Published:
Z. Zhang and P. Tokekar, “Tree Search Techniques for Adversarial Target Tracking with State-Dependent Measurement Noise”
IEEE Transactions on Control Systems Technology, 2021.
Z. Zhang, J. Lee, J. Smereka, L. Zhou, and P. Tokekar “Game Tree Search for Minimizing Detectability and Maximizing Visibility”
Autonomous Robots, 2021.
J. Chen, A. Baskaran, Z. Zhang, and P. Tokekar. "Multi-Agent Reinforcement Learning for Persistent Monitoring."
International Conference on Intelligent Robots and Systems (IROS), 2021. Accepted, to appear.
Z. Zhang, J. Lee, J. Smereka, Y. Sung, L. Zhou, and P. Tokekar, “Tree Search Techniques for Minimizing Detectability and Maximizing Visibility,”
in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2019.
Z. Zhang, L. Zhou, and P. Tokekar, “Strategies to Design Signals to Spoof Kalman Filter,”
American Control Conference (ACC), 2018.
Z. Zhang and P. Tokekar, “Non-Myopic Target Tracking Strategies for Non-Linear Systems,”
in Proceedings of the IEEE Conference on Decision and Control (CDC), 2016, p. 5591–5596.
Zhang, Z., Xu, B., Ma, L., & Geng, S. (2014, July). Parameter estimation of a railway vehicle running bogie using extended Kalman filter.
In Proceedings of the 33rd IEEE Chinese Control Conference (pp. 3393-3398).
Xu, B., Zhang, Z., Geng, S., & Ma, L. (2014, October). Parameter estimation of high-speed railway vehicle using improved Rao-Blackwellised Particle Filter.
In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 1199-1204).