基于人工智能的多模态雷达自适应抗干扰优化算法OACSTPCD

Multimodal radar systems are susceptible to external environmental interference,such as weather conditions and electromagnetic interference,which may affect the accuracy and stability of multimodal radar data.The anti-interference performance of multimodal radar determines its measurement accuracy.Therefore,an artificial intelligence based adaptive anti-interference optimization algorithm for multimodal radar is proposed to improve its anti-interference ability.This algorithm is based on a multimodal radar signal model,analyzing the principles of range velocity synchronous deception interference and smeared spectrum(SMSP)jamming,and calculating the total echo signal received by the radar during deception interference.The calculated echo signal results are inputted into the YOLOv5s deep learning model which is of artificial intelligence.By model training and mapping processing,multimodal radar adaptive anti-interference optimization is completed to achieve radar deceptive signal interference suppression.The test results show that the interference cancellation ratio of this algorithm is above 0.935,and the interference output power result is below 0.017.It can reliably suppresses both multi interference and single interference,which achieves adaptive anti-interference optimization for multimodal radar.