DOA estimation based on an improved hybrid PSOGSA approach for CDMA system

Jhih-Chung Chang, Po-Ting Chen
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Abstract

In this paper, we consider the problem of estimating the direction-of-arrival (DOA) of code-division multiple access (CDMA) signals. It has been shown that the searching complexity and estimating accuracy of the conventional spectral searching methods strictly depend on the number of search grids used during the search. It is time consuming and the required number of search grids is not easy to determine. As proposed in this paper, a hybrid population-based algorithm (PSOGSA) is proposed with the combination of particle swarm optimization (PSO) and gravitational search algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. However, the proposed technique offers a much faster convergence compared to the PSO. For the purpose to increase the estimation accuracy, we also propose an improved PSOGSA with adaptive multiple velocity, which depend on first-order Taylor series expansion of the objective function. In conjunction with an improved PSOGSA for angle searching, the proposed approach can achieve the advantages of reducing search complexity and more accurate estimate over existing conventional spectral searching method. Finally, several computer simulation examples are provided for illustration and comparison.
基于改进混合PSOGSA方法的CDMA系统DOA估计
本文研究了码分多址(CDMA)信号的到达方向(DOA)估计问题。研究表明,传统谱搜索方法的搜索复杂度和估计精度严格依赖于搜索网格的个数。这种方法耗时长,而且所需的网格数不易确定。本文将粒子群优化算法(PSO)与引力搜索算法(GSA)相结合,提出了一种基于种群的混合算法(PSOGSA)。其主要思想是将粒子群算法的挖掘能力与粒子群算法的探索能力相结合,综合两种算法的优势。然而,与PSO相比,所提出的技术提供了更快的收敛速度。为了提高估计精度,我们还提出了一种基于目标函数一阶泰勒级数展开的自适应多速度改进PSOGSA。结合改进的PSOGSA进行角度搜索,与现有的传统谱搜索方法相比,该方法具有降低搜索复杂度和提高估计精度的优点。最后,给出了几个计算机仿真实例进行说明和比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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