LAUSR

Tracking and detecting multiple objects is difficult for a single radar device, as it may not have the capacities such as anti-interference and anti-stealth. However, if radar devices of diverse capabilities can be combined to realize collaborative networked operation, the reliability and performance of a radar system in a complex environment can be significantly improved. This paper classify the networked radar-based multi-objective task planning as a combinatorial optimization problem with constraints and abstract a distributed multi-agent system (MAS) model from a networked radar system. A node-selection algorithm was designed based on a greedy policy to narrow down solution space for subsequent networked radar task planning, reduce the amount of calculation, and improve the efficiency of the proposed algorithm. Moreover, focusing on NSGA-II, the proposed algorithm was modified using self-adaptive operators and reinforcement learning. A dual-population strategy was introduced to allow exchanges of multiple individuals between populations during migration, and the number of individuals for the exchange was obtained through reinforcement learning. In this paper, five algorithms are compared and analyzed. In addition, statistical analyses are conducted from four perspectives: the average evaluation value of energy consumption and bandwidth in the Pareto front solutions, the time consumption of the algorithm, and the diversity of the population. The results indicate that among those algorithms, the reinforcement-learning-based RNSGA-II algorithm can produce the best outcomes for networked radar task planning.