波达方位估计中基于SML精确解空间特征及膜计算的低复杂度算法研究

波达方位估计是阵列信号处理中一个非常重要的研究方向，广泛应用于雷达、声纳、现代通信系统等等。随机最大似然估计（SML）为其中一个非常重要的算法，因其在理论上有着最优的性能。然而SML算法受制于：1）代价函数本身计算复杂；2）解析算法涉及多维非线性最优化问题；导致其整体计算复杂度高。从而限制了这一优秀算法在实际系统中的应用。针对以上2个方面的问题，本研究在前期对SML精确解空间充分研究的基础上，拟提出：1）代价函数方面：从传统SML代价函数和精确SML代价函数两个方面着手，研究如何以较低的计算复杂度确保得到SML的精确解。2）解析算法方面：利用膜计算并行式、分布式高效的计算特点，对SML解空间进行膜划分、进化规则以及交流机制定义，探索出一种低复杂度的解析算法。研究成果将大幅度降低SML算法的整体计算复杂度，并为其在实际系统中的应用提供理论与关键技术支持。

The Direction-of-Arrival (DOA) is a basic and important problem in sensor array signal processing, which has wild applications in radar, sonar, communication systems and so on. The Stochastic Maximum Likelihood (SML) algorithm is one of the most important solving methods for DOA because it has the best performance in theory. However, the SML algorithm has the following two defects: 1) the SML Criterion is too complicate; 2) the solving algorithm for SML estimation usually needs high computational complexity because it involves a multi-dimensional non-linear optimization problem. To overcome these two defects, based on the previous research of the exact solution space of SML, we would like to propose the following two research points. 1) In the aspect of SML Criterion: we propose a low complexity method to get the exact solution of SML through conventional SML Criterion and Exact SML Criterion. 2) In the aspect of solving technique: we will explore a low complexity membrane computing algorithm for SML estimation through membrane division and definition of evolution rule and communication mechanism. The feature of this algorithm is parallel and distributed computing. Therefore, it will be greatly efficient. The computational complexity of SML will be greatly reduced through the two research points above. The findings of our research can be used to provide theoretical basis and key technology for the realization of SML algorithm.