Robust Resource Allocation for Multitarget Tracking in Multiradar Systems With Parameter Estimation Uncertainty

In the design of resource-aware multitarget tracking (MTT) framework for multi-radar systems (MRSs), the estimated target parameters from the previous frame, such as distance, angle, and reflectivity, are generally utilized as prior information to guide resource scheduling at the current frame. However, achieving perfect estimation of these parameters is impossible in practice. Ignoring such uncertainty can result in unreliable solutions for resource scheduling. To address the uncertainty stemming from target parameter estimation during MTT, this article proposes a robust joint radar-to-target assignment and power allocation (RJRAPA) strategy in MRSs. At its core, the proposed method analyzes the probabilistic uncertainties of target parameters estimation within the confidence region and establishes the parameter uncertainty model. The posterior Cram & eacute;r-Rao lower bound (PCRLB), considering parameter estimation uncertainty, is derived and adopted as the metric to evaluate target tracking accuracy, since it can provide a lower bound for the accuracy of the target state estimation. Then, based on the derived PCRLB, a robust resource scheduling problem is formulated with the goal of ensuring the optimal MTT accuracy across the whole confidence region of target parameters. The RJRAPA problem is shown to be nonconvex with respect to the confidence regions of the target parameters. Thus, we present a convex relaxation approach for transforming the RJRAPA problem into a convex optimization problem with specific deterministic parameters. Numerical experiments that involve scenarios with parameter estimation uncertainties demonstrate the efficiency and robustness of our presented RJRAPA strategy.