Abstract:

Aiming at the sensortarget assignment (STA) problem under the complex aerial defense combat environment, a new STA model is proposed with the total combat effectiveness of the identification, tracking and positioning stages. And then, the quantum particle swarm optimization (QPSO) algorithm is improved based on the judgment of particles aggregation and evolution, which is called the selfadaptive QPSO algorithm. Next, combined with the cooperation of multiparticles and memetic searching strategy, the cooperative memetic selfadaptive QPSO algorithm is proposed. At the same time, in order to reflect the sensor combination in particle position vector, two special particle coding methods are designed for different combat environments. Finally, the experiments show that the improved algorithm is effective.