimize the beamforming weights. As a result, the main lobe is accurately directed toward the target, while deep nulls are imposed in interference directions, thereby significantly improving the output SINR. In addition, an early-stopping strategy is introduced to optimize the iteration process. The resulting beamformer, namely, FSABFE, maintains excellent beamforming performance while reducing computational overhead to 11.90% of that of FSABF, thereby significantly enhancing overall efficiency. This advantage makes the proposed approach more suitable for practical radar applications in situations featuring limited computational resources. The simulation results show that the proposed FSABF and FSABFE achieve robust beam control under sensor position error.