Sensor resource management allocation strategy for multi-target tracking in distributed radar network

Abstract

The complexity of combat environment makes it more and more difficult for traditional monostatic radar to track targets continuously. The radar network technology can effectively improve the overall tracking performance. Therefore, in the distributed radar network scenario, a joint sensor selection, bandwidth and dwell time resource optimal allocation algorithm is proposed for multi-target tracking. At first, the local state is estimated and corrected by the Covariance Intersection (CI) fusion strategy. Then, the algorithm aims to minimize the predicted Bayesian Cramer-Rao Lower Bound (BCRLB) of the worst target tracking mean square error. And a joint radar node selection, bandwidth and dwell time resource optimization model is established for multi-target tracking in distributed radar network. The radar node is selected with BCRLB as the metric criterion first. Then, the optimal model after sensor selection is carried out by using the cyclic minimum algorithm and the minimax algorithm. Simulation experiments show that compared with the uniform resource allocation and single-resource optimal allocation algorithms, the proposed algorithm can effectively improve the multi-target tracking accuracy under the limited resources.