阵元位置误差影响下的神经网络测向

针对阵元位置误差情况下RBF(radial-basis function)神经网络波达方向估计,提出一种直接数据域补偿算法。这种算法通过对神经网络的输入数据进行误差补偿来获得正确的训练样本,因而在无需对RBF神经网络测向系统作任何改进的情况下,可获得对波达方向的准确估计。文中给出了应用该方法的具体步骤,通过仿真实验证明该算法的有效性。     

Performance of radial-basis function networks for direction ofarrival estimation with antenna arrays

The problem of direction of arrival (DOA) estimation of mobile users using linear antenna arrays is addressed. To reduce the computational complexity of superresolution algorithms, e.g. multiple signal classification (MUSIC), the DOA problem is approached as a mapping which can be modeled using a suitable artificial neural network trained with input output pairs. This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and correlated signals. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations     

An adaptive moving target tracking algorithm for smart antenna systems

In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations （MS） degrade the performance of the conventional Direction-Of-Arrival （DOA） tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood （ML） principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algorithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as meas urements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better performance of DOA estimation and tracking of MS than the conventional ML or subspace based algorithms in terms of accuracy and robustness.     

Direction of arrival estimation based on phase differences usingneural fuzzy network

A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attack these problems, neural networks have become popular for DOA estimation. However, the normal neural networks such as the multilayer perceptron (MLP) and radial basis function network (RBFN) usually produce the extra problems of low convergence speed and/or large network size (i.e., the number of network parameters is large). Also, the may to decide the network structure is heuristic. To overcome these defects and take use of neural learning ability, a powerful self-constructing neural fuzzy inference network (SONFIN) is used to develop a new DOA estimation algorithm. By feeding the PDs of the received radar-array signals, the trained SONFIN can give high-resolution DOA estimation. The proposed scheme is thus called PD-SONFIN. This new algorithm avoids the need of empirically determining the network size and parameters in normal neural networks due to the powerful on-line structure and parameter learning ability of SONFIN. The PD-SONFIN can always find itself an economical network size in the fast learning process. Our simulation results show that the performance of the new algorithm is superior to the RBFN in terms of convergence accuracy, estimation accuracy, sensitivity to noise, and network size     

基于MUSIC算法对相干信号DOA估计的研究

空间谱估计是阵列信号处理中的一个重要研究课题。针对经典MUSIC算法在入射源相关的情况下测向失败的问题,通过前后向空间平滑MUSIC算法,利用子阵平滑恢复数据协方差矩阵的原理进行解相干,进而对入射源的波达方向（DOA）进行估计。仿真结果证明：前后向空间平滑算法不仅能估计出空间相互独立信号源的波达方向,还能有效分辨出相干信源的到达角度,且具有较高的分辨能力和估计精度。     

利用深度学习方法的相干源DOA估计

基于子空间分解的相干信源DOA ( Direction of arrival) 估计算法对阵列有特殊的要求，且估计性能较差，在低信噪比时甚至失效；另外，基于压缩感知的DOA估计算法在高信噪比下可以实现相干源的DOA估计，但计算复杂度较高。针对这些不足，本文基于稀疏表示的阵列接收信号模型，提出一种基于深度学习的相干源DOA估计方法，该方法利用卷积网络和全连接网络构造了深度学习网络，并通过选择合适的训练策略，对网络进行了有效训练，利用训练好的深度学习网络能够对相干源进行有效的DOA估计。仿真实验表明，与现有的相干源DOA估计算法相比，本文提出的方法适合于任意阵列结构，在时间复杂度上有着明显的优势，在估计性能上优于平滑解相干和L1-SVD（Sigular Value Decomposition）算法，略差于OGSBI（Off-Grid Sparse Bayesian Inference）算法。      

相干分布源DOA与角度扩展估计求根算法

论文提出了一种具有低复杂度的相干分布源波达方向和角度扩展估计算法。该算法将点源模型中的求根MUSIC算法推广应用至分布源模型。利用空间频率下的相干分布源广义方向矢量可以表示成参数去耦形式的结构特点,并根据相干分布源的角信号密度函数,构造参数估计的多项式求根形式,然后通过交替迭代的求根方法得到分布源的中心波达方向和角度扩展的估计值。该算法参数估计性能与DSPE算法相当,其计算复杂度要远小于DSPE算法,并且适用于不同分布类型的相干分布源同时存在的情况。计算机仿真验证了算法的性能。      

SWEDE算法提高模式空间内DOA估计抗噪声能力

均匀测向圆阵相干信号DOA估计在模式空间内将阵列流形化为线性结构后 ,一般情况下都是先进行空间平滑去相干处理后再运用MUSIC算法进行DOA估计 ,文中运用SWEDE算法代替MUSIC算法进行了模式空间内的DOA估计 ,仿真结果证明SWEDE算法更好地适应了均匀圆阵模式空间内DOA估计数据矩阵的特点 ,使系统抗噪声能力、测向精度、分辨率均得到较大提高     

基于单快拍极化平滑处理的DOA跟踪算法

该文研究了基于电磁矢量传感器阵列的相干信号波达方向(DOA)跟踪问题。首先,对一种增强阵列有效孔径的单快拍极化平滑估计算法(SSPSA)的解相干性能进行了分析,此算法可以用于相干信号的DOA快速估计。在此基础上,提出了一种基于迭代的相干信号DOA跟踪算法,此算法无需奇异值分解和矩阵求逆运算,具有较快的跟踪速度。仿真实验验证了算法的有效性。     

脉冲噪声下基于相关熵的相干分布源DOA估计新方法

针对复杂电磁环境下被动无线监测定位问题,该文提出广义相关熵的概念,推导了广义相关熵的性质,用以抑制阵列输出信号中的脉冲噪声。为了实现脉冲噪声环境下相干分布源中心DOA和扩散角的联合估计,提出基于广义相关熵的DOA估计新方法,并证明了该方法的有界性。为进一步提升算法的鲁棒性,推导了一种仅依赖阵列输出信号的自适应核函数。仿真结果表明,该算法能够实现脉冲噪声环境下相干分布源参数的联合估计,相比已有算法,具有更高的估计精度和鲁棒性。     

采用混合核支持向量机的DOA估计

人工神经网络( ANN)进行建模时通常需要准备大量的数据样本,同时网络结构一般都比较复杂;而采用支持向量机( SVM)进行建模时,不同核函数有不同的效果,各有利弊,且选取SVM模型参数的理论支撑尚不完整。为了解决这些问题,提出了一种基于混合核函数的支持向量机来改善来波到达角( DOA)的估计性能,并结合二进制粒子群算法( PSO)来对混合核函数进行参数寻优。该混合核函数由全局核函数和局部核函数构成,提高了SVM的泛化能力和学习能力。首先通过拟合多项式函数,验证了该混合核SVM的有效性。将该方法用于DOA估计建模,在不同信噪比和快拍数下,通过与径向基函数( RBF)神经网络、基于各单一核函数的SVM和MUSIC算法预测结果对比,混合核SVM均方差有所降低,提高了DOA估计的精度且有更好的稳定性。     

相干声波信号DOA单快拍矢量平滑估计与跟踪算法

本文研究了基于声矢量传感器阵列的相干信号波达方向(DOA)估计和跟踪问题.首先,根据中心对称均匀线阵方向矩阵的平移不变特性,提出了一种增强阵列有效孔径的单快拍矢量平滑估计算法(PVFSIA),该算法可以用于相干信号的DOA快速估计.在此基础上,提出了基于迭代的相干信号DOA跟踪算法,该算法无需奇异值分解和矩阵求逆运算,...     

Ambiguity-function-based techniques to estimate DOA of broadband chirp signals

Various methods are available to perform direction-of-arrival (DOA) estimation for random sources. However, the work on DOA estimation of deterministic sources, such as broadband chirp signals, is quite limited. This paper proposes two novel methods for broadband chirp DOA estimation (BCD), namely the incoherent broadband chirp DOA estimation (BCD-I) and coherent broadband chirp DOA estimation (BCD-C). The proposed methods exploit the time-frequency structure of the chirp signal via the ambiguity function, which converts the absolute time and frequency of the chirp signal into relative time lag and frequency difference. The algorithms can be applied to arrays of any aperture size and arbitrary chirp rate signals. The signal frequency of the source can be higher than the conventional array design frequency, and the number of sources can be greater than the number of sensors as long as the signals are separable in the ambiguity function plane. These methods can be applied to single or multiple chirps with the same or different chirp rates. The performance analysis shows that our algorithms generally provide improvements in the DOA estimation of the broadband chirp sources.     

DOA estimation in multipath: an approach using fourth-ordercumulants

We consider the problem of direction of arrival (DOA) estimation in the presence of multipath propagation. The sensor elements are assumed to be linear and uniformly spaced. To perform DOA estimation, we combine two existing algorithms which are often used for other purposes. The first algorithm exploits fourth-order cumulants to perform blind estimation of the steering vectors and the second algorithm estimates the directions of arrival using the estimated vectors. We refer to this two step approach as the SV-DOA estimation algorithm. We also present an algorithm independent performance analysis for the DOA estimation problem based on fourth-order cumulants. We summarize the algorithms and present Monte-Carlo simulations demonstrating the effectiveness of the SV-DOA algorithm as well as verifying the performance analysis using an optimal (but computationally expensive) DOA estimation algorithm     

一种快速分布式目标波达方向估计新方法

在现有的分布式目标波达方向估计方法中,绝大部分算法需要一维或二维搜索,而在不需搜索的特征分解类算法中,其DOA估计精度又受快摄数积累的影响。文中在对分布式目标的角信号密度函数进行合理假设基础上,利用波达方向参数在超完备基空间上的稀疏表示,在单次快摄情况下即可给出分布式目标中心波达方向的快速估计,算法在分布式目标快变环境下尤为有效。仿真结果证明了方法的有效性及可行性。     

基于互相关抽样分解的分布式非圆信号DOA快速估计

在非相干分布式非圆信号波达方向(DOA)估计中,针对利用信号非圆特性后输出矩阵维数扩展带来的较大运算量问题,该文提出一种基于互相关抽样分解的DOA快速估计算法。该算法仅需要从子阵间的扩展互相关矩阵中抽样出少量行元素和列元素,构成两个低维子矩阵,进而通过低秩近似分解便可快速地同时求出左右奇异矢量,即分别对应两个子阵的信号子空间,避免了计算整个互相关矩阵及其奇异值分解运算;最后利用两个子阵信号子空间的旋转不变性通过最小二乘得到DOA估计。仿真分析表明,当行列抽样数大于信源数的两倍时,所提算法与直接基于互相关矩阵奇异值分解的非相干分布式非圆信号DOA估计算法性能相近,但复杂度得到了大幅度降低;而相比于传统的低复杂度非相干分布源DOA估计算法,所提算法利用信号非圆特性具有更高的估计性能。     

Direction-of-arrival tracking scheme for DS/CDMA systems: direction lock loop

In this paper, a new direction-of-arrival (DOA) tracking algorithm, direction lock loop (DiLL), is proposed for wireless direct-sequence code-division multiple-access systems. It has a similar concept to the delay lock loop that is used for timing synchronization. It may track the DOA of sources by iterations. Its computational requirements are NK+O(K) for coherent DiLL and 2NK+O(K) for noncoherent DiLL, where N is the number of antenna elements and K is the number of signal sources, which is less than that of the projection approximation subspace tracking with deflation algorithm. The DOA tracking accuracy is demonstrated by analysis and computer simulations.     

基于RBF神经网络的相关干涉仪测向方法

提出了一种新的基于径向基(RBF)神经网络的相关干涉仪测向方法,实现了自组织学习选取中心、正交最小二乘法及基于遗传算法的进化优选算法等训练方法,经训练后的RBF神经网络可用于多源信号波达角(DOA)估计。仿真结果表明,在一定范围内,该方法对信道噪声不敏感,测向精度与传统相关干涉仪相当,且测向处理时间和测向设备的存储量大大降低。     

Robust maximum likelihood bearing estimation in contaminatedGaussian noise

A robust maximum likelihood (ML) direction-of-arrival (DOA) estimation method that is insensitive to outliers and distributional uncertainties in Gaussian noise is presented. The algorithm has been shown to perform much better than the Gaussian ML algorithm when the underlying noise distribution deviates even slightly from Gaussian while still performing almost as well in pure Gaussian noise. As with the Gaussian ML estimation, it is still capable of handling correlated signals as well as single snapshot cases. Performance of the algorithm is analyzed using the unique resolution test procedure which determines whether a DOA estimation algorithm, at a given confidence level, can resolve two dominant sources with very close DOAs     

DS/CDMA系统上行链路一种稳健的阵列接收方法

W G.Hou等对DS/CDMA移动通信系统上行链路提出了通过估计空间到达角（DOA）而进行波束形成的空间滤波方法,本文首先对该方法进行了从对移动台的初始DOA定位到跟踪的大量改进,使该方法对期望用户的跟踪和对干扰用户进行空域抑制更为准确和有效。然后将算法与导频位辅助LMS-DRMTA算法进行了比较,该方法比导频位辅助LMS-DRMTA算法具有更小的运算量,更好的稳定性,当扇区激活用户数较大时,该方法还具有更低的误码率（BER）,该阵列接收法更适合大容量的移动通信系统。