Research On Formation Optimization Algorithm For Different Size/Configuration Arrays

With the continues development of science and technology,large-scale phased array radar is widely used in many areas.That the array size of radar has been continuously expanded,not only has brought great pressure on radar engineering implementation,feeder networks and receiving/transmitting components,but also increased the complexity of related algorithms.Aims at this problem,this thesis improves the small-scale array sparse optimization algorithm and proposes a sub-array partitioning algorithm based on sparse for large arrays,which reduces the number of channels of transceiver modules and reduces the computational complexity of the algorithm.First of all,this thesis discusses the algorithm principle of the traditional genetic algorithm(GA)and particle swarm optimization(PSO)applied to array optimization,and analyzes the advantages and disadvantages of the two algorithms.Secondly,this thesis improves the coding,crossover and propagation methods of the traditional GA algorithm applied to the formation optimization.The improved GA algorithm has a faster iteration speed,can quickly converge to the global optimal value,and has engineering application value.The improved GA algorithm is applied to the optimization of the array elements of the line array and the planar array,and the array element spacing optimization of the line array.Then,this paper proposes an array optimization method based on quantum particle swarm optimization(QPSO)is presented in the thesis,and the effectiveness of the algorithm and its advantages over other algorithms are verified through simulation experiments.In addition,this thesis discusses the subarray partitioning method for large-scale arrays and the beamforming principle after subarray partitioning.Secondly,a sub-matrix partitioning algorithm is proposed,based on sparse array for large arrays.Then,the QPSO algorithm is applied to the subarray partitioning method based on sparse matrix,and the method is validated by experiments.Finally,the performance based on sparse array sub-matrix partitioning and uniform matrix sub-matrix partitioning are compared,and the performance advantages based on sparse array sub-matrix partitioning method is analyzed.