Pareto-based bi-objective optimization for robust power allocation in hybrid MIMO phased-array radar system under air defense maneuvering tracking

Recently, Hybrid MIMO phased-array (H-MIMO-PA) radar systems have been demonstrated to offer enhanced target tracking capabilities. In practice, an effective radar resource allocation (RA) strategy can ensure the quality of service (QoS) of subtasks and improve the system resource utilization in multiple target tracking (MTT) applications. In this paper, a robust power allocation strategy is developed with the objectives of enhancing target tracking accuracy (TTA) and low probability of intercept (LPI) performance in H-MIMO-PA radar. Firstly, in order to accurately grasp the air defense battlefield situation and improve the QoS for MTT, we propose a threat assessment model based on the intuitionistic fuzzy entropy (IFE) to calculate the comprehensive threat degree of each target in real time. Next, since the predicted conditional Crame & PRIME;r-Rao lower bound (PC-CRLB) is based on the most recently realized measurement and provides an accurate lower bound, the PC-CRLB in clutter is derived and utilized to formulate the task utility function for TTA. In addition, the probability of intercept is derived and adopted as the performance metric for LPI. Due to power allocation process may become conflicting for achieving better TTA and LPI performance at the same time, the Pareto-based bi-objective optimization scheme is adopted, which formulates a non-convex optimization problem. Finally, to solve the formulated model, we propose the modified bi-objective particle swarm optimization (MBOPSO) algorithm. Simulation results are provided to verify the effectiveness of the proposed power allocation algorithm.