柏业超，电子科学与工程学院教授、博士生导师。2005年和2010年在南京大学分别获得学士和博士学位。2013年赴美国密西根大学安娜堡分校访学一年。现主要从事目标检测与参数估计、多维信号处理、信息融合等研究。

目标检测与参数估计、信息几何、阵列信号处理、随机矩阵理论、机器学习

电路分析、信号检测与估计

• B. Fu, Y. Shi, Y. Bai and F. Yan, Floating Target Detection in Sea Clutter Based on Likelihood Ratio Test on Hermitian Positive Definite Manifold, IEEE Transactions on Aerospace and Electronic Systems, 2026.

• S. Jing, H. Chen and Y. Bai, Multitarget Parameter Estimation for Cognitive Radar Based on RIS, IEEE Transactions on Instrumentation and Measurement, vol. 75, 2026.

• X. Zhang, S. Jing, J. Li, Y. Bai and F. Yan, Cognitive Radar Recognition with Kolmogorov-Smirnov Test and Momentum Gradient Descent, Digital Signal Processing, vol. 163, 105212, 2025. 

• Y. Jia, S. Zhang, X. Zhang, H. Long, C. Xu, Y. Bai, Y. Cheng, D. Wu, M. Deng, C. Qiu and X. Liu, Compact Meta-differentiator for Achieving Isotropically High-Contrast Ultrasonic Imaging, Nature Communications, 15, 2934, 2024.

• Y. Yan, R. Wang, C. Hu and Y. Bai, Coherent Detection of Weak Moving Targets in Compound-Gaussian Clutter Using Nonlinear Preprocessing System: Performance Measure and Implementation, IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 6, pp. 8598-8613, 2023.

• H. Chen, Y. Bai, Q. Wang, H. Chen, L. Tang and P. Han, DOA Estimation Assisted by Reconfigurable Intelligent Surfaces, IEEE Sensors Journal, vol. 23, no. 12, pp. 13433-13442, 2023.

• L. Zhang, H. Jiang, Y. Zhou, Y. Zhai, D. Zhou and Y. Bai, Wheeled Vehicle Detection Under the Asymmetric Micro-Doppler Interference, IEEE Sensors Journal, vol. 22, no. 18, pp. 18085-18092, 2022.

• Y. Jia, Y. Liu, B. Hu, W. Xiong, Y. Bai, Y. Cheng, D. Wu, X. Liu and J. Christensen, Orbital Angular Momentum Multiplexing in Space–Time Thermoacoustic Metasurfaces, Advanced Materials, 34, 2202026, 2022.

• Y. Yan, G. Wu, Y. Dong and Y. Bai, An Improved MSR-Based Data-Driven Detection Method Using Smoothing Pre-Processing, IEEE Signal Processing Letters, vol. 28, pp. 444-448, 2021.

• Y. Gao, J. Li, Y. Bai, Q. Wang and X. Zhang. An Improved Subspace Weighting Method Using Random Matrix Theory, Frontiers of Information Technology & Electronic Engineering, vol. 21, no. 9, pp. 1302–1307, 2020.

• Y. Bai, Q. Wang, C. Lo, M. Liu, J. P. Lynch and X. Zhang, Adaptive Bayesian Group Testing: Algorithms and Performance, Signal Processing, vol. 156, pp. 191-207, 2019.

• H. Chen, X. Zhang, Q. Wang and Y. Bai, Efficient Data Fusion Using Random Matrix Theory, IEEE Signal Processing Letters, vol. 25, no. 5, pp. 605-609, 2018.