Research On The Optimization For Subarray Structure In Electronic Warfare Environment

Phased array system is usually composed of thousands of array elements, To reduce the hardware cost,elements are often combined into subarrays. For project implementation, it is needed to form the relative concentration of the subarrays. Phased array system needs to be able to maintain good performance in face of the complex electromagnetic interference environment. That is to say, it is needed to study how to get a phased array system which can maintain good performance under the complex interference environment and the subarrays configure is relatively concentrated.Structural optimization design for the subarrays is a very complex issue, the traditional optimization algorithm can not be used to solve the problem, so we learn from biological and natural mechanisms using genetic algorithm, because of our genetic algorithm can widen the search space and improve search efficiency, it has become a effective tool to optimize the design of subarray configuration.For the phased array system can maintain good performance in the complex interference environment and be a relative concentrate subarray configuration, there is very little research on the method. So we study the method has great significance.This thesis studies four aspects: genetic algorithms and decoding program and the relative concentration of subarray configuration constraints; multi-objective genetic algorithm used for structure optimization of subarrays; circular array structure optimization; sidelobe suppression in the multiple interference case and optimization of subarray configuration.The coding and decoding methods of"start point"are used to obtain the initial array structure, while in the process of the structure of the subarrays, the legal function and full covered function are needed to meet the requirements of the array. The legal function is mainly used to keep the antenna elements adjacent and in a given range, while the full covered function using nearest neighbor rule and the improved nearest neighbor rule to make the array elements covered and make the elements of every subarray is concentrated.Multi-objective genetic algorithm for planar phased array system of structural optimization, the weighted coefficient method is used, the optimization of the sidelobe level of the section U and V of the beam pattern and the use of restraint conditions for the planar phased array system make the subarrays relatively concentrated. So the sidelobe level optimization and the relative concentration of subarrays structure of the phased array system is obtained. Finally, we compare the Pareto rank and weighted coefficient method.To optimize of the subarrays structure of the circular array, different distribution of start parts are used, including random distribution, ring distribution and uniform distribution. At the same time, the nearest neighbor criteria, the improved nearest neighbor criteria and Regular shape criteria are use to keep the subarrays configuration concentrated.For the sidelobe suppression in the multiple interference, the adative beamforming is used and space-time adaptive processing is also introduced. By the use of average sidelobe level as the objective function which is obtained by the sidelobe level of different interference, the sidelobe level of different interference is optimized. According to the comparison of the sidelobe level in different case of interference, the method to suppress sidelobe in various interference environment is obtained. Finally, the phased array system with concentrated subarray configuration which can be suppress sidelobe level in different interference is given.The computer simulation of the above propose methods are given, which shows the proposed method is correct and effective.