Array Pattern Design Based On Ant Lion Optimizer

Array pattern synthesis is to optimize the number,position and weight of array elements to get low sidelobe and deep null pattern.It is widely used in radar,communication,sonar and other fields.Although all kinds of existing intelligent optimization algorithms have achieved some results in array pattern design,they are still prone to local convergence.This thesis focuses on the optimization design of array pattern,the work carried out in the thesis is as follows:1.To solve the problem of sparse array optimization of MIMO radar,firstly,ant lion optimizer is used to optimize the elements of transmitting array and receiving array of MIMO radar.The positions of transmitting elements and receiving elements are taken as optimization variable,and the peak sidelobe level is taken as the objective function.The simulation results show that the sidelobe of array pattern is optimized.At the same time,this thesis studies the effect of the number of effective virtual elements of the array on the degree of freedom of the radar.The number of effective virtual elements is also related to the positions of the transmitting and receiving elements.The positions of transmitting elements and receiving elements are taken as optimization variables,the peak sidelobe of the pattern and the number of effective array elements are normalized and transformed into a single objective optimization problem.The ant lion optimization algorithm is compared with other algorithms.The simulation results show that the ant lion optimization algorithm has advantages in solving the sparse array optimization problem.The number of elements used in the array is reduced,the hardware cost of radar is reduced,and the peak side lobe level of radar is reduced,and the degree of freedom of radar is improved.2.To solve the problem of sub-array division of large phased array radar,this thesis improves the ant lion optimization algorithm to enhance the optimization effect of ant lion optimization algorithm.Firstly,the collision avoidance factor and elimination mechanism are added to the ant lion optimization algorithm to improve the efficiency of the search process and the defect of local convergence.Then,under the constraint of fixed total number of array elements and divided number of subarrays,the number of array elements in each subarray and the amplitude weight of each subarray are taken as optimization variables,and the peak side lobe level and null depth are taken as optimization objective functions.The improved ant lion optimization algorithm is used to optimize the pattern of subarray,and compared with other algorithms.The simulation results show that the improved ant lion optimization algorithm proposed in this thesis has advantages in solving the problem of subarray division of large array,and realizes the nonuniform and nonoverlapping subarray division of large array.The sidelobe level obtained is lower than other algorithms,and the null depth is deeper than other algorithms.3.To solve the problem of multi-objective optimization of array pattern,a multi-objective ant lion optimization algorithm based on decomposition mechanism is proposed.Firstly,the decomposition mechanism of multi-objective evolutionary algorithm based on decomposition is analyzed,and then the optimization steps of ant lion optimization algorithm are added into the framework of multi-objective evolutionary algorithm based on decomposition,and the advantages of ant lion optimization algorithm are used to find a better solution in the neighborhood of the solution.Then,the influence of nulling on the anti-jamming performance of the pattern is analyzed,and multiple nulling directions and wide nulling regions are set.Finally,the array element amplitude weight is taken as the optimization variable,and the peak sidelobe level and the depth of multiple nulling or wide nulling region are taken as the optimization objective function.The multi-objective optimization design of the array pattern is realized and compared with other multi-objective optimization algorithms comparative analysis.According to the simulation results,the proposed algorithm can achieve good results in solving multi-objective optimization problems.It can not only reduce the peak sidelobe of the pattern,but also form deeper nulling in the desired nulling angle or nulling area to suppress interference.The optimization performance of the algorithm proposed in this thesis is better than other multi-objective optimization algorithms.Based on the ant lion optimization algorithm,this thesis proposes improved ant lion optimization algorithm and multi-objective ant lion optimization algorithm.The simulation results show that the algorithm in this thesis is better than other algorithms in solving the problem of array pattern design,and provides an effective optimization method for array pattern design.