菲涅尔区聚焦多波束天线空间谱估计性能分析

首先分析了基于菲涅尔区聚焦多波束天线的信号处理特点,在与基于均匀线形阵的空间谱估计性能比较的基础上,讨论了菲涅尔区聚焦多波束天线波达方向估计的性能;最后利用多信号分类法进行波达方向估计的计算机模拟,并定量地分析了数值结果,给出了一些有意义的结论。     

Source localization for multiple speech sources using low complexity non-parametric source separation and clustering

This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity non-parametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.     

Complex-valued ICA utilizing signal-subspace demixing for robust DOA estimation and blind signal separation

This paper deals with direction of arrival (DOA) estimation and blind signal separation (BSS) based on independent component analysis (ICA) with robust capabilities. An efficient demixing procedure of complex-valued ICA is presented here, which combines the signal-subspace demixing procedure exploiting individual signal-subspace projection and Newton’s iteration algorithm based on maximization of the approximate negentropy of non-Gaussian signal for array signal processing. It resolves the problems of order ambiguity and identifiability of traditional ICA for time-domain BSS. The proposed method could be directly applied to radar, sonar, radio surveillance, and communications systems for separating signals and estimating relative DOAs of signals. Several computer simulation examples for perturbations to the array manifold, unknown noise environments, and Rayleigh fading channel are provided to illustrate the effectiveness of the proposed method.     

Two-dimensional DOA estimation of coherent signals using acoustic vector sensor array

In this paper, we present two new methods for estimating two-dimensional (2-D) direction-of-arrival (DOA) of narrowband coherent (or highly correlated) signals using an L-shaped array of acoustic vector sensors. We decorrelate the coherency of the signals and reconstruct the signal subspace using cross-correlation matrix, and then the ESPRIT and propagator methods are applied to estimate the azimuth and elevation angles. The ESPRIT technique is based on the shift invariance property of array geometry and the propagator method is based on partitioning of the cross-correlation matrix. The propagator method is computationally efficient and requires only linear operations. Moreover, it does not require any eigendecomposition or singular-value decomposition as for the ESPRIT method. These two techniques are direct methods which do not require any 2-D iterative search for estimating the azimuth and the elevation angles. Simulation results are presented to demonstrate the performance of the proposed methods.     

虚拟阵列变换解到达角模糊研究

在进行宽频段波达方向估计时,频率高端可能会出现阵元间距大于半波长的情况,从而会引起角度模糊,无法分辨出真实的波达方向。本文利用虚拟阵列变换技术,使存在角度模糊的阵列虚拟为一个阵元间距较小的虚拟阵列,该虚拟阵列的阵元间距小于最高频率的半波长,使其满足无模糊阵列的条件。然后利用虚拟后的阵列进行到达角估计,得到无模糊的到达角估计值。仿真实验验证了本文方法在频率高端能有效地解决多值模糊的问题,具有良好的适应性和实用性。     

基于稀疏贝叶斯学习的空间紧邻信号DOA估计算法

离格(off-grid)波达方向(DOA)估计解决的是实际DOA和假设网格点的失配问题。对于空间紧邻信号的DOA,稀疏的网格点会导致精度和分辨率的下降,密集的网格点虽然可以提高估计精度却显著增加计算负担。针对此问题,该文提出基于稀疏贝叶斯学习(SBL)的空间紧邻信号DOA估计算法,主要包括3个步骤。首先,通过最大化阵列输出的边缘似然函数,推导了信号在拉普拉斯先验下的新不动点迭代方法,进行超参数的预估计,相比其他经典SBL算法提高了收敛速度;其次,利用新网格插值方法优化网格点集,并二次估计噪声方差和信号功率以分辨空间紧邻信号的DOA;最后,推导了似然函数关于角度的最大化公式以改进离格DOA搜索。仿真表明该算法比其他经典SBL类算法对空间紧邻信号的DOA具有更高的精度和分辨率,同时有计算效率的提升。     

双重不变ESPRIT

ESPRIT利用了阵列在一个方向上的平移不变性,从而可估计信号的一个方向。该文利用3个相互位移的子阵列所具有的双重不变性质,从而估计出了信号的两个方向,并且计算量较小,两个方向角自然配对,对子阵列的内部结构也没有要求,因此可以选择适当的子阵列结构以获得良好的估计效果。     

A 2D adaptive beamforming method in sparse array

This study presents an Accelerated Proximal Gradient singular value thresholding based Linearly Constrained Singular Canceler (APG-LCSC) algorithm to improve the performance of two-dimensional (2D) adaptive beamforming in sparse array. Firstly, a signal model is established based on Matrix Completion (MC), which can be proved to meet Null Space Property (NSP). Subsequently, left and right singular value vector of the received signal matrix is achieved via MC, and then a linearly constrained singular canceler is obtained by employing the singular value vector. The proposed algorithm decreases the required number of antennas, lowers the computational complexity and has a robust performance. Computer simulations demonstrate that the proposed APG-LCSC algorithm can acquire high accuracy of 2D beamforming in sparse array.     

Frequency-Azimuth-Elevation Determination with a Single Acoustic Vector-Sensor Involved in a Reflecting Boundary

The temporally regularized multiresolution ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is proposed to exploit the second-order information present in the superposition of incidence of direct and reflected monochromatic acoustic waves for uni-vector-sensor direction finding in the presence of a reflecting boundary. The data are organized such that the phase shifts for frequency estimation of each signal corresponding to two distinct sampling rates are separately embedded in the modulus and argument of the ESPRIT eigenvalue, whereas the direction cosines for azimuth-elevation direction-of-arrival (DOA) estimation dwell in the associated ESPRIT eigenvector, and thus the matching procedure for aligning the output of multidimensional and multi-invariant data manipulation is avoided. The temporal multiresolution herein realized is iteration free and needs merely one lower-dimensional eigendecomposition. The simplified version of the presented method offers a similar performance while requiring only two time-delayed data sets and thus has a comparable computational complexity with the traditional single resolution ESPRIT. This ESPRIT-type method is also blind to the reflection coefficient which generally depends on the boundary nature, medium property, and signal parameters still to be estimated, viz. DOAs and signal frequencies. Simulation results are included to validate the proposed algorithm.     

基于互质阵列冗余分析的稀疏阵列设计方法

互质阵列因具有较低的互耦效应而备受关注,但交替部署的子阵却在一定程度上限制了连续自由度的提升。针对上述问题,该文在分析子阵互差集中冗余虚拟阵元产生条件的基础上,提出了两种子阵移位互质阵列(Coprime Array with Translated Subarray, CATrS),以改善自由度性能。首先,将子阵平移至适当位置以优化布阵结构,并分析了子阵的平移距离。随后,推导了CATrS结构的自由度、连续自由度、孔洞位置和虚拟阵元权重的闭合表达式。理论分析表明,CATrS结构能够在保持物理阵元数量不变的条件下,有效增加自由度和连续自由度,并抑制阵元互耦。最后,利用仿真实验验证了CATrS结构在波达方向估计中的优越性。     

Distributed uplink scheduling and rate control in cdma2000 using adaptive antenna arrays

Distributed uplink scheduling and rate control in CDMA networks are considered in the case of adaptive antenna arrays present at the base station. The system model with omnidirectional antennas is generalized to the case where adaptive antenna arrays are deployed. Rate control in a probabilistic manner is investigated. Long-term control by the base station through token bucket constraints is incorporated in the system. Monte Carlo simulation results show considerable improvement when adaptive antenna arrays are used. Optimization of the rate transition probabilities is treated in the special case of on–off scheduling.     

信号数目估计错误的最大似然波达方向估计的性能分析

本文分析了汝信号数目估计与真正的信号数目有出入时的波达方面（ＤＯＡ）最大似然（ＭＬ）估计的性能。中给出此时角度估计的渐近分布,也就给出这一估计的渐近方差的角析表达式,计算机模拟试验表明,理论预计结果是相当准确的。     

基于改进多信号分类算法的波达方向估计

针对传统波达方向估计算法在低信噪比条件下性能表现不佳的问题,本文提出了一种基于改进多信号分类的波达方向估计算法。本文首先对多信号分类算法的不足进行分析,并采用时间平滑技术构建相关矩阵,然后采用相关矩阵建立空间谱函数达到波达方向估计,最后采用仿真实验对算法的性能进行测试。结果表明,本文算法可以快速准确的估计出多个信号的波达方向,降低了波达方向估计误差,而且性能远远优于其它改进多信号分类算法。     

Optimization of programmable logic arrays

We describe two techniques for the minimization of the area of a Programmable Logic Array (PLA). Based on the logic functions to be implemented an assignment of the inputs and outputs to the columns of a PLA is determined that is especially suited for row segmentation. An upper bound and a lower bound for the number of rows in the segmented PLA are derived. Furthermore, it is shown how the result can be improved upon by the duplication of some of the inputs.     

最佳稀布直线阵列的分区动态规划法

在动态规划阵列的基础上,为降低峰值旁瓣电平,本文提出了分区动态规划阵列,并与其它几种优化布阵方法进行比较,结果表明：分区动态规划阵列具有相当的优越性。     

Application of natural computing algorithms to maximum likelihood estimation of direction of arrival

This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application.     

基于Y形阵的空时二维DOA估计

提出了一种适用于Y形阵的空时二维波达方向估计方法,在低信噪比下实现了无模糊的二维波达方向估计。该方法利用了Y形阵的特性,采用空时二维处理,通过采样数据归一化、加权处理和参数回代的方法解决了前后向模糊问题,具有较好的稳定性和较小的估计方差。由于在低信噪比下的优良性能,该算法适合于复杂电磁环境下的应用。仿真结果证明了方法的有效性。