神经网络在来波到达角.doc
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神经网络在来波到达角,摘 要在导航、移动通信、雷达、电子战系统、声纳以及地震等诸多领域，测向都是一个热点问题。测向算法也被称为空间谱估计、波达方向估计、到达角估计或者方位估计。其实，所谓波到达角估计（doa）的目标就是从一系列接收的信号中（包括噪声）估计出我们所感兴趣信号的方位。在过去的几十年中，一些有效的高分辨率算法得到了很好的发展，如m...
内容介绍
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摘 要
在导航、移动通信、雷达、电子战系统、声纳以及地震等诸多领域，测向都是一个热点问题。测向算法也被称为空间谱估计、波达方向估计、到达角估计或者方位估计。其实，所谓波到达角估计（DOA）的目标就是从一系列接收的信号中（包括噪声）估计出我们所感兴趣信号的方位。
在过去的几十年中，一些有效的高分辨率算法得到了很好的发展，如MUSIC算法、ESPRIT算法。然而，这些传统的方法通常运用线性代数的方法，需要计算大量的矩阵反演，进而消耗大量的时间，因此，它们不能满足实时性的要求。
随着计算智能技术的飞速发展，人们开始研究通过学习大量的样本来解决来波到达角估计问题，而神经络因其非线性映射及泛化能力无疑被人们认为是解决这一问题的强有力工具。神经网络的优点在于建模过程是采用训练样本构造神经网络，而不再是精确的数学方程式，在实际的应用环境中，采集到的训练样本可以将噪声、信号模型、信噪比、传输通道等因素考虑进去，而无需进行特征值分解、谱峰搜索、并且能快速实现并行就算，有望应用到实际工程。
本文的主要工作包括：
1. 研究了一种基于选择性神经网络集成的单信号源来波到达角估计算法。文中通过粒子群优化算法合理选择组成神经网络集成的各个神经网络，使个体间保持较大的差异度，减小“多维共线性”和样本噪声的影响，以此建立单信号源的波到达角估计模型。仿真结果表明，该方法同BP神经网络、RBF神经网络、泛回归神经网络、MUSIC算法相比在处理单信号源的来波到达角估计时具有更好的准确性，进而有望应用在实际的定位系统中。
2. 研究了利用粒子群算法优化BP神经网络来提高DOA估计性能。传统的BP神经网络易陷入局部最优，因此采用粒子群算法对网络的权值和阈值进行优化，并将其应用到来波到达角估计中。另外，本方法仅利用阵列协方差矩阵的第一行作为来波方位特征，与常用的协方差矩阵上三角特征相比，在不损失有效方位信息的基础上使特征维数得到极大地降低。经仿真实验证明：同经典的RBF神经网络方法相比，基于本文方法的神经网络结构更简洁，泛化性能更好，来波方位估计精度更高。
3. 在不同的信噪比、阵元数目、快拍数以及信号角度间隔的变化下，比较和分析PSO-BP神经网络和经典的RBF方法进行DOA估计的性能。实验证明在同等的实验条件下，PSO-BP神经网络方法的性能明显优于RBF方法。
关键词：来波到达角估计；粒子群算法；神经网络；精确性
Abstract
Direction finding is one of the major problems for many applications such as radar, navigation, mobile communications, electronic warfare systems, sonar and seismology. Direction finding algorithms have also been known as spectral estimation, direction-of-arrival (DOA) estimation, angle of arrival (AOA) estimation, or bearing estimation. In fact, the goal of DOA estimation algorithm is to estimate the direction of the signal of interest from a collection of noise ‘‘contaminated’’ set of received signals.
Direction finding have followed an evolutionary trend. In the previous decade, some powerful and high-resolution methods for DOA estimation such as MUSIC and ESPRIT have already been developed. However, these conventional methods usually consumed a lot of time, because they use the method of linear algebra as to require the calculation of a matrix inversion. Therefore, they were not able to meet real-time requirements.
With the rapid development of computational intelligence technology, people begin to research to solve the direction of arrival estimation problem by learning a lot of samples. Neural Network is considered to be a powerful tool to solve this problem as result of the nonlinear mapping and generalization ability. The advantage of this method lies that modeling process is using the training samples to structure neural network instead of accurate mathematical equations. In practice, the collected training samples can take the noise, signal noise ratio, transmission channel model, and other factors into account, without the need for eigen value decomposition, spectral peak searching, and calculations can be fast parallel implementation, which is expected to be applied to practical engineering.
The main contributions of this paper are as follows.
1. This thesis studied a method for the direction of arrival estimation about one signal using Selective Neural Network Ensemble (SNNE). Selective Neural Network Ensemble based on Particle Swarm Optimization (PSO) is proposed to solve the direction of arrival estimation of one signal. The basic idea of the method is to optimally select Neural Network to construct Neural Network Ensemble with the aid of PSO. This may maintain the diversity of Neural Network and decrease the effect of co-linearity and noise of sample. The computer simulation shows that the method is more excellent compared with BPNN, RBFNN, GRNN and MUSIC algorithms about the one signal of DOA estimation, which makes it feasible to carry out in practical interference location system.
2. Particle swarm optimization is used for optimization of BP neural network to improve the performance of direction of arrival estimation. Due to the fact that BP neural network is inclined to be trapped in local extreme, a novel network, particle swarm optimization based BP neural network, is proposed to solve the above shortcoming, it is applied to direction of arrival estimation for study. This thesis only presents using the first row of cor..
在导航、移动通信、雷达、电子战系统、声纳以及地震等诸多领域，测向都是一个热点问题。测向算法也被称为空间谱估计、波达方向估计、到达角估计或者方位估计。其实，所谓波到达角估计（DOA）的目标就是从一系列接收的信号中（包括噪声）估计出我们所感兴趣信号的方位。
在过去的几十年中，一些有效的高分辨率算法得到了很好的发展，如MUSIC算法、ESPRIT算法。然而，这些传统的方法通常运用线性代数的方法，需要计算大量的矩阵反演，进而消耗大量的时间，因此，它们不能满足实时性的要求。
随着计算智能技术的飞速发展，人们开始研究通过学习大量的样本来解决来波到达角估计问题，而神经络因其非线性映射及泛化能力无疑被人们认为是解决这一问题的强有力工具。神经网络的优点在于建模过程是采用训练样本构造神经网络，而不再是精确的数学方程式，在实际的应用环境中，采集到的训练样本可以将噪声、信号模型、信噪比、传输通道等因素考虑进去，而无需进行特征值分解、谱峰搜索、并且能快速实现并行就算，有望应用到实际工程。
本文的主要工作包括：
1. 研究了一种基于选择性神经网络集成的单信号源来波到达角估计算法。文中通过粒子群优化算法合理选择组成神经网络集成的各个神经网络，使个体间保持较大的差异度，减小“多维共线性”和样本噪声的影响，以此建立单信号源的波到达角估计模型。仿真结果表明，该方法同BP神经网络、RBF神经网络、泛回归神经网络、MUSIC算法相比在处理单信号源的来波到达角估计时具有更好的准确性，进而有望应用在实际的定位系统中。
2. 研究了利用粒子群算法优化BP神经网络来提高DOA估计性能。传统的BP神经网络易陷入局部最优，因此采用粒子群算法对网络的权值和阈值进行优化，并将其应用到来波到达角估计中。另外，本方法仅利用阵列协方差矩阵的第一行作为来波方位特征，与常用的协方差矩阵上三角特征相比，在不损失有效方位信息的基础上使特征维数得到极大地降低。经仿真实验证明：同经典的RBF神经网络方法相比，基于本文方法的神经网络结构更简洁，泛化性能更好，来波方位估计精度更高。
3. 在不同的信噪比、阵元数目、快拍数以及信号角度间隔的变化下，比较和分析PSO-BP神经网络和经典的RBF方法进行DOA估计的性能。实验证明在同等的实验条件下，PSO-BP神经网络方法的性能明显优于RBF方法。
关键词：来波到达角估计；粒子群算法；神经网络；精确性
Abstract
Direction finding is one of the major problems for many applications such as radar, navigation, mobile communications, electronic warfare systems, sonar and seismology. Direction finding algorithms have also been known as spectral estimation, direction-of-arrival (DOA) estimation, angle of arrival (AOA) estimation, or bearing estimation. In fact, the goal of DOA estimation algorithm is to estimate the direction of the signal of interest from a collection of noise ‘‘contaminated’’ set of received signals.
Direction finding have followed an evolutionary trend. In the previous decade, some powerful and high-resolution methods for DOA estimation such as MUSIC and ESPRIT have already been developed. However, these conventional methods usually consumed a lot of time, because they use the method of linear algebra as to require the calculation of a matrix inversion. Therefore, they were not able to meet real-time requirements.
With the rapid development of computational intelligence technology, people begin to research to solve the direction of arrival estimation problem by learning a lot of samples. Neural Network is considered to be a powerful tool to solve this problem as result of the nonlinear mapping and generalization ability. The advantage of this method lies that modeling process is using the training samples to structure neural network instead of accurate mathematical equations. In practice, the collected training samples can take the noise, signal noise ratio, transmission channel model, and other factors into account, without the need for eigen value decomposition, spectral peak searching, and calculations can be fast parallel implementation, which is expected to be applied to practical engineering.
The main contributions of this paper are as follows.
1. This thesis studied a method for the direction of arrival estimation about one signal using Selective Neural Network Ensemble (SNNE). Selective Neural Network Ensemble based on Particle Swarm Optimization (PSO) is proposed to solve the direction of arrival estimation of one signal. The basic idea of the method is to optimally select Neural Network to construct Neural Network Ensemble with the aid of PSO. This may maintain the diversity of Neural Network and decrease the effect of co-linearity and noise of sample. The computer simulation shows that the method is more excellent compared with BPNN, RBFNN, GRNN and MUSIC algorithms about the one signal of DOA estimation, which makes it feasible to carry out in practical interference location system.
2. Particle swarm optimization is used for optimization of BP neural network to improve the performance of direction of arrival estimation. Due to the fact that BP neural network is inclined to be trapped in local extreme, a novel network, particle swarm optimization based BP neural network, is proposed to solve the above shortcoming, it is applied to direction of arrival estimation for study. This thesis only presents using the first row of cor..
