A resource optimization allocation algorithm for radar networked system for stealth target tracking

Resources are typically optimized using the radar cross section (RCS) statistical model in the detection process of conventional collocated multiple-input multiple-output (MIMO) radar networks. However, the RCS of stealth targets changes dynamically, which can lead to the degradation of target tracking accuracy or even target loss. To address this problem, a collocated MIMO radar networked system resource optimization allocation algorithm for stealth target tracking is proposed. Firstly, the target state is estimated using the covariance intersection (CI) fusion filtering algorithm, and the predicted Bayesian Cramér-Rao lower bound (BCRLB) under the CI fusion criterion is derived. After that, the target RCS is predicted based on the property that the target RCS is related to the radar predicted observation angle, and the objective function is consisted of the weighted sum of individual target BCRLB. Consequently, a beam and power optimization algorithm under the RCS predicted model is established. Subsequently, a contribution-based fast solution algorithm is proposed to solve the model. In comparison to the RCS statistical model strategy, simulation results demonstrate that the proposed algorithm can efficiently use the target RCS information to achieve a better resource allocation, which can increase the accuracy of stealth target tracking, under the stealth target RCS dynamically changing scenario.