摘要
基于最大似然估计(ML)的阵列测向方法具有测向精度高、可以分辨相干信号等优点,但是因为计算复杂度过高而工程应用受限。针对该问题,利用交叉熵(CE)方法对最大似然估计快速求解,并对初始样本的产生和平滑参数的设置进行了优化,提出改进型CE-ML二维测向算法,最后进行了算法运算量分析和仿真验证。仿真实验表明,在精度相近条件下,改进型的CE-ML算法的迭代次数大约是粒子群算法(PSO)的1/3,大大减少了ML测向的计算量。
The array DOA estimator method of maximum likelihood （ML） has high precision and can resolve the coherent signal. The application of maximum likelihood has been restricted by the computation burden. In order to overcome the problem existing in the majority of solution of maximum likelihood estimation, the cross entropy （CE） method is used. Through the optimization of the smooth parameter and the initial sample, a new improved CE-ML algorithm of 2-D DOA estimator is proposed. Comparing with the PSO （Particle Swarm Optimization） algorithm, simulation results show that the improved CE-ML algorithm performs better, and iteration number of CE-ML algorithm is one-third of PSO algorithm.
出处
《电子信息对抗技术》
2011年第5期1-4,共4页
Electronic Information Warfare Technology
