Numerical simulation of multi output radar orthogonal waveform based on chaos optimization algorithm

Aiming at the problems of low probability of interception and poor anti-jamming performance of multi output radar, the numerical simulation of orthogonal waveform of multi output radar based on chaos optimization algorithm is proposed. Firstly, chaotic frequency coding is applied to multi output radar signal, and different frequency modulation is applied to different sub pulse. At the same time, in view of the low efficiency of numerical simulation algorithm in large space and high dimension optimization, GASA algorithm is used to increase the diversity of chaotic optimization algorithm process. According to the specific working mode, the initial phase of each cycle of multi output radar orthogonal waveform is obtained, and the number of numerical simulation of multi output radar orthogonal waveform is established Model. The experimental results show that the proposed method can improve the radar energy utilization and the signal-to-noise ratio of the echo signal, allocate the transmitting energy reasonably, and keep the structural stability of LFM signal frequency changing continuously with time.

Multiobjective Parallel Chaos Optimization Algorithm with Crossover and Merging Operation

Chaos optimization algorithm (COA) usually utilizes chaotic maps to generate the pseudorandom numbers mapped as the decision variables for global optimization problems. Recently, COA has been applied to many single objective optimization problems and simulations results have demonstrated its effectiveness. In this paper, a novel parallel chaos optimization algorithm (PCOA) will be proposed for multiobjective optimization problems (MOOPs). As an improvement to COA, the PCOA is a kind of population-based optimization algorithm which not only detracts the sensitivity of initial values but also adjusts itself suitable for MOOPs. In the proposed PCOA, crossover and merging operation will be applied to exchange information between parallel solutions and produce new potential solutions, which can enhance the global and fast search ability of the proposed algorithm. To test the performance of the PCOA, it is simulated with several benchmark functions for MOOPs and mixed H2/H∞ controller design. The simulation results show that PCOA is an alternative approach for MOOPs.

Group Formation Basd on SATC-ALO and SOM Neural Network

Firstly, the problem of group-air grouping is analyzed to introduce the aircraft attribute grouping model and aircraft fuel consumption grouping model. Then, SATC-ALO optimized by Chaos optimization algorithm and Tournament Selection strategy and SOM neural network are used to solve the formation grouping model. Finally, comparative experiments of similarity calculation method and formation grouping method were performed with 50 groups of data. The experimental results show that hybrid method is superior to Euclidean distance method. SATC-ALO algorithm has the highest grouping accuracyand meets the real-time requirements. However, the number of groups needs to be specified in advance. The accuracy of SOM neural network grouping is slightly lower than SATC-ALO algorithm, but the grouping time is lower than SATC-ALO algorithm, and there is no need to specify the number of groups. Both SOM neural network and SATC-ALO algorithm can perfectly solve the problem of group-air grouping and have practical application value.