A Novel Modification of PSO Algorithm for SML Estimation of DOA.
A Novel Modification of PSO Algorithm for SML Estimation of DOA.
复制标题
一种改进的PSO算法用于SML DOA估计
DOI：
10.3390/s16122188
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发表时间：
2016-12-19
期刊：
影响因子：
--
通讯作者：
Suzuki M
中科院分区：
文献类型：
--
作者：
Chen H;Li S;Liu J;Liu F;Suzuki M
本文解决了降低到达方向 (DOA) 的随机最大似然 (SML) 估计的计算复杂性的问题。 SML算法在传感器阵列信号处理中以其高精度的DOA估计而闻名。然而，由于SML准则的估计是一个多维非线性优化问题，其计算复杂度非常高。因此，SML算法很难应用到实际系统中。粒子群优化（PSO）算法被认为是解决 DOA 估计中多维非线性优化问题的一种相当有效的方法。然而，传统的PSO算法存在两个缺陷，即粒子数过多和迭代次数过多。因此，使用传统PSO算法进行SML估计的计算复杂度还是有点高。为了克服这两个缺陷并进一步降低计算复杂度，本文提出了一种对 SML 估计的传统 PSO 算法的新颖修改，我们将其称为联合 PSO 算法。该修改的核心思想在于，它利用旋转不变技术估计信号参数（ESPRIT）和随机克拉默-饶界（CRB）的解来确定新的初始化空间。由于这个初始化空间已经接近SML的解，因此需要更少的粒子和更少的迭代次数。结果，可以大大降低计算复杂度。在仿真中，我们将所提出的算法与传统的PSO算法、经典的改变最小化（AM）算法和遗传算法（GA）进行了比较。仿真结果表明，我们提出的算法是最有效的求解算法之一，显示了SML在实际系统中应用的巨大潜力。
This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.
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影响因子：
5.4
作者：
Stoica, Petre;Babu, Prabhu;Li, Jian
通讯作者：
Li, Jian
DOI：
10.1109/29.32276
发表时间：
1989-07-01
期刊：
IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING
影响因子：
--
作者：
ROY, R;KAILATH, T
通讯作者：
KAILATH, T
影响因子：
5.4
作者：
WAX, M
通讯作者：
WAX, M
影响因子：
6.8
作者：
Kuchar, A;Tangemann, M;Bonek, E
通讯作者：
Bonek, E
影响因子：
4.4
作者：
Li, MH;Lu, YL
通讯作者：
Lu, YL