@inproceedings{42ae7a2b7fce456ab71c92fc3f3ef6ff,
title = "Exploration of Bayes Risk-Based Radar Resource Management for Multi-Target Track Maintenance",
abstract = "The Radar Resource Management (RRM) problem for a multi-target tracking application is considered. The problem is defined as a stochastic optimal control problem with resource constraints. A novel Bayes risk-based formulation for the objective function, with the aim of allocating the radar resources by minimizing the risk of committing track maintenance errors, is proposed. The formulation proposed increases the interpretability in the decision-making process and conforms more with the system user's wishes. The effectiveness and benefits of active time resources management in dynamic conditions are demonstrated by means of simulations.",
keywords = "Radar Resource Management, Bayes Risk, Hypothesis Testing, Track Maintenance, Lagrangian Relaxation",
author = "S. Chioccarello and H. Driessen and A. Yarovoy",
note = "Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2025 IEEE Radar Conference (RadarConf25) ; Conference date: 04-10-2025 Through 10-10-2025",
year = "2025",
doi = "10.1109/RadarConf2559087.2025.11205111",
language = "English",
isbn = "979-8-3315-4434-8",
series = "Proceedings of the IEEE Radar Conference",
publisher = "IEEE",
pages = "1128--1133",
editor = "Marek Rupniewski and Shannon Blunt and Jacek Misiurewicz and Greco, \{Maria Sabrina\} and Braham Himed",
booktitle = "Proceedings of the 2025 IEEE Radar Conference, RadarConf 2025",
address = "United States",
}