针对最大似然(maximum likelihood,ML)方位估计方法多维非线性搜索计算量大的问题,将连续空间蚁群算法与最大似然算法相结合,提出基于蚁群算法的最大似然(ant colony optimization based maximum likelihood,ACOML)估计新方法.该方法将传统蚁群算法中的信息量留存过程拓展为连续空间的信息量高斯核概率密度函数,得到最大似然方位估计的非线性全局最优解.仿真结果表明,ACOML方法保持了原最大似然方位估计方法算法的优良估计性能,而计算量只是最大似然方法的1/15.%A new maximum likelihood direction of arrival (DOA) estimator based on ant colony optimization (ACOML) is proposed to reduce the computational complexity of multi-dimensional nonlinear existing in maximum likelihood (ML) DOA estimator. By extending the pheromone remaining process in the traditional ant colony optimization into a pheromone Gaussian kernel probability distribution function in continuous space, ant colony optimization is combined with maximum likelihood method to lighten computation burden. The simulations show that ACOML provides a similar performance to that achieved by the original ML method, but its computational cost is only 1/15 of ML.
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