An adaptive array utilizing an adaptive spatial averaging technique for multipath environments

When the interference is coherent with the desired signal, the conventional adaptive arrays working under the guiding principle of output power minimization tend to cancel the desired signal by using the coherent interference. A technique is described which enables the adaptive array to function even under such an environment. The array is divided into subarrays, whose input correlation matrices are adaptively averaged so as to produce a Toeplitz matrix which would be obtained when the interference did not correlate with the desired signal. The averaged matrix is now free from correlation terms between the desired signal and interference, and therefore may be used to derive the optimum weight for the array element just as in the ordinary radio environment of incoherent interference. Numerical examples show that the new adaptive array is highly capable to suppress the coherent interferences as well as incoherent ones.     

Design of two-dimensional rectangular array beamformers withpartial adaptivity

This paper considers the problem of designing a two-dimensional (2-D) rectangular array beamformer with partial adaptivity. Using the eigenstructure of the signal data received by a 2-D rectangular array beamformer, we first show that the optimal weight matrix when using full adaptivity can be obtained from a set of singular vectors. Then the design problem of using partial adaptivity is formulated. As a result, the optimal solution for the partially adaptive weight matrix can be found by solving two basic problems, namely determining the proper dimension of the partially adaptive weight matrix and the set of the singular vectors. We develop the detection formulas for the information theoretic criteria AIC and MDL to find the proper dimension. Next, an efficient method is presented so that the optimal solution for the set of the singular vectors can be found analytically. We also investigate the required computational complexity. It is shown that 2-D partially adaptive beamforming using the proposed technique requires less computational complexity than 2-D fully adaptive beamforming using conventional techniques. Moreover, computer simulations demonstrate that the proposed 2-D partially adaptive technique provides satisfactory array performance when compared with conventional fully adaptive techniques     

一种快速的特征空间自适应波束形成算法

特征空间自适应波束形成（EBAB）算法的权向量是线性约束最小方差（LCMV）波束形成器的权向量在信号干扰子空间上的投影,因此需要计算量巨大的特征分解。基于信号干扰子空间投影矩阵可利用阵列协方差矩阵进行高精度的有理近似的思想,提出一种快速计算自适应权向量的算法。理论分析和计算机仿真表明,在信号干扰子空间维数已知时,该算法的性能同EBAB算法一致,同时该算法对子空间维数过估计不敏感。     

基于特征空间的降维STAP方法比较研究

系统接收数据的杂噪协方差矩阵完整地包含了杂波和噪声信息,将其做特征分解(EVD)而得到的特征空间,可将杂噪能量按序排列,且各特征矢量相互正交且统计独立.基于特征空间作自适应处理,特别是降维自适应处理,便于探寻理论上的最佳方案.本文分别讨论了互谱法(CSM)、最小功率特征对消(MPE)以及最小范数特征对消(MNE),从特征结构阐明了三种方法的内在关系,比较了它们的杂波抑制性能.     

On adaptive beamforming in correlated noise

This paper deals with the problem of adaptive beamforming in the presence of fully coherent (correlated) noise sources. Two different techniques are developed for minimizing the effects of coherent interference. The first method employs spatial interpolation of the array aperture, followed by spatial smoothing in order to decorrelate the desired signal and the interference. The second technique is based on a simple algebraic transformation for restoring the rank of the array signal correlation matrix, which is normally rank deficient in such situations. This technique is shown to work for nonuniform adaptive arrays as well. Extensive computer simulation results are presented to illustrate the effectiveness of the proposed techniques.This week was supported by the N.R.C., Resident Research Associateship Programme.     

Improved adaptive ing of coherent interference without spatial smoothing

A new scheme for adaptive ing in the presence of coherent interference is proposed that finds the weight vector using a nonsquare correlation matrix without spatial smoothing. Conventional schemes, which use square matrices, correspond to specific cases of the new method. It allows us to increase the dimension of the weight vector such that the ing capability is enhanced. Theoretic performance analysis is made, showing that the output signal-to-interference ratio (SINR) of such a beamformer is proportional to the weight dimension for sidelobe interference. Simulation results confirm this.     

Sparse LCMV beamformer design for suppression of ground clutter inairborne radar

Cancellation of the ground clutter received at an airborne phased array radar is an inherently two dimensional problem. Clutter returns are Doppler shifted due to platform motion forcing the use of processors that can resolve targets in both velocity (Doppler) and azimuth. Fully adaptive processors that operate in both dimensions require prohibitively large computation so that reduced adaptive dimension, or partially adaptive processors must be considered. In conventional partially adaptive linearly constrained minimum variance (LCMV) beamformer design the approach taken has been to represent the interference subspace with some reduced set of vectors, typically the eigenvectors associated with the largest eigenvalues of the interference covariance matrix. This technique does yield good performance but will not give the optimum performance for a given partially adaptive dimension. In this paper, an off-line method for selecting the “best” degrees of freedom to be retained in a partially adaptive design is presented. The sequential algorithm described selects those degrees of freedom that best minimize the beamformer output mean square error. This approach leads to a sparse structure for the transformation matrix, which when implemented in a generalized sidelobe canceller (GSC) structure results in a reduction in the computational load. This approach also allows a reduction in the required adaptive dimension as compared to the eigenvector based approach. Illustrative examples demonstrate the effectiveness of this method     

Blind adaptive multiuser detection for DS/CDMA over multipathfading channels

Using a hard null scheme, multipath fading and multiple access interference suppression can be realised for a multiple constrained minimum variance (MCMV) detector at the same time. A modified version of the MCMV detector is also presented, which utilises the eigenstructure of the correlation matrix to enhance the performance of the MCMV detector. Numerical results demonstrate the effectiveness of the proposed detectors     

分形编码在图像检索中的应用

分形编码在图像压缩方面取得了很好的效果,同时,分形编码也能够用于基于内容的图像检索.本文提出了一种基于块限制的分形编码算法和匹配策略,并将它们用于图像检索.在我们编码算法中,图像会被预先分成互相不重叠的子图像块,然后对这些子图像进行独立地分形编码,从而获得整幅图像的分形码.该编码算法能够在很大程度上减少编码时间.在进行图像间相似性的匹配时,我们采用改进的基于九叉树的分配策略,从而避免全局地进行分形码的匹配,减少了计算量.实验结果说明,我们的编码算法和匹配策略能够比较有效地应用于基于内容的图像检索,在计算时间和存储时间上都优于实验中其它两种方法.     

Reduced‐rank space–time adaptive interference suppression for navigation systems based on QR decomposition and approximations of basis functions

This paper presents a novel reduced‐rank space–time adaptive processing (STAP) algorithm for interference suppression in global positioning system (GPS) receivers with low computational complexity for protection against the multipath and jamming interferences. The proposed STAP algorithm is based on the least‐squares (LS) criterion to jointly optimize a projection matrix, which is used for dimensionality reduction, and the reduced‐rank filter. The main novelties are the design of the projection matrix based on approximations of basis functions, the pattern matching between the projection matrix and the received data, and the derivation of a QR decomposition‐based reduced‐rank recursive LS algorithm for practical implementations. The proposed scheme works on an instantaneous basis, i.e. at each time instant, the most suitable pattern and the rank of the projection matrix are selected to reduce the dimensionality of the received data aiming at minimizing the squared error, while using an improved search algorithm to save the effort in finding the best projection matrix. Simulation results in a GPS system show that compared to existing reduced‐rank and full‐rank algorithms, the proposed algorithm has a much lower computational complexity, and remarkably better performance for interference suppression. Copyright © 2011 John Wiley & Sons, Ltd.     

Design of partially adaptive array beamformers based on informationtheoretic criteria

In this paper, two basic problems in designing partially adaptive array beamformers based on the structure of generalized sidelobe canceller (GSC) are considered. The first problem is to decide the proper dimension of the required adaptive weight vector. Using the information of the array output power, we develop the detection formulas for the information theoretic criteria AIC and MDL to decide the proper dimension of the adaptive weight vector. If the input noise power is unknown a priori, efficient methods are proposed for estimating the input noise power in both cases, with and without the desired signal, to make the detection formulas still feasible. The second problem is to find the most appropriate channel signals for weight adaptation for efficiently canceling interference. An efficient method based on the maximum power reduction criterion is presented for selecting the most desired channel signals from the output of the signal blocking matrix. Theoretical analysis concerning the performance of the proposed methods is made. Computer simulations showing the effectiveness of the proposed methods are also provided     

自适应天线中阵元间互耦的校正＊

本文基于对自适应天线协方差矩阵特征结构的分析,推导出了考虑阵元间互耦时的自适应天线最佳权矢量的解析表达式。阐明了互耦导致自适应天线系统性能下降的机制,最后提出了一种有效的互耦校正方法。     

基于特征空间的相干干扰抑制技术

该文把多线性约束和特征空间技术相结合,提出了基于特征空间的多约束最小方差波束形成器(EM-CMVB),该波束形成器可以有效抑制相干干扰。EMCMVB是利用估计得到的相干干扰方向和阵列信号相关矩阵特征分解来得到修改的信号子空间,再把多约束最小方差波束形成器(MCMVB)的权矢量向修改的信号子空间投影来得出新的权矢量。经分析表明,EMCMVB的性能优于MCMVB的性能。最后,给出了计算机仿真结果,证实了EMCMVB的优越性。     

Joint space-time interpolation for distorted linear and bistatic array geometries

This paper presents a new joint space-time interpolation technique (STINT) to improve the small sample support performance of space-time adaptive processing (STAP) with distorted linear monostatic arrays and linear bistatic array configurations. Brennan's rule for the space-time clutter covariance matrix rank is extended to monostatic linear arrays with arbitrary intersensor spacing, distorted linear arrays and bistatic geometries. It is shown that both distortion in the array geometry and bistatic operation increase the clutter rank and cause the space-time clutter covariance matrix to become range dependent. This results in lower output signal-to-interference-plus-noise ratio (SINR) for the same number of adaptive degrees of freedom and reduced available sample support. This motivates the development of the STINT technique aimed at compensating for the clutter rank inflation, while also making the clutter statistics appear more stationary across range. More specifically, a linear transformation is designed that maps the received clutter across space and time to that which would be received by a "virtual" monostatic side-looking ULA. By mapping the data to form a reduced rank clutter covariance matrix, fewer snapshots are needed for a statistically stable matrix inversion as required in STAP, thereby improving the short observation time performance. Simulation results for a typical airborne radar scenario indicate up to 10-dB SINR improvement can be obtained using STINT with limited sample support.     

Blind channel identification: subspace tracking method without rankestimation

Subspace (SS) methods are an effective approach for blind channel identification. However, these methods also have two major disadvantages: 1) They require accurate channel length estimation and/or rank estimation of the correlation matrix, which is difficult with noisy channels, and 2) they require a large amount of computation for the singular value decomposition (SVD), which makes it inconvenient for adaptive implementation. Although many adaptive subspace tracking algorithms can be applied, the computational complexity is still O(m³), where m is the data vector length. In this paper, we introduce new recursive subspace algorithms using ULV updating and successive cancellation techniques. The new algorithms do not need to estimate the rank of the correlation matrix. Furthermore, the channel length can be overestimated initially and be recovered at the end by a successive cancellation procedure, which leads to more convenient implementations. The adaptive algorithm has computations of O(m² ) in each recursion. The new methods can be applied to either the single user or the multiuser cases. Simulations demonstrate their good performance     

混沌时间序列的Volterra自适应预测滤波器定阶

由于Volterra自适应滤波器的阶数对预测性能有较大的影响,在实际预测中,如何确定Volterra自适应滤波器的最优阶数就成为一个关键问题,该文运用相空间重构理论,推导出了Volterra自适应滤波器的最优阶数等于混沌动力系统的最小嵌入维数,作者用六种混沌时间序列进行实验,结果表明这种定阶方法在混沌时间序列Volterra自适应预测中非常成功,该方法对噪声影响的变化,表现出较好的鲁棒性。     

基于特征结构的自适应波束形成新算法

基于相关矩阵的特征分解，本文提出了一种改进的线性约束最小方差波束形成（ＬＣＭＶ）新算法，当目标信号含于相关矩阵中时，新算法能有效地抑制信号对消现象，并且具有良好的波束保形能力和快速收敛特点，计算机仿真结果验证了这些优点。     

一种单通道自适应零位天线系统的研究与设计

在自适应阵列天线系统中,通常的多通道接收机电路复杂,难以保证各通道性能的一致性,以及使用的可靠性.文中对HF自适应三元阵列零位天线系统提出了一种单通道检测接收机方案,并分析了实现的可行性,给出了具体的实现方法,同时讨论了相关矩阵的算法.为了降低镜频干扰、交调和互调干扰,接收机采用了双中频方案.模拟测试结果证明了该方案的可行性.     

时频子空间拟合波达方向估计

本文提出了一种基于信号空时特征结构的时频子空间拟合方法,利用双线性时频分布构造时频相关矩阵 C _x代替传统的阵列相关矩阵 R _x,通过 C _x的特征分解实现了信号子空间与噪声子空间的分离.该方法在空域和二维时频域同时进行处理,能够区分具有不同时频特征的信号,既适用于平稳信号的场合又适用于时变、非平稳信号的情形,属于空时多维处理的范畴.可以证明,基于平稳信号假设的经典子空间方法是该方法的低维特例.由于包含了时变滤波的过程,因此该方法具有信号选择性以及抗干扰和抗噪声的能力.仿真结果证实了该方法的有效性.