Pattern Synthesis for Opportunistic Array Radar Using Least Square Fitness Estimation-Genetic Algorithm Method

摘要

Pattern synthesis in three-dimensional (3D) opportunistic array radar becomes complex when a multitude of antennas are considered to be randomly distributed in a 3D space. To obtain an optimal pattern, several freedoms must be constrained. A new pattern synthesis approach based on the improved genetic algorithm (GA) using the least square fitness estimation (LSFE) method is proposed. Parameters optimized by this method include antenna locations, stimulus states, and phase weights. The new algorithm demonstrates that the fitness variation tendency of GA can be effectively predicted after several "eras" by the LSFE method. It is shown that by comparing the variation of LSFE curve slope, the GA operator can be adaptively modified to avoid premature convergence of the algorithm. The validity of the algorithm is verified using computer implementation.