摘要
针对于序列二次规划算法在求解信杂比限定下的最大互信息雷达波形设计模型时,在计算时受寻优初始值的影响较大,并且极易陷入局部最优的问题,提出了一种基于粒子群序列二次规划算法的波形自适应设计技术。该算法将粒子群算法作为全局搜索算法,序列二次规划算法作为局部搜索算法,将粒子群算法的全局性和序列二次算法的精确性二者有效结合起来,实现对目标模型的求解。通过MATLAB仿真实验的结果可以看出,该算法能够有效对目标设计模型进行求解,且得到的结果能够有效提升序列二次规划算法的求解精度及收敛速度。
When solving the maximum mutual information radar waveform design model under the constraint of signalto-noise ratio (SNR), the sequential quadratic programming (SQP) algorithm is greatly influenced by the initial value of optimization, and is easily trapped in the local optimum. In order to solve this problem, this paper proposes an adaptive waveform design technique based on the particle swarm optimization (PSO)-SQP algorithm. It takes PSO as the global search algorithm, and SQP as the local search algorithm, and effectively combines the overall importance of PSO and the accuracy of SQP to solve the target model. From the results of MATLAB simulation experiment, the algorithm can effectively solve the target design model, and the results obtained can effectively improve the accuracy and convergence rate of the SQP algorithm.
作者
张羽鑫
刁鸣
ZHANG Yuxin;DIAO Ming(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
出处
《应用科技》
CAS
2019年第5期39-44,共6页
Applied Science and Technology
基金
国家自然科学基金项目（KY10800150057）
关键词
认知雷达
波形自适应设计
序列二次规划算法
粒子群算法
信杂比
互信息
波形
cognitive radar
waveform adaptive design
sequential quadratic programming algorithm
particle swarmoptimization algorithm
SNR
mutual information
waveform
