一种基于波束空间的单次快拍MUSIC算法

针对MUSIC算法在工程应用中存在的计算量大、快拍需求数多等问题,详细研究了一种单次快拍MUSIC算法。针对这种算法存在的分辨力差、估计偏差大等缺陷,提出了一种新的基于波束空间的单次快拍MUSIC算法,该方法首先利用单次快拍来估计阵列数据的协方差矩阵,再将常规波束形成方法和MUSIC超分辨方法相结合,实现对空间谱的估计。仿真结果表明,这种新方法提高了分辨力,降低了估计偏差,进一步减少了运算量。     

DOA Estimation in Mobile Communications System using Subspace Tracking Methods

The radio environments in mobile communications are complicated and time-varing in general; therefore, we need high resolution DoA (direction of arrival) estimation methods that can follow quickly the change of radio environments. High resolution DoA estimation methods have been proposed which are based on the eigen decomposition of the correlation (covariance) matrix of an array input. MUSIC (Multiple Signal Classification) is one of a typical of such methods. However, these methods must normally repeat high-load computation involving the eigen decomposition of a correlation matrix every time a snapshot is taken. Therefore, it takes a very long time to obtain the estimated DoA when the number of array elements is too large. In addition, it is quite inefficient in the case that the DoA estimation is carried out continuously. To solve the above problems, Bi-SVD (Bi-Iteration Singular Value Decomposition) and PAST (Projection Approximation Subspace Tracking) have been proposed and investigated, which are typical methods of successively updating (tracking) eigenvectors in the signal subspace of correlation matrix. The radio environments in mobile communications are complicated and time-varing in general; therefore, we need high resolution DoA (direction of arrival) estimation methods that can follow quickly the change of radio environments. High resolution DoA estimation methods have been proposed which are based on the eigen decomposition of the correlation (covariance) matrix of an array input. MUSIC (Multiple Signal Classification) is one of a typical of such methods. However, these methods must normally repeat high-load computation involving the eigen decomposition of a correlation matrix every time a snapshot is taken. Therefore, it takes a very long time to obtain the estimated DoA when the number of array elements is too large. In addition, it is quite inefficient in the case that the DoA estimation is carried out continuously. To solve the above problems, Bi-SV     

智能天线系统中空间分域虚拟阵列的DOA估计算法研究

在即将商用的3G移动通信系统中，智能天线技术成为研究的热点．本文是以智能天线技术中DOA估计为基础，研究一种可提高估计能力的虚拟阵列DOA估计算法。介绍了虚拟阵列变换技术，根据智能天线系统的特点．采用空间分域的子阵思想．结合虚拟阵列变换技术．实现空间分域的虚拟阵列DOA估计。     

RAM-based angle estimation with linear spatially separated polarisation sensitive array

Two-dimensional direction of arrival (2D-DOA) and polarisation estimation of multiple incident signals for linear spatially separated polarisation sensitive array (SS-PSA) is investigated with reweighted atomic norm minimisation (RAM) algorithm. Single vector sensor in this paper is composed of spatially separated three dipoles and three loops. Firstly, the received data of the proposed array with single snapshot is converted into the new received data with six virtual snapshots. Each virtual snapshot data is the data sensing by dipoles or loops located in the same direction. Secondly, based on each virtual snapshot data, the complex amplitudes and frequencies of the virtual signals are restored using RAM algorithm. Lastly, 2D-DOA and polarisation angles are derived according to the restored complex amplitudes and frequencies. Simulation results demonstrate that the RAM algorithm used in 2D-DOA and polarisation estimation with SS-PSA exhibits superior performance, which is better compared with polarisation multiple signal classi?cation (PMUSIC) algorithm and rotational invariance techniques (ESPRIT) algorithm.     

基于线性时频分析的非平稳源空间谱估计

针对存在幅度和相位调制的非平稳信号的定向问题,本文提出了一种改进的线性时频空间谱估计算法。该算法首先在线性时频面上检测信号的时频脊,并沿各时频脊进行采样形成独立的阵列时频快拍,然后对各快拍采用通用算法进行空间谱估计,此时阵列能估计的信号源数已不再受阵元数的限制。计算机仿真实验表明,该算法具有优良的定向精度,且能有效地对多个非平稳信号源进行分离和空间谱估计,其计算量远低于二次时频空间谱估计算法。     

Robust antenna array calibration and accurate angle estimation based on least trimmed squares

A robust antenna array calibration and single target angle estimation algorithm is proposed. The proposed algorithm is based on the least trimmed squares algorithm and operates in two steps. First, the conventional least squares algorithm is used to estimate the intermediate phases (or angle) and the residual values at each element are calculated. In the second step, it excludes large residual elements and uses only the smallest of them, which prevents large errors during the angle estimation. The least trimmed-based phase difference approximation algorithm is simple to implement and is a practical way of mitigating errors at the antenna elements that are due to hardware and imperfect calibration. The results demonstrate that our proposed algorithm is robust and outperforms other algorithms in three scenarios.     

TD-SCDMA中DOA估计的一种ESPRIT算法

智能天线是TD-SCDMA系统的关键技术之一,其核心为波达方向(Direction of Arrival,DOA)估计和波束形成算法。目前大多数DOA估计算法都是以均匀线阵为基础,而TD-SCDMA采用的是8阵元的圆阵列天线,因此,文中采用空间预处理技术,将圆阵虚拟到均匀线阵,再用ESPRIT算法进行DOA估计,最后通过仿真验证了该算法的可行性。     

稀疏阵列L 型MIMO 雷达运动目标三维成像方法

针对L型多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达对空中运动目标三维成像天线数目较多问题,提出了发射阵列采用稀疏布阵的L型MIMO雷达三维成像方法。该文首先分析了MIMO雷达发射阵列的稀疏布阵方式,其次结合压缩感知理论具体阐述了基于稀疏阵列的三维成像方法。该方法在大幅减少L型MI-MO雷达发射天线的条件下,实现了对运动目标的单次快拍三维成像,不仅有效避免了目标机动带来的运动补偿难题,同时又降低了系统的硬件复杂度,便于工程应用。最后利用仿真实验验证了本文方法的有效性和可行性。     

Beamspace Transform for UCA: Error Analysis and Bias Reduction

In this paper, we analyze the error caused by the beamspace transform (BT) when it is applied to uniform circular array (UCA) configuration. Several algorithms for direction of arrival (DoA) estimation exploit this modal transform because it allows using computationally efficient techniques such as polynomial rooting and dealing with coherent sources. The BT is based on the phase-mode excitation principle. The performance of such DoA estimators is degraded if the array has a small number of elements. We introduce a modified beamspace transform (MBT) that performs mapping from element-space to beamspace domain taking into account the error caused by the transform. Justification of the difference in the statistical performances of MUSIC and root-MUSIC algorithms for UCA is also given. Moreover, we show that there is a significant difference in the performance of the UCA root-MUSIC technique depending on whether an even or odd number of elements is used. We derive an expression approximating the bias in the DoA estimates that is caused by the beamspace transform. Some design guidelines are provided for choosing the key UCA configuration parameters such as number of sensors, array radius, and interelement spacing in order to reduce the error. Finally, we propose a novel technique for bias removal. It allows practically bias-free DoA estimation.     

基于Bessel先验快速稀疏贝叶斯学习的互质阵列DOA估计

为提高低采样点条件下互质阵列DOA估计精度,该文提出基于Bessel先验快速稀疏贝叶斯学习算法。该方法针对互质阵列输出的多采样点复数数据,首先构建了基于Bessel先验的多量测分层模型;其次推导了模型所涉超参数的对数似然函数,根据最大似然估计准则得到了超参数的迭代公式;最后提出了快速实现方案,提高了运算效率。仿真结果表明,该方法不依赖先验信息,在低采样点条件下具有更高的DOA估计精度和分辨率,能够对相干信号进行高精度DOA估计,并具有较高的运算效率。此外,该文探究了虚拟阵列扩展与互质阵列测向自由度扩展间的关联,为后续阵列误差条件下互质阵列DOA研究估计提供参考。     

DoA Estimation Via Manifold Separation for Arbitrary Array Structures

In this paper, we consider the manifold separation technique (MST), which stems from the wavefield modeling formalism developed for array processing. MST is a method for modeling the steering vector of antenna arrays of practical interest with arbitrary 2-D or 3-D geometry. It is the product of a sampling matrix (dependent on the antenna array only) and a Vandermonde structured coefficients vector depending on the wavefield only. This allows fast direction-of-arrival (DoA) algorithms designed for linear arrays to be used on arrays with arbitrary configuration. In real-world applications, the calibration measurements used to determine the sampling matrix are corrupted by noise. This impairs the performance of MST-based algorithms. In particular, we study the effect of noisy calibration measurements on subspace-based DoA algorithms using MST. Expressions describing the error in the DoA estimates due to calibration noise and truncation are derived. This allows predicting the performance of MST-based algorithms in real-world applications. The analysis is verified by simulations. We established a link between the optimal number of selected modes and the statistics of calibration noise. We analyze the modeling error when MST is used for 1-D (azimuth) DoA estimation.     

基于双旋转单偶极子阵列的谱参数估计方法

提出了一种基于双旋转单偶极子阵列的联合谱参数估计算法.通过旋转阵元及修正极化多重信号分类(multiple signal classification, MUSIC)算法中谱函数的导向矢量, 便能实现用两阵元进行多目标测向, 回避了传统算法中要求信号源个数小于阵元数的问题.仿真结果表明:在小快拍数、低信噪比的影响下, 该算法在测向性能上优于阵元数为6的极化空间谱估计算法, 且所用通道数少, 可降低成本.该算法所述的单偶极子阵列可用任意极化敏感天线单元或组合进行构造, 可移植性较强.     

基于压缩感知的稀疏阵列MIMO雷达成像方法

针对MIMO雷达对空目标单次快拍成像时天线数目较多问题,该文提出了一种稀疏阵列MIMO雷达的成像方法。首先分析了MIMO雷达天线的稀疏布阵方式,其次结合压缩感知理论具体阐述了稀疏阵列MIMO雷达的成像方法。该方法不仅能够对运动目标实现单次快拍成像,避免了目标机动带来的运动补偿难题,同时又能够大幅减少MIMO雷达的天线规模,便于工程实现。最后利用仿真实验验证了所提方法的有效性。     

A multiple antenna wireless testbed for the validation of DoA estimation algorithms

In this paper, a wireless testbed with a multiple antenna receiver is described. It comprises a rotating four-element antenna array connected to a quad radio frequency (RF) front-end, and a channel acquisition board equipped with a field programmable gate array (FPGA). The algorithms can be implemented directly on the FPGA, but they can also be tested via simulation, e.g., with Matlab, since the acquired data can be transferred from the board memory to a personal computer (PC). Moreover, the algorithm implementation on the FPGA can be done by exploiting the System Generator for DSP Xilinx tool that allows the algorithm synthesis from a Simulink block diagram. These features make the testbed useful for rapid prototyping. In particular, the presence of the rotating antenna array enables the analysis of direction of arrival (DoA) estimation techniques. We report the main results from the experimental measures conducted to characterize the hardware non idealities. We then describe a DoA estimation algorithm that has been used to compare real and simulated results. It is shown that the results are in good agreement with the simulations, also when the effect of non isotropic antenna gains and/or phase noise originated from non co-phased RF front-ends becomes considerable.     

Full Azimuth Direction-of-Arrival Estimation With Successive-Selection Technique

A method for full-azimuth direction-of-arrival (DoA) estimation of multiple signals with a hexagonal array is proposed. The DoA estimation is performed in two steps. In the first, a set of estimate candidates is constructed by gathering the estimates that are obtained from applying the low-computational-cost Unitary-ESPRIT algorithm to several translational invariances designed into a hexagonal array. In the second step, the DoA estimates are successively selected from the estimate candidate set by using a selection function. The proposed method removes the north-or-south signal membership ambiguity and the limitation on the number of estimable sources, problems common to any ESPRIT-based algorithm used with one translational invariance. Therefore, up to $M-1$ signal DoA estimations can be expected with an $M$- element hexagonal array in the full azimuth. The successive-selection approach is based on a selection function that uses an estimate of the signal's spatial correlation matrix to successively select the DoA estimates. For each DoA estimate selection, the already estimated signal components are removed from the correlation matrix. Such an approach is shown to actually allow the selection of suitable estimates from among a set of estimate candidates. Finally, the method's DoA estimation and resolution capabilities are demonstrated by computer simulation and comparison with the CramÉr–Rao bound.      

High resolution target localization using rotating linear array radar

In this paper, an algorithm is proposed for detection and joint estimation of parameters of multiple targets using rotating antenna array. This paper is sequel to our previous work addressing a two element antenna array only. Joint estimation of number of targets, the targets’ range, Doppler and their directions of arrivals is carried while the effects of antenna rotation are compensated for a multi element linear array. The effectiveness of the algorithm is verified by the simulation results carried out for an eight element array. The proposed algorithm is able to resolve targets with same range and Doppler. The computations of the proposed algorithm are calculated and comparison is also given with other algorithms.

     

Multidimensional frequency estimation using LU decomposition eigenvector–based algorithm

Many algorithms have been proposed for multidimensional frequency estimation from a single snapshot or multiple snapshots of data mixture. Most of these algorithms fail when one or more identical frequencies are found in certain dimensions. In this paper, a multidimensional frequency estimation technique from a single datum snapshot is proposed. It applies LU decomposition (Gaussian Elimination) on an eigenvector-based algorithm for multidimensional frequency estimation. This proposed technique is simulated using a MATLAB code. The average root mean square error (RMSE) is investigated as a performance measure of the proposed technique. A comparison between original eigenvector-based (traditional) and the proposed techniques is introduced. The simulation results show that the RMSE of the proposed technique is less than the original one, and it has a more efficient solution for an identical frequency case but at the expense of complexity.     

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。     

An efficient algorithm for two-dimensional frequency estimation

This paper presents a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. We derive the theoretical performance of the 2D-MODE estimator and show that it is asymptotically statistically efficient under either the assumption that the number of temporal snapshots is large or the signal-to-noise ratio is high. Numerical examples showing the performance of this algorithm and comparing it with the computationally efficient subspace rotation algorithms are also given. We show that the statistical performance of the 2D-MODE algorithm is better than that of the subspace rotation methods. The amount of computations required by the former is no more than a few times of that needed by the latter for either small numbers of spatial measurements or a single temporal snapshot, which are the cases of interest herein.     

Direction of Arrival (DoA) Estimation Under Array Sensor Failures Using a Minimal Resource Allocation Neural Network

This paper presents the use of a minimal resource allocation network (MRAN) for the direction of arrival (DoA) estimation under array sensor failure in a noisy environment. MRAN is a sequential learning algorithm in which the number of hidden neurons are added or removed based on the input data and produces a compact network. The training for MRAN is done under no failure and no noise case and the trained network is then used when there is a failure. Thus, the need for knowing the element and the time of its failure, as required in other methods is eliminated. MRAN's performance is compared with the conventional MUSIC algorithm and also the radial basis function neural network scheme developed by A. H. El Zooghby under normal and failed cases. In normal case, different antenna effects like mutual coupling, nonuniform array and unequal source power have been studied under different signal to noise ratio (SNR) values. Results indicate the superior performance of MRAN based DoA estimation scheme under different antenna effects, failure conditions and noise levels