سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

Artificial Intelligence Techniques in Multiple-Input Multiple-Output Radar Systems

نویسندگان :

Rouhollah Aghajani¹

1- 1Department of Electrical Engineering, Na. C., Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

MIMO radar،artificial intelligence،deep learning،adaptive beamforming،target detection،cognitive radar،interference mitigation

چکیده :

Multiple-Input Multiple-Output (MIMO) radar systems have emerged as a transformative technology in modern surveillance and target detection applications. The integration of Artificial Intelligence (AI) with Multiple-Input Multiple-Output (MIMO) radar systems represent a transformative leap in modern sensing technology, offering unprecedented advancements in target detection, signal processing, and adaptive beamforming. Unlike conventional phased-array radars, MIMO architectures leverage multiple transmitters and receivers to form virtual arrays, enhancing spatial resolution, angular diversity, and resilience against interference. AI techniques—particularly deep learning (e.g., CNNs, RNNs, Transformers), reinforcement learning, and generative models—optimize these systems by automating complex tasks such as real-time target classification, interference mitigation, and cognitive beamforming. However, challenges persist, including data scarcity, computational complexity, and the "black-box" nature of AI models, which hinder deployment in safety-critical applications like autonomous vehicles and defense. Emerging trends such as quantum machine learning, neuromorphic processing, and federated learning promise to address these limitations, enabling real-time, energy-efficient, and interpretable AI-radar fusion. This paper comprehensively reviews key AI techniques in MIMO radar, their applications (e.g., autonomous driving, military surveillance), and comparative advantages over traditional methods.