基于限定PSO初始化空间的随机最大似然算法

针对随机最大似然算法(SML)在波达方位(DOA)估计中由于多维非线性优化导致计算复杂度大的问题,提出一种限定粒子群(PSO)算法搜索空间的SML算法。该算法克服了一个缺陷,即在采用ESPRIT算法限定PSO初始化空间时,在阵列结构是非均匀线性阵列而且信号是相干信号时ESPRIT算法不能直接处理信号,且需要采用一组预处理技术,这增加了算法计算的复杂度。提出的算法的关键之处在于采用假设技术确定初始化点来代替ESPRIT算法的解,结合克拉美罗界(CRB)确定PSO算法的初始化解空间。这一方法不必再采用预处理技术,且利用限定PSO初始化空间的算法大大降低了SML算法的计算复杂度。实验结果表明,提出的算法为相干情况和非相干情况都提供了相当好的初始值。最后,将该算法与许多现有算法进行比较,验证提出算法的有效性和准确性。

A Random Maximum Likelihood Algorithm Based on Limited PSO Initial Space

Aiming at the problem of large computational complexity due to multidimensional nonlinear optimization in the DOA estimation of the random maximum likelihood algorithm, a SML algorithm is proposed to limit the search space of particle swarm optimization algorithm. This algorithm overcomes a defect that when the ESPRIT algorithm defines the initial space of PSO, the signal cannot be directly processed by ESPRIT algorithm when the array structure is a non-uniform linear array and the signal is a coherent signal, and we need to adopt a set of preprocessing techniques, which increases the complexity of algorithm calculation. The key point of the proposed algorithm is to use the hypothesis technique to determine the initialization point instead of the solution of the ESPRIT algorithm, the initial solution space of the PSO algorithm is determined by combining the CRRAM. This method eliminates the need for preprocessing techniques, and the algorithm that defines the PSO initialization space greatly reduces the computational complexity of the SML algorithm. Experimental results show that the proposed algorithm provides fairly good initial values for both coherent and incoherent cases. Finally, the proposed algorithm is compared with many existing algorithms, and the validity and accuracy of the proposed algorithm are verified.