Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks

Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Quan Pan, Min Yang

科研成果: 期刊稿件文章同行评审

摘要

This paper addresses beam scheduling for tracking multiple smart targets in phased array radar networks, aiming to mitigate the performance degradation in previous myopic scheduling methods and enhance the tracking performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem, where each bandit process is associated with a target and its TEC states evolve with different transition rules for different actions, i.e., either the target is tracked or not. However, non-linear measurement functions necessitate the inclusion of dynamic state information for updating future multi-step TEC states. To compute the marginal productivity (MP) index, the unscented sampling method is employed to predict dynamic and TEC states. Consequently, an unscented sampling-based MP (US-MP) index policy is proposed for selecting targets to track at each time step, which can be applicable to large networks with a realistic number of targets. Numerical evidence presents that the bandit model with the scalar Kalman filter satisfies sufficient conditions for indexability based upon partial conservation laws and extensive simulations validate the effectiveness of the proposed US-MP policy in practical scenarios with TEC states.

源语言英语
文章编号7755
期刊Sensors
24
23
DOI
出版状态已出版 - 12月 2024

指纹

探究 'Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks' 的科研主题。它们共同构成独一无二的指纹。

引用此