Sensor and Resource Scheduling for Search and Track Tasks in Distributed Radar Sensor Network
Yang Su, Shengnan Shi · 2024
In this paper, an adaptive sensor and resource scheduling strategy is proposed to simultaneously perform the tasks of the surveillance search and the tracking of time-varying number of maneuvering targets in radar sensor network (RSN). Firstly, aiming to maximize the search and tracking (SAT) performances while saving the resource consumption as much as possible, we model the sensor and resource scheduling problem as a mixed-integer mathematic optimization problem. Secondly, to tackle the nonconvex optimization problem, an efficient solution technique integrated with the generalized reward descent (GRD) and the modified particle swarm optimization (MPSO) algorithm is put forward, where the entropy theory is employed in the design of the MPSO. Finally, numerical simulation results are provided to demonstrate the effectiveness of the proposed algorithm.