Mr. Shen Tingli | Mimo Radar | Best Researcher Award

Mr. Shen Tingli | Naval University of Engineering | China

Academic Background

Shen Tingli completed his undergraduate studies in Navigation Engineering at Naval Aviation University, where he built a strong foundation in aerospace and maritime navigation technologies. He pursued advanced studies in Electronic Information at Naval University of Engineering, focusing on cognitive waveform design for MIMO radar systems. His academic work has been widely cited and is accessible through multiple platforms including Scopus, reflecting a growing recognition in radar signal processing research. His publications and conference documents demonstrate both theoretical innovation and practical applications in multi-target detection, and his h-index underscores the influence of his research contributions.

Research Focus

Shen Tingli’s research centers on cognitive waveform design for MIMO radar systems, with an emphasis on adaptive and intelligent signal optimization. He investigates techniques that improve radar detection performance, enhance multi-target resolution, and reduce interference in complex environments. His work integrates machine learning strategies with classical signal processing, advancing both theoretical frameworks and practical radar applications.

Work Experience

Shen Tingli has applied his expertise in electronic information and radar systems across academic and applied research roles. He has contributed to projects involving waveform design optimization and cognitive radar development, collaborating with interdisciplinary teams to enhance detection capabilities. His experience spans algorithm development, simulation studies, and performance evaluation in advanced radar systems, bridging the gap between theoretical research and engineering implementation.

Key Contributions

Shen Tingli is recognized for developing novel approaches to cognitive MIMO radar waveform design. He has contributed algorithms that improve adaptive detection accuracy and efficiency, particularly in multi-target scenarios. His work has facilitated the integration of gradient-based optimization with genetic algorithms, enabling more effective signal design under varying operational constraints. These contributions provide a foundation for future advancements in intelligent radar systems and defense applications.

Awards & Recognition

Shen Tingli has received commendations for his research excellence and innovation in radar signal processing. His contributions to adaptive waveform design have been acknowledged in peer-reviewed journals and by professional research communities, highlighting the impact and originality of his work.

Professional Roles & Memberships

He actively participates in scientific and engineering communities, contributing to the development of radar and electronic information research. His professional roles include collaborative research projects, peer review activities, and membership in relevant technical societies, promoting knowledge exchange and innovation within the field.

Publication Profile

Scopus | ORCID

Featured Publications

Shen, T., Lu, J., Zhang, Y., Wu, P., & Li, K. Waveform Design of a Cognitive MIMO Radar via an Improved Adaptive Gradient Descent Genetic Algorithm. Applied Sciences.

Impact Statement / Vision

Shen Tingli aims to advance the field of cognitive radar by developing intelligent waveform design methods that enhance detection and operational efficiency. His vision is to contribute to next-generation radar technologies that integrate adaptive learning, robust performance, and multi-target precision, driving innovation in both defense systems and civilian radar applications.