宽带恒定束宽盲波束形成算法

针对传统盲源分离算法对宽带阵列信号适用性较差的问题,提出一种基于时频分析的宽带恒定束宽盲波束形成算法。该算法首先将接收信号变换到时频域上并提取出单源点。然后,对单源点聚类并求解信号在不同频点上的导向矢量。最后,通过提出一种信号来向未知的空间响应变化约束方法,实现宽带恒定束宽盲波束形成。该算法避免了将宽带盲波束形成转换为卷积混合的盲源分离,因而不存在时域盲源分离算法中系统参数随滤波器阶数急剧增加的问题,也不存在频域算法中排序和幅度模糊的问题。仿真结果表明,算法能够较好地实现宽带信号的盲分离,且输出信干噪比高于时域、频域以及时频域盲源分离算法,实测数据的处理结果验证了该算法的实用性。      

基于拉伸处理的宽带频域接收波束形成方法

为了抑制宽带干扰信号,提出了一种基于频域的宽带接收波束形成方法。该方法针对线性调频信号,在对接收信号做拉伸处理和窄带滤波之后,进行傅立叶变换划分为许多窄带,然后对不同频带的信号按窄带方法（例如采样矩阵求逆法）接收波束形成。与传统的频域方法相比,大大降低了数据率和计算量,易于工程实现;与拉伸处理波束形成方法相比,该方法能有效抑制宽带干扰,阵列输出信号干扰噪声比高。最后计算机仿真证实了新方法的有效性。     

Wideband LFM Interference Suppression Based on Fractional Fourier Transform and Projection Techniques

A novel wideband LFM interference suppression method is proposed based on fractional Fourier transform (FRFT) and projection techniques. By using the concentration property of LFM interferences in fractional Fourier domain, the initial frequency, frequency modulation rate and direction-of-arrival (DOA) of LFM interferences are first estimated. Afterwards, the space–time interferences subspace is constructed. In order to suppress wideband LFM interferences, the received signal is projected onto the subspace orthogonal to the interferences subspace when the signals are uncorrelated. Furthermore, considering the scenario when the signals are coherent, according to the properties of oblique projection, LFM interferences are suppressed by projecting the received signal onto the joint signal–noise subspace. Then, the desired signal is extracted by beamforming. Unlike the existing algorithms, the proposed method not only can exactly estimate parameters and directly solve the DOA of LFM interferences but also can construct the interferences subspace with the time-variant steering vector of interferences. Moreover, our method can effectively suppress LFM interferences without reducing the freedom, and significantly improve the performance, even at low signal-to-noise ratio (SNR) or small number of snapshots. Simulation results demonstrate the effectiveness of the proposed method.     

基于性能权重聚类的频域盲分离排序算法

卷积混合盲源分离可以在频域得到有效解决，但频域盲分离必须要解决排序模糊性问题。本文提出了一种基于性能权重聚类的频域盲分离排序算法，该算法利用聚类来得到顺序参考，对各频点上分离信号的准确性进行计算，根据分离结果的准确性予以不同频点不同的聚类权重，从而提高聚类结果的可靠性。通过对频点进行分段处理可以有效抑制排序错误的传播，提高算法性能。最后通过多组仿真实验验证了基于性能权重聚类的频域盲分离排序算法的普适性与性能上的优越性，同时也探究了接收端个数对算法性能的影响。仿真结果表明本文提出的基于性能权重聚类的频域盲分离排序算法相较于传统的幅度相关性排序算法在信干比上会有2 dB左右的提升。接收天线数越多，算法分离性能越好。     

宽带光控阵多波束超分辨测向算法

为了解决多信号并存时多波束系统的测向问题,针对宽带光控阵幅度加权和时延控制的多波束形成方式,提出了一种宽带波束域超分辨测向算法。建立了宽带光控阵多波束数据模型,首先将接收数据变换到频域,进行频谱分析,然后选择较大信号频率分量分别进行超分辨谱估计,最后将各频点估计结果融合得到最终的估计值。分析了波束参数的选取,包括波束数目、波束间隔以及幅度加权方式对算法性能的影响。算法能够分辨同时达到多波束系统的多个信号,仿真实验验证了算法的有效性和正确性。      

基于联合对角化的声信号深度卷积混合盲分离方法

声信号在空间中的传播具有较强的多径效应,在接收端往往以卷积形式相互叠加,尤其在海洋、剧场等强混响条件下,混合滤波器冲激响应的长度会显著增加,现有的频域卷积盲分离算法将失效。为了消除长脉冲响应导致解混合模型失效的问题,该文对观测信号进行两次短时傅里叶变换(STFT),第1次STFT缩短了脉冲响应长度,第2次STFT将信号模型转化为瞬时盲分离,最终利用联合对角化(JD)技术估计出分离矩阵。与现有方法相比,所提方法解决了深度卷积混合下模型失效的问题,并且当源信号数较多或存在加性噪声时,可以得到更好的分离性能。仿真结果验证了方法的有效性和性能优势。     

宽带DOA估计的类MUSIC波束形成算法

为了提高频域波束形成的宽带波达方向估计性能,提出了类MUSIC波束形成算法(MBM,MUSIC-like Beamfomfing Method).在频域将宽带信号划分为若干窄带信号,叠加各窄带的MBM算法的空间谱后其峰值对应角度即为宽带波达方向估计结果.MBM算法的主瓣宽度在不同分析频率下基本保持不变,计算量与常规波束...     

结合ICA和复数神经网络的二麦阵列盲源分离技术

为了降低语音信号盲源分离算法的延时,提高其准确性和稳定性,本文结合传统盲源分离技术和深度神经网络的优势,提出了一种基于ICA独立分量分析和复数神经网络的二麦阵列盲源分离技术。本文将复数递归神经网络和独立分量分析方法有机融合,提出一种基于时频域的双通道复数神经网络,同时解决了独立分量分析中的排列问题。所提方法利输入混合信号利用复数域神经网络计算初始化分离矩阵,神经网络输出采用复数域形式,利用复数学习标签估计复数矩阵,然后采用独立分量分析方法获得目标分离矩阵。实验数据表明,所提方法相较于其它独立分量分析方法提高了盲源分离的实时性和准确性。      

基于雷达系统抗干扰的卷积盲分离算法研究

如何提高雷达系统的抗干扰能力一直是雷达信号处理中比较关注的问题。卷积混合盲分离技术是当前盲分离研究的热点和难点。文中用卷积混合盲分离算法来分离雷达系统接收的信号,把卷积混合的干扰信号和有用信号分离开来,以实现雷达系统抗干扰的目的。该方法使用基于Householder变换的高阶累积量的联合对角化的频域方法来分离卷积混合的雷达接收信号。计算机仿真表明,该抗干扰方法可把卷积混合的雷达信号和干扰信号分离出来,且达到较好的分离效果。     

基于子带SDL的宽带自适应波束形成

针对宽带波束形成通常需要大量阵元或延迟线所带来的硬件开销较大,波束形成效率相对较低的问题,提出一种基于子带阵元延迟线（SDL）的宽带自适应波束形成算法。该算法首先建立子带SDL模型,然后利用分析滤波器组将阵元接收的宽带信号分解为子带信号并进行相应的子带线性约束最小方差（LCMV）波束形成,最后通过综合滤波器组得到全带的波束形成。仿真结果表明,子带波束形成不仅具有比全带波束形成更高的效率,更好的频率不变性,更强的抗干扰能力及更快的收敛速度,而且可以大大降低硬件开销。      

Geometric method for blind signal separation of multichannel MPSK signals

In this letter, the problem of blind source separation of Multiple-Phase-Shift-Keying (MPSK) digital signal is considered. The geometry of received MPSK signals constellation is exploited. The column vectors of received signals can be regarded as the points of hyper-cube. All the possible distinct vectors of received signals are found by clustering, and mixing matrix and sources are estimated by searching out the pairing vectors and eliminating redundant information in all possible distinct vectors. Simulation results give the polar diagram of estimated original signals. They show that the proposed algorithm is effective when the original signals is Quadrature-Phase-Shift-Keying (QPSK) or 8-Phase-Shift-Keying (8PSK).     

基于循环频率特征的单信道混合通信信号的调制识别

单信道多个时频重叠的混合通信信号盲分离具有广泛的应用价值,是近年来通信信号处理研究领域的一个热点和难点,而单信道多个混叠通信信号的调制识别和信号个数的估计是实现单信道多个混叠通信信号盲分离的基础。该文通过分析数字调制信号的二阶和四阶循环累积量的循环频率特性和结构特征,提出一种基于循环频率特征的单信道混合通信信号调制识别的方法和实现算法,其主要特点是不需要已知混合信号的个数、功率、载频、符号率及定时等先验信息,对一些典型的数字调制信号(如BPSK, QPSK, OQPSK, MSK等)的随机混合,能够有效地辨识接收信号中所包含的信号个数以及每个调制信号的调制方式。利用计算机仿真实验,考察了不同条件下算法的性能,表明了其可行性和有效性。     

基于特征向量选择的非线性盲分离算法

在基于解广义特征方程的线性混合信号盲分离方法的基础上,结合核特征空间给出了一种基于特征选择的非线性混合信号盲分离算法。该算法首先将非线性混合信号映射到高维特征空间,根据适应度函数选出一组完备的特征向量基。其次,通过这组特征向量基将高维特征空间的信号映射到参数空间,从而把非线性混合信号盲分离问题转化为特征空间的线性混合信号盲分离问题。在特征空间中,应用基于解广义特征方程的线性混合信号盲分离方法对信号进行分离。该方法稳定性好,收敛精度高,计算量小。仿真结果表明该算法具有良好的分离性能。     

基于JADE和平行因子分解的欠定混合盲辨识算法

针对源信号个数未知的欠定混合盲源分离问题,本文提出了一种基于特征矩阵联合近似对角化（Joint Approximate Diagonalization of Eigenmatrices, JADE）和平行因子分解的欠定混合盲辨识算法,该算法不需要源信号满足稀疏性要求,仅在源信号满足相互独立和最多一个高斯信号的条件下,通过将JADE算法中的样本四阶协方差矩阵叠加成三阶张量,再对此三阶张量进行平行因子分解来完成源信号数和混合矩阵的估计,由于平行因子分解的唯一辨识性在欠定条件下仍然满足,该算法能够解决欠定盲源分离问题。并对该欠定混合盲辨识算法进行了深入的分析。通过仿真实验,计算估计矩阵与混合矩阵的平均相关误差,结果表明本文提出的算法在适定和欠定混合时均具有很好的辨识效果,而且实现简单,可满足实际应用的要求。      

Direction of arrival estimation of sources with intersecting signature in time–frequency domain using a combination of IF estimation and MUSIC algorithm

Time–frequency (TF) approaches are frequently employed for source localization at low signal to noise ratio. However, TF approaches fail to achieve the desired performance for sparsely sampled signals or signals corrupted by heavy noise in an under-determined scenario when sources are not TF separable. In this study, we propose a new TF method for direction of arrival (DOA) estimation of sources with closely spaced and overlapping TF signature. The proposed method uses a combination of a high-resolution time–frequency distribution and instantaneous frequency estimation method for extraction of sources with intersecting and closely spaced time–frequency signatures. Once sources are extracted, their DOAs are estimated using a well known multiple signal classification (MUSIC) algorithm. Experimental results demonstrate that the proposed source localization method achieves better performance as compared to the conventional time–frequency MUSIC algorithm.

     

Variable-step blind source separation method with adaptive momentum factor

A variable-step blind source separation algorithm based on the natural gradient with adaptive momentum factor was proposed,which could cope with the determined blind source separation in the environment of stationary and non-stationary.Function estimation mixed matrix was constructed by performance index.The estimated performance index was obtained by the estimated mixed matrix,and the constructor was updated by the estimated performance index.Then,the constructor was plugged with appropriate experienced parameter into the proposed algorithm and step and momentum factor was adaptively adjusted.Finally,the estimation source signals could be obtained.Simulations show that the proposed algorithm is effective to estimate the mixed matrix in the stationary and non-stationary environments,and the proposed algorithm has faster convergence speed and lower steady error as well as separates source signals effectively.     

基于多信道信号增强的卷积混迭语音信号盲分离的后处理方法

由于频域盲源分离方法无法精确解决排列模糊问题,分离出的信号质量受到很大影响.本文提出一种后处理方法以进一步消除不同信源间的空间干扰和噪声且无需增加额外的先验信息.首先在频域盲分离方法中利用分裂语谱技术把一个M×N的多输入多输出(MIMO)混迭系统分裂成N个单输入多输出(SIMO)系统,再对每个SIMO系统分别利用传递函数比和广义旁瓣相消器来重构源信号.仿真实验表明该算法具有良好的性能.     

Kalman滤波下的多信号单通道盲源分离

窄带物联网环境中,接收机收到的信号通常为多路混合信号,对单通道接收来说,利用常规盲源分离方法很难实现混合信号的分离和源信号提取。针对这一问题,本文提出了一种利用Kalman滤波算法进行信号估计,解决单通道盲源分离的方法。该方法利用信号间的时序结构,通过Kalman滤波算法对多信号混合中的源信号不断估计并迭代更新,最终得到分离信号。仿真实验结果表明,该方法能有效估计并分离出源信号。      

空间任意阵的多宽带信源定位算法性能研究

针对宽带信号非平稳特性,以及常用宽带DOA估计算法要求信源个数是已知的情况,提出一种新的基于空间任意阵的非平稳信号DOA估计算法。首先,通过短时傅里叶变换将信号转为频域表示,然后构造阵列频域数据模型,最后利用短时功率谱矩阵的联合对角化特性实现宽带信号的波达方向估计。本文对提出的算法进行了理论分析,并在常见阵型（如线阵、十字阵）上进行了仿真和性能分析,仿真结果表明该算法可高分辨率地估计出DOA,并且估计误差小。相对于已有的MUSIC谱估计方法,该算法无需进行信源个数估计,更具实用性。      

Super-Resolution Time Delay Estimation in Multipath Environments

The super-resolution time delay estimation in multipath environment is very important for many applications. Conventional super-resolution approaches can only deal with signals with wideband and flat spectra. In this paper, we propose a novel super-resolution time delay estimation method that can treat signals with narrowband spectra. In our method, the time delay estimation is first transformed into the frequency domain, in which the problem is converted into the parameter estimation of sinusoidal signals with lowpass envelopes. Then a MUSIC-type algorithm taking account of the envelope variation is applied to achieve the super-resolution estimation. Time delay estimation in active and passive systems are considered. Simulation results confirm that the proposed estimators provide better performance than the classical correlation approach and the conventional MUSIC algorithm for separating closely spaced signals with narrowband spectra.