子空间投影的频谱感知算法研究

为了发现空间中的“频谱空洞”而加以利用以使频谱利用率最大化,频谱感知技术得到了广泛关注。已有基于特征矢量的频谱感知算法因涉及大量特征值分解运算导致算法运算量大,不适应实时检测。本文提出的频谱感知算法利用信号子空间和噪声子空间之间的正交性,将次用户接收信号分别投影到上述子空间,根据投影值的差异实现快速频谱感知。理论分析和仿真结果表明本文提出的算法与已有算法相比有效降低了运算量,检测性能不受噪声不确定度影响、不需要预知主用户先验知识和噪声方差,且低信噪比、小采样情况下有更优越的检测性能。      

超宽带通信中模拟空时码的一种子空间检测方法

该文针对应用于超宽带(UWB)通信中的模拟空时码, 提出了一种基于子空间的盲检测方法。在接收端不需要估计信道,利用信号子空间和噪声子空间正交的性质,建立了一个二次型,最小化该二次型就可得到发送信号的估计。平坦衰落信道环境中的仿真表明,子空间方法能够实现和相关接收相同的分集增益;当多个连续的空时码联合解码时,其性能优于现有的非相关解码方法。     

空时编码多载波码分多址MIMO系统下行频率选择性信道盲估计

基于子空间方法的无线信道盲估计由于其算法的固有特性,使得估计结果与实际信道之间存在一个不确定复系数,无法得到无线信道的精确估计。在利用子空间分解方法对空时编码多输入多输出MC-CDMA系统下行频率选择性信道盲估计的基础上,利用发射符号的有限码集特性,将单载波系统下的模糊复系数盲辨识方法推广到多载波多输入多输出系统,从而得到信道的精确估计。Monte-Carlo仿真表明,在信噪比较低的情况下,本方法的信道估计误差仍然较小。     

Distributed Cooperative Wideband Spectrum Sensing Algorithm Based on Subspace Methods

We present a novel distributed cooperative spectrum sensing algorithm from compressive sampling in wideband cognitive radio (CR) networks. Each CR utilizes compressive sampling to reduce data acquisition costs. A subspace method is then adopted to directly detect occupied channels without reconstructing the sparse spectrum. To obtain the spatial diversity gain, global signal subspace is estimated by the distributed projection approximation subspace tracking (DPAST) algorithm in which the CRs exchange information locally and cooperate without the need for a fusion center. Then, the orthogonality property of the signal subspace and noise subspace can be exploited to find spectral support to complete the spectrum sensing. We study the convergence behavior of the DPAST algorithm and evaluate the performance of spectrum sensing. Simulation results indicate that the DPAST can effectively estimate the global signal subspace, and the proposed compressed wideband spectrum sensing scheme performs better than spectrum sensing at a single CR.     

一种子空间投影的高分辨宽测绘带SAR成像通道均衡方法

结合数字波束形成技术,多通道SAR可以有效克服天线最小面积的限制,从而实现高分辨率宽测绘带的合成孔径成像。然而通道之间的幅相误差以及基线误差等因素严重影响数字波束形成解模糊操作。该文提出了一种针对高分辨宽测绘带(HRWS)的多通道SAR通道间误差校正方法。该方法考虑了通道误差的多普勒空变性,利用信号子空间和噪声空间的正交性实现对包括基线误差,阵元误差等综合通道间幅相误差的快速优化估计。实测数据验证了方法的有效性。     

MIMO系统中一种改进的盲MMSE空时多用户检测算法

针对MIMO系统,提出了一种改进的基于子空间的盲MMSE空时多用户检测算法。该算法结合MIMO系统的空间分集技术与Alamouti空时分组码方案,预估计MIMO信道信息并对信号子空间进行预处理,使用正交性能和稳态性能较好的NOOja算法跟踪信号子空间,在自适应过程中对特征值矩阵进行优化,去除迭代带来的噪声,解决了跟踪过程中信号特征值矩阵的近似估计会带来检测器性能恶化的问题。仿真结果表明这种算法,能有效地抑制多址干扰,抗远近效应能力强,尤其在低信噪比、远近效应明显的恶劣环境下,有稳定良好的性能表现。      

正交模约束的稳健波束形成算法

Capon波束形成算法在导向矢量存在误差或是在较少快拍数或高信噪比情况下,都会使波束产生严重的畸变。为了提高波束的稳健性,该文提出了一种正交模约束的稳健波束形成算法(OCCB),并通过二次求导得到阵列权值的具体数学表达式。该算法在Capon算法的基础上,增加对阵列权值和噪声子空间的正交模约束,实现了在不影响信号和干扰特征值的同时,完成对噪声特征值的加载,减小噪声特征值的扩散程度。分析了部分对角加载对期望信号、干扰和噪声的影响。该算法在导向矢量存在误差、采样快拍数较少和高信噪比情况下,都可使波束具有更低的旁瓣和更加准确的主瓣指向,同时对干扰能进行较好的抑制,仿真结果证实了方法的有效性。     

一种基于子空间分析的快速信源个数估计方法

针对阵列信号处理中传统信源个数估计方法如基于Akaike信息论准则方法(AIC)、最小描述长度准则方法(MDL)等特征值分解类算法计算量大,且在小快拍数、低信噪比时性能下降甚至无法正确估计的问题,提出了一种基于子空间分析的快速信源个数估计方法。该算法首先利用多级维纳滤波器(MWF)对信号进行快速的子空间估计,然后计算阵列信号协方差在子空间匹配滤波器中的投影值,通过分析其正交性来估计信源个数。研究结果表明,该算法不但在小快拍数、低信噪比时相较于传统特征值分解类算法具有更优异的性能,并且计算量大大降低。     

基于降维噪声子空间的二维阵列DOA估计算法

为提高波达方向(Direction Of Arrival, DOA)的估计速度,该文基于子空间的正交性原理,利用噪声子空间及其共轭的交集进行奇异值分解(SVD)实现噪声子空间的降维,并基于降维噪声子空间与导向矢量及其共轭的双正交性提出一种2维阵列快速DOA估计算法。理论分析和仿真实验表明:该算法不受实际阵型的限制,能将传统MUSIC谱的角度范围压缩至原来的一半,从而将DOA估计的计算量降至传统方法的50%,并具有与MUSIC算法相当的角度分辨率。     

采用矩阵累乘的自相关矩阵构造

提出了一种适用于低信噪比情况下提取扩频信号特征参数的算法。该算法通过对被测信号的多次采样、分段累乘,扩大了待分解信号的样本数,降低了噪声的影响,从而能够获得比传统子空间分解算法更好的性能。通过分段累乘构造的自相关矩阵,对其进行特征值分解后,表现出对噪声不敏感的特性,在一定程度上克服了常规方法的噪声敏感缺点。对算法的仿真计算表明,该方法应用在低信噪比的通信环境下,信号特征值不会被噪声湮没,解决了传统子空间方法在低信噪比条件下的分辨率不足的问题。该算法的提出对低信噪比条件下的扩频信号处理和参数检测有重要的工程和实际意义。     

Low-complexity nonlinear least squares carrier offset estimator for OFDM: identifiability, diversity and performance

Orthogonal frequency division multiplexing (OFDM) transforms frequency-selective channels into multiple low-rate flat-fading subchannels. Carrier frequency offset between transmitter and receiver local oscillators must be estimated and compensated at the receiver to maintain orthogonality of these subchannels. In this paper, we derive the nonlinear least squares (NLS) estimator for carrier frequency synchronization that exploits receiver diversity and known OFDM signal subspace structure due to the placement of unmodulated (virtual) subcarriers. The resulting estimator benefits from the high-resolution subspace method without the computational overhead associated with subspace decomposition. Fundamental estimator performance relationships against parameters such as signal-to-noise ratio (SNR), frequency-selective fading, and diversity branch correlation are derived. In particular, we derive the Cramer-Rao bound (CRB) for the mean square error (MSE) of the carrier frequency offset estimator. Numerical studies are presented to verify the results.     

加权多维标量接收信号强度定位方法

本文提出了一种利用加权多维标度的基于接收信号强度的定位方法,利用信号接收强度与目标到接收机距离之间的关系,将信号接收强度转化为距离,利用多维标度框架对辐射源定位问题进行建模,对多维标度矩阵进行子空间分解,根据信号子空间与噪声子空间的正交性,可以得到定位参数的线性方程组,根据该线性方程组的残差的统计特性,得到定位参数的加权最小二乘解。计算机仿真验证了此方法具有较好的定位性能,在接收信号强度测量精度较高时,本方法能够达到CRB界,有较强的应用价值。      

频谱分析技术在层析SAR森林垂直结构信息提取中的应用性能分析

为了分析频谱分析在不同应用场景的信号提取能力,分别应用数值模拟与真实林区的多基线InSAR数据分析五种频谱分析方法的提取效果.数值模拟实验表明,Capon方法受信噪比影响较小;在TSAR林区实验中,Capon方法提取森林垂直结构信息效果最优,NSF方法提取冠层高度及地表信息效果最优.     

基于相位匹配原理的稳健方位估计

文中介绍了一种新的信号方位估计原理--相位匹配原理(PMT),该原理计算简单、且不受噪声相关特性的影响。论文提出了一个新的基于矩阵范数的判别准则,该准则极大地改进了方位的估计精度和方差,并保持了算法计算简单的特点。仿真结果表明改进后的准则在高信噪比下接近MUSIC的估计性能,在低信噪比下却超过MUSIC估计,而且其估计性能不受未知相关噪声的影响。     

Channel assignments for improved gain in baseband array feedcompensation systems

The performance of a real-time digital combining system for use with array feeds has been considered in previous articles. The purpose of the combining operation is to recover signal-to-noise ratio (SNR) losses due to antenna deformations and atmospheric effects. Previously, arbitrary signal powers and noise variances were assumed, but no attempt was made to match the receiver channels to the available signal powers. Here it is shown that for any signal power and noise variance distribution, a “best” channel assignment exists that maximizes the combined SNR in the limit of vanishingly small combining losses. This limit can be approached in practice by observing sufficiently many samples. Specific signal power and noise variance distributions are considered, and it is shown that even relatively “noisy” channels can be used effectively to recover SNR losses resulting from signals diverted out of a “high-quality” channel by antenna deformations     

A New Approach for Estimating the Number of Sources Under the Coexistence of Circular and Various Noncircular Sources

Estimation of source number is a fundamental problem of direction-of-arrival (DOA) estimation. In the problem of DOA estimation under the coexistence of circular and various noncircular signals, the source number should be estimated in order to distinguish the signal subspace from the noise subspace. Thus, a new method for source number estimation is proposed in this paper. Using the approach of k-means clustering, the projections of a one-dimensional reduced covariance matrix are divided into two categories. Then the signal subspace and the noise subspace are separated by the optimal classification boundary of those two categories so as to obtain the equivalent source number. Simulation results show that the proposed method has relatively better performance even in low SNR or in a colored noise environment.     

An angle difference of directions arrival algorithm with channel inconsistency

This article proposes a new angle difference of directions arrival (ADOA) estimation algorithm. It can estimate the values of ADOA of incidence signals even under channel incoherence environments. It performs an eigenvalue decomposition of a covariance matrix to obtain signal and noise subspace eigenvectors, then estimates the values of ADOA of signals based on the orthogonality between the noise subspace eigenvectors and steering vectors. Its main advantage is that the performance is not degenerated by array elements channel incoherence. In addition, the system complexity is also reduced. Finally, the numerical simulations have been conducted to verify the performance improvement of this algorithm.     

基于TOFS的宽带信号波束空间DOA估计

频域子空间正交性测试(TOFS)方法是宽带信号DOA估计的一类重要算法。TOFS无需进行聚焦,避免了角度的预估计,在中等信噪比时性能较优,但该方法信噪比门限较高。文中利用均匀线阵模型,将波束空间方法应用于TOFS算法,在降低分辨信噪比门限的同时减少了TOFS算法的运算复杂度。通过仿真验证了新方法性能优于TOFS算法。     

空间非平稳噪声下的宽带DOA估计算法

投影子空间正交性测试(TOPS)法是利用子空间的正交性实现宽带信号DOA估计,而在空间非平稳噪声环境下子空间的正交性条件不再满足,尤其是在低信噪比或低快拍条件下子空间估计将出现较大误差,TOPS算法性能将急剧下降。针对该问题,提出了一种空间非平稳噪声下宽带DOA估计算法。该算法首先通过构造特殊对角矩阵将噪声从数据协方差矩阵中剔除,从而克服非平稳噪声对DOA估计的影响;然后利用平方TOPS法实现宽带信号DOA估计,消除了传统TOPS算法中的伪峰。该算法适用于空间非平稳噪声背景及低信噪比环境,提高了对角度相近目标的分辨性能;仿真实验表明了该算法的有效性。     

The LSCM_SUB algorithm for blind multiuser detection in multipath CDMA channels

1 Introduction Multiuser detection (MUD) is applied mainly in CDMA to cancel the multiaccess interference. The single-user matched filter or RAKE receiver treats interference from other users as noise, but MUD takes advantage of other user’s interference…