基于人工智能的多模态雷达自适应抗干扰优化算法

Artificial intelligence based adaptive anti⁃interference optimization algorithm for multimodal radar

多模态雷达系统容易受到外界环境干扰,如天气条件、电磁干扰等,而这些干扰可能会影响多模态雷达数据的准确性和稳定性。多模态雷达的抗干扰性能决定雷达的测量精度,因此,为提升多模态雷达的抗干扰能力,提出基于人工智能的多模态雷达自适应抗干扰优化算法。该算法以多模态雷达信号模型为基础,分析距离速度同步欺骗干扰、频谱弥散干扰原理,计算欺骗干扰时雷达接收的总回波信号。将计算的回波信号结果输入至人工智能的YOLOv5s深度学习模型中,通过模型的训练和映射处理,完成多模态雷达自适应抗干扰优化,实现雷达欺骗性信号干扰抑制。测试结果显示,该算法的干扰对消比结果在0.935以上,干扰输出功率结果在0.017以下,能够可靠完成多干扰和单一干扰两种干扰抑制,实现多模态雷达自适应抗干扰优化。

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.