基于GSC框架降秩自适应滤波算法研究

基于对自适应滤波算法的研究可以得出,GSC(Generalized Sidelobe Canceller)框架是所有降秩滤波算法的统一基础模型,通过对GSC一般结构的降秩模型的研究。文中提出了3种能够改善一般结构结果的优化模型,PC(Prin-ciple Component)主分量算法、CS(Cross Spectrum)交叉谱算法、MWF(Mutistage Wiener Filter)多级维纳滤波器。这三种方法都是基于特征子空间截断的方法。通过对降秩矩阵T的特征值分解和重新构造,大大降低了自由度和工程运算量。该两种方法在实际应用中具有更优的实时性。仿真结果证明了提出的基于广义旁瓣相消的降秩自适应波束形成算法具有良好的降低自由度和波束形成性能,验证了算法的有效性。     

降秩自适应滤波算法研究

对降秩自适应滤波算法进行了系统的总结和分析，推导了其相互关系。分析表明，GSC（Generalized Sidelobe Canceller）框架降秩变换自适应滤波是各种降秩自适应滤波算法的统一模型。在此基础上导出了线性约束正交投影算法。降秩多级维纳滤波器在相关意义上进行截断降秩，其降秩性能优于基于特征子空间截断的降秩方法。酉多级维纳滤波器与共轭梯度法等效，均是基于Krylov子空间截断降秩的方法，降秩性能更优。最后通过计算机仿真试验比较了各种降秩处理算法的性能。     

一种基于降秩变换的ESB改进算法

提出了一种基于降秩变换的ESB改进算法,该方法首先用降秩变换矩阵对阵列接收数据进行降秩变换,利用干扰子空间和修正的期望信号导向矢量来构造降秩变换矩阵,然后进行波束形成.该算法解决了传统的基于特征空间(ESB)自适应波束形成算法方向图副瓣电平较高的问题,并且解决了当阵列协方差矩阵中不含期望信号或期望信号较小时方向图畸变的问题,从而大大提高了抗干扰能力.仿真结果证明,所提出的基于降秩变换的ESB改进算法具有良好的波束形成性能,验证了算法的有效性.     

一种运动干扰稳健滤波方法

传统的阵列干扰抑制算法假设天线阵列与干扰之间相对静止,完成干扰抑制,当阵列与干扰之间存在相对运动时,存在协方差矩阵估计快拍数不足的问题,从而导致阵列滤波性能下降。针对上述运动干扰稳健滤波问题,提出了一种算法对现有零陷展宽技术抗运动干扰方法进行改进。一方面,将零陷展宽技术与导数约束方法相结合,用得到的新协方差矩阵求自适应权值;另一方面,为了提高自适应波束形成对噪声和误差的稳健性,引入了对角加载技术,加载量通过寻找协方差矩阵的特征值曲线拐点得到,有效避免了加载量过大导致的干扰信号被淹没以及加载量过小导致噪声分量引起性能损失的问题。该算法对零陷展宽系数选取依赖性较低,且对角加载量不需要设置固定的经验值,有利于稳健运动干扰滤波,适用于实际工程中先验信息不足条件下的自适应阵列抗运动干扰情况。     

基于多级嵌套维纳滤波器的空时自适应处理

针对空时自适应处理使计算量显著增加这个问题,本文研究一种基于多级嵌套维纳滤波器的降秩自适应滤波方法.进一步与主分量分析方法进行了计算机仿真比较,指出其有效的降秩作用.该方法运算量小,对采样要求低,不需要协方差矩阵,也不需要协方差矩阵求逆,增加了空时自适应处理的实际工程应用的可行性.     

一种基于子带GSC的语音增强算法

提出一种基于GSC的语音增强算法,该算法应用了DFT调制子带滤波器组将语音信号分解到子带进行自适应滤波,从而获得更好的增强效果以及更低的运量复杂度.同时,将范数约束自适应滤波(NCAF)算法应用于自适应噪声对消器(ANC)以降低语音的失真度.为了进一步去除增强后语音中的残留噪声,算法使用改进的Wiener后置滤波器.仿真结果表明,相对于基于全带GSC的麦克风阵列语音增强算法以及传统Wiener后置滤波算法,采用本文所用算法具有更高的输出分段信噪比.     

麦克风阵列的协同自适应滤波语音增强方法

在麦克风阵列语音增强方法中,传统的广义旁瓣抵消器在处理存在显著脉冲噪声的语音信号时效果较差。为提高在脉冲噪声干扰下的语音信号增强效果,提出一种麦克风阵列的协同自适应滤波语音增强方法。该方法采用协同自适应滤波取代线性自适应滤波,基于NLMS算法导出了滤波器权值和协同因子的自适应更新算法。仿真实验结果表明,所提方法能有效地消除掉语音信号的脉冲噪声和高斯噪声,克服线性自适应滤波对非线性脉冲噪声的不敏感性,比广义旁瓣抵消器效果优越很多。     

基于广义旁瓣相消的新降维方法

针对阵列信号中部分自适应信号处理问题，提出了一种新的基于广义旁瓣相消的降维方法。该方法利用波束形成无法对加性白噪声进行抑制的特点，根据广义旁瓣相消器的结构，设计出一种新的阻塞降维矩阵，使辅助通道内仅存干扰信号，从而增强主辅通道关于干扰信号的相关性，更有利于抑制干扰，同时也达到了降维的效果。另一方面，在存在阵列误差的情况下，借助稳健的波束形成算法，对阻塞降维矩阵进行校正，既使算法对模型误差不敏感，又便于进行降维后的部分自适应处理，降低运算量。最后通过大量的计算机仿真验证了所提方法的有效性和优越性。     

一种基于广义旁瓣相消的稳健降维方法

在自适应阵列信号处理中,为了高效进行干扰和噪声对消,提出了一种基于广义旁瓣相消的稳健降维方法。该方法利用信号子空间特征矢量和期望信号的投影导向矢量构造稳健降维阻塞矩阵,阻塞期望信号和噪声分量,使辅助支路中只含有干扰信号,达到了降维的效果,同时提高了算法对阵列天线误差的稳健性。仿真结果表明,该方法对阵列模型误差不敏感,可以有效地降低运算时间。     

7元圆阵高维空时自适应滤波的快速实现

本文在联合空时处理框架下,在基于共轭梯度算法(CG)的MSNWF基础上,讨论了7元圆阵高维空时自适应滤波的快速实现问题.通过分析空间中7元圆阵的阵列平行四边形关系(APR),得到简化信号协方差矩阵计算的方法,通过分析信号协方差矩阵的Toeplitz特性,提出加快信号协方差矩阵与矢量相乘的方法,并最终得到了一种7元圆阵条件下的高维空时自适应滤波的快速实现方法,并且比较了新方法与原有方法的计算量.     

实测数据的降秩空时自适应处理方法

本文介绍了两种在广义旁瓣相消器结构上的降秩空时自适应处理方法:主分量法和互谱法,这两种方法是利用杂波和干扰的低秩属性,应用采样数据来构造降维转换矩阵。最后对MountainTop计划进行了详细的描述,并对从MountainTop计划测得的数据,应用上述两种降秩方法对其进行了分析。仿真实验表明了这两种方法的可行性,减少了计算量,并提高了有效可测收敛性。     

A Novel Method for Partially Adaptive Broadband Beamforming

In this paper, a novel subband-selective generalized sidelobe canceller (GSC) for partially adaptive broadband beamforming is proposed. The blocking matrix of the GSC is constructed such that its columns constitute a series of bandpass filters, which select signals with specific angles of arrival and frequencies. This results in bandlimited spectra of the blocking matrix outputs, which is further exploited by a subband decomposition prior to running independent unconstrained adaptive filters in each non-redundant subband. We discuss the design of both the blocking matrix using a genetic algorithm for an efficient sum-of-power-of-two coefficient format and the filter bank for the subsequent subband decomposition. By these steps, the computational complexity of our subband-selective GSC is greatly reduced compared to other adaptive GSC schemes, while performance is comparable or even enhanced due to subband decorrelation, as simulations indicate.     

多点约束空时抗干扰算法的研究

提出了一种多点约束下的GNSS接收机空时抗干扰算法。从多点约束LCMV准则的等效GSC结构入手,对其前向分解过程进行了简化与改进,使计算复杂度降低到与单点约束问题相当;随后利用Householder多级维纳滤波(MWF)算法求解GSC结构中的经典维纳滤波问题,并利用MWF的分解特性对其后向迭代过程进行改进,得到了一种阶递归的实现结构,大大提高了算法的实时性,并可根据MSE值自适应地估计降维维数。给出了完整的算法流程,并进行了仿真实验,验证了算法的有效性。     

Blind adaptive beamforming based on generalized sidelobe canceller

The generalized sidelobe canceller (GSC) is an efficient implementation of the direction constrained adaptive array. Conventional GSC is designed according to a quiescent weight vector and a blocking matrix. The quiescent weight vector provides the array with specified array response at some direction. The blocking matrix is designed based on a priori knowledge of the direction of arrival (DOA) of the desired signal. In this paper, we propose a new GSC-based adaptive array without a priori knowledge of the DOA of the desired signal. This paper utilizes eigensubspace decomposition and statistically cyclostationary properties of the signals to design the adaptive array. A method for constructing the most efficient blocking matrix for the GSC is developed. Simulation examples illustrate the effectiveness of the proposed method.     

Adaptive reduced-rank interference suppression based on themultistage Wiener filter

A class of adaptive reduced-rank interference suppression algorithms is presented based on the multistage Wiener filter (MSWF). The performance is examined in the context of direct-sequence (DS) code division multiple access (CDMA). Unlike the principal components method for reduced-rank filtering, the algorithms presented can achieve near full-rank performance with a filter rank much less than the dimension of the signal subspace. We present batch and recursive algorithms for estimating the filter parameters, which do not require an eigen-decomposition. The algorithm performance in a heavily loaded DS-CDMA system is characterized via computer simulation. The results show that the reduced-rank algorithms require significantly fewer training samples than other reduced- and full-rank algorithms     

Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering

We present an adaptive reduced-rank signal processing technique for performing dimensionality reduction in general adaptive filtering problems. The proposed method is based on the concept of joint and iterative interpolation, decimation and filtering. We describe an iterative least squares (LS) procedure to jointly optimize the interpolation, decimation and filtering tasks for reduced-rank adaptive filtering. In order to design the decimation unit, we present the optimal decimation scheme and also propose low-complexity decimation structures. We then develop low-complexity least-mean squares (LMS) and recursive least squares (RLS) algorithms for the proposed scheme along with automatic rank and branch adaptation techniques. An analysis of the convergence properties and issues of the proposed algorithms is carried out and the key features of the optimization problem such as the existence of multiple solutions are discussed. We consider the application of the proposed algorithms to interference suppression in code-division multiple-access (CDMA) systems. Simulations results show that the proposed algorithms outperform the best known reduced-rank schemes with lower complexity.     

A novel wavelet-based generalized sidelobe canceller

This paper presents a novel narrow-band adaptive beamformer with the generalized sidelobe canceller (GSC) as the underlying structure. The new beamformer employs a wavelet-based approach for the design of the blocking matrix of the GSC, which is now constituted by a set of regular M-band wavelet filters. Such a construction of the blocking matrix can not only block the desired signals from the lower path as required provided the wavelet filters have sufficiently high regularity, but it also encompasses the widely used one with ones and minus ones along the diagonals as a special case. In addition, it possesses two advantageous features. First, the eigenvalue spreads of the covariance matrices of the blocking matrix outputs, as demonstrated in various scenarios, are decreased as compared with those of previous approaches. Since the popular least mean squares (LMS) algorithm has been notorious for its slow convergence rate, the reduction of the eigenvalue spreads can, in general, accelerate the convergence speed of the succeeding LMS algorithm. Second, the new beamformer belongs to a specific type of partially adaptive beamformers, wherein only a portion of the available degree of freedom is utilized in the adaptive processing. As such, the overall computational complexity is substantially reduced when compared to previous works. The issues of choosing the parameters involved for superior performance are also addressed. Simulation results are furnished as well to justify this new approach     

Two-Dimensional Adaptive Array Beamforming With Multiple Beam Constraints Using a Generalized Sidelobe Canceller

This paper deals with the problem of two-dimensional (2-D) adaptive array beamforming with multiple beam constraints (MBC) using a generalized sidelobe canceller (GSC). We present a method for the construction of signal blocking matrix required by the 2-D GSC. The resulting 2-D adaptive beamformer can provide almost the same performance as conventional 2-D adaptive beamformers based on a linearly constrained minimum variance (LCMV) criterion. The effectiveness of the proposed GSC is that the construction of the required signal blocking matrix requires only the computation of a few entries from analytical formulas. In comparison with conventional methods, the proposed technique gets rid of the computational complexity due to the eigendecomposition required for finding the 2-D signal blocking matrix. For dealing with the performance degradation due to coherent interference, we present a 2-D weighted spatial smoothing scheme to effectively alleviate the coherent jamming effect. Several simulation examples are provided for illustration and comparison.     

Design of partially adaptive arrays using the singular-valuedecomposition

The objective of partially adaptive array design is to reduce the number of adaptive weights without significantly degrading the performance of the adaptive array. Previous work includes numerical techniques for approximately minimizing the average generalized sidelobe canceller (GSC) output power for a desired number of adaptive weights, where the average is over a range of jammer parameters. Our new “power-space method” also attempts to minimize the average GSC output power, but under a constraint that the reduced-dimensional solutions for all of the scenario trials lie in the same subspace. This constraint allows us to use the singular value decomposition to get the rank-reducing transformation, thereby simplifying the optimization problem. Using computer simulation, we show that our transformation yields lower output power in comparison with a previously reported example that used the numerical methods. The simplicity of the method allows large ranges of jammer parameters to be considered, Using our transformation, we consider how closely the reduced-rank solutions match the full-rank solutions and how the quality of this match relates to the output power of the partially adaptive array