Study On The Synthesis Of Sparse Array By Hybrid Genetic Algorithm

To satisfy engineering needs, it is required that array antennas have narrow main lobe and low side lobe to improve spatial resolution and anti-interference performance of many systems, such as radar. Compared with full array, sparse array can lower construction cost, reduce complexity of the system, and ultimately satisfy engineering needs. Thus, to choose an efficient algorithm for pattern synthesis, and optimize elements'positions and weight coefficients to obtain ideal beam pattern become a research project.Genetic Algorithm (GA) is a heuristic optimization algorithm based on natural selection and genetic mechanism of organism. The perimeters which influence the outcome of the problem are coded into chromosomes, whose fitness values are determined by fitness function that expresses the pros and cons of each individual. Through crossing and variation, the searching process leads the group to evolve to higher fitness value. Synthesis of sparse array using Genetic Algorithm has been widely researched. In this paper, we proposed a hybrid Genetic Algorithm which combined Genetic Algorithm with convex optimization, and applied it on the synthesis of sparse array, and finally obtained good results.Multi-Input Multi-Out (MIMO) Radar has received wide attention since it is proposed. The pattern of MIMO radar is equivalent to the product of the patterns of transmit array and receive array. Calculating the pattern of MIMO radar by this equivalent function is much easier than directly using the MIMO radar pattern function. For MIMO radar with collocated antennas of sparse arrays, the hybrid genetic algorithm proposed in this paper obtains ideal beam pattern through optimizing element positions and weight coefficients. The results obtained satisfy the requirements of engineering application of MIMO radar system.