An improved subspace tracking algorithm for blind adaptive multiuser detection

As the Projection Approximation Subspace Tracking with deflation(PASTd) algorithm is sensitive to impulsive noise, an improved subspace tracking algorithm is proposed and applied to blind adaptive multi-user detection. Simulation results show that the improved PASTd algorithm not only remains the properties of the conventional PASTd algorithm, but also has good Bit Error Rate(BER) performance in impulsive noise environment, thus it can effectively improve the system performance.     

An improved stochastic gradient algorithm for principal componentanalysis and subspace tracking

We propose a new stochastic gradient algorithm for principal component analysis and subspace tracking, requiring O(nm) operations per update, where n is the number of input signals, and m is the signal subspace dimension. A parallel version with problem size independent throughput is obtained at the expense of O(n²) additional flops     

基于自适应子空间追踪的层析SAR成像算法

在对分布式SAR进行数据降采样下会信号的三维处理增加不少难题。其中在解决频域距离弯曲校正时,由于方位向的降采样使数据不再满足奈奎斯特定理,导致在多普勒域计算距离偏移量时会出现数据的混叠。针对该问题,提出了基于LMS估计的距离弯曲校正算法,该方法根据最小均方估计思想估计权值系数完成方位向的插值,有效解决了该条件下的距离弯曲问题。针对高层成像中稀疏阵列导致基线数量有限且不均导致成像分辨率差的问题,提出了基于压缩感知的自适应子空间追踪方法来提高高度维成像的分辨性能,相比于正交匹配追踪算法,它能实现对迭代得到候选解的同步检验,避免了错误结果积累的问题,有效提高了成像的质量。     

基于特征子空间的自适应多视角目标跟踪算法

提出了一种新的自适应特征子空间跟踪算法,该算法通过计算跟踪目标的似然来自适应调整模型更新的权重,以减小更新过程中样本误差积累导致的模型漂移.同时,跟踪算法利用多视角贝叶斯理论框架进行多视角的信息融合,并对跟踪模型进行分块处理和更新,以提高跟踪精确度.仿真结果表明,本算法比对比算法的跟踪误差更小,并能够更好地解决目标遮挡和形变等问题,从而得到精确、高效的跟踪结果.     

The fast recursive row-Householder subspace tracking algorithm

We introduce a new sequential algorithm for tracking the principal subspace and, optionally, the r dominant eigenvalues and associated eigenvectors of an exponentially updated covariance matrix of dimension N×N, where N>r. The method is based on an updated orthonormal-square (QS) decomposition using the row-Householder reduction. This new subspace tracker reaches a dominant complexity of only 3Nr multiplications per time update for tracking the principal subspace, which is the lower bound in dominant complexity for an algorithm of this kind. The new method is completely reflection based. An updating of inverse matrices is not used.     

A robust past algorithm for subspace tracking in impulsive noise

The PAST algorithm is an effective and low complexity method for adaptive subspace tracking. However, due to the use of the recursive least squares (RLS) algorithm in estimating the conventional correlation matrix, like other RLS algorithms, it is very sensitive to impulsive noise and the performance can be degraded substantially. To overcome this problem, a new robust correlation matrix estimate, based on robust statistics concept, is proposed in this paper. It is derived from the maximum-likelihood (ML) estimate of a multivariate Gaussian process in contaminated Gaussian noise (CG) similar to the M-estimates in robust statistics. This new estimator is incorporated into the PAST algorithm for robust subspace tracking in impulsive noise. Furthermore, a new restoring mechanism is proposed to combat the hostile effect of long burst of impulses, which sporadically occur in communications systems. The convergence of this new algorithm is analyzed by extending a previous ordinary differential equation (ODE)-based method for PAST. Both theoretical and simulation results show that the proposed algorithm offers improved robustness against impulsive noise over the PAST algorithm. The performance of the new algorithm in nominal Gaussian noise is very close to that of the PAST algorithm.     

An algorithm for formant tracking

A formant tracking algorithm than first forms strings of spectral peaks considering only the relative positions of the peaks and then assigns these strings to the formants according to the relative positions and lengths of the strings is presented. An example is also given.     

AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter,on the basis of Frequent Pattern(FP) tree,the support function to update FP-tree is introduced,then an incremental FP(IFP) algorithm for mining association rules is proposed.IFP algorithm considers not only adding new data into the database but also reducing old data from the database.Furthermore,it can predigest five cases to three case .The algorithm proposed in this letter can avoid generating lots of candidate items,and it is high efficient.     

迭代子空间跟踪和结构约束的自适应波束形成算法

自适应波束形成(adaptive beam-forming:ABF)方法通过对阵列数据进行加权处理来获得最大的输出信干噪比,对采样协方差矩阵依赖性较大.在小快拍和通道随机响应误差条件下,采样协方差矩阵的估计值与真实值通常存在较大差异,严重恶化了基于线性约束最小均方误差准则的自适应波束形成方法的干扰抑制性能.针对该问题,本文基于子空间投影类波束形成方法的思想,提出采用迭代子空间跟踪和结构约束的自适应波束形成方法.该方法首先利用clearing技术在训练样本集上依次跟踪主特征矢量并构成信号子空间;然后根据子空间投影类波束形成权矢量的结构特性计算自适应加权矢量.仿真结果表明本文方法能有效提高自适应波束算法的输出信干噪比.     

Noniterative subspace tracking

A rank-one spherical subspace update that is appropriate for subspace-based methods like MUSIC and minimum norm is introduced. This noniterative, highly parallel, numerically stabilized, subspace update is closely related to rank-one eigenstructure updating. However, a rank-one subspace update involves less computation than simple rank-one correlation accumulation. Moreover. The frequency tracking capabilities of the noniterative subspace update are virtually identical to and in some case more robust than the more computationally expensive eigen-based methods     

Interference cancellation in GPS receiver using noise subspace tracking algorithm

In this paper, we address the problem of interference cancellation in global positioning system (GPS) receiver using a two-step approach: subspace projection technique and maximum signal-to-noise ratio (MSNR) beamforming. The interference signals can be effectively suppressed by projecting the received signal on the noise subspace. Here noise subspace tracking algorithm is employed to estimate the noise subspace directly. We then apply a beamformer to maximize the signal-to-noise ratio of the interference-free signal. Simulation results show that our approach can effectively eliminate the strong interference and enhance the performance of the GPS receiver.     

基于子空间跟踪的盲MMSE多用户检测算法

在深入研究子空间方法盲多用户检测器子空间跟踪算法的基础上,针对OPAST算法中矩阵Z(i)的迭代运算过程导致子空间跟踪算法发散的问题,提出一种改进算法--newOPAST算法,并应用于盲MMSE多用户检测器中.仿真结果表明,该算法具有稳定的全局收敛性,并保持了较低的算法复杂度,而其输出信噪比和误码率性能接近高算法复杂度的经典算法.     

Multiple target angle tracking algorithm using angular innovationsextracted from signal subspace

A multiple target angle tracking algorithm that can avoid data-association problems is proposed. This algorithm uses a nonlinear equation between the signal subspace and angular innovation, and the angular innovation is obtained by solving the linear approximation of the nonlinear equation in the least squares sense     

Projection approximation subspace tracking

Subspace estimation plays an important role in a variety of modern signal processing applications. We present a new approach for tracking the signal subspace recursively. It is based on a novel interpretation of the signal subspace as the solution of a projection like unconstrained minimization problem. We show that recursive least squares techniques can be applied to solve this problem by making an appropriate projection approximation. The resulting algorithms have a computational complexity of O(nr) where n is the input vector dimension and r is the number of desired eigencomponents. Simulation results demonstrate that the tracking capability of these algorithms is similar to and in some cases more robust than the computationally expensive batch eigenvalue decomposition. Relations of the new algorithms to other subspace tracking methods and numerical issues are also discussed     

On-line subspace algorithms for tracking moving sources

Proposes a class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors. The proposed methods estimate the DOA using only linear operations on the data, and can hence be implemented in a very efficient manner. Furthermore, these methods can accommodate more general noise models than the spatially white noise model commonly used in the literature. Large sample expressions are derived for the variance of the estimates obtained by using the proposed techniques. A comparative statistical study is performed in which comparisons against MUSIC are considered. It is found that usually MUSIC offers slightly more accurate DOA estimates at the cost of an increased computational burden and a more restrictive noise model. The paper includes simulation results lending support to the theoretical results obtained     

Inverse QR iterative method for subspace tracking

The Bi-LS method based on QR decomposition provides a convenient framework for developing efficient subspace tracking algorithms. To overcome the shortcoming of the backsubstitution step and improve the parallel architecture of the Bi-LS algorithms, a Bi-LS subspace tracking algorithm based on Inverse QR (IQR) decomposition is developed. The proposed IQR iterative algorithm for subspace tracking is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed IQR subspace tracking algorithm.     

Two algorithms for fast approximate subspace tracking

New fast algorithms are presented for tracking singular values, singular vectors, and the dimension of a signal subspace through an overlapping sequence of data matrices. The basic algorithm is called fast approximate subspace tracking (FAST). The algorithm is derived for the special case in which the matrix is changed by deleting the oldest column, shifting the remaining columns to the left, and adding a new column on the right. A second algorithm (FAST2) is specified by modifying FAST to trade reduced accuracy for higher speed. The speed and accuracy are compared with the PL algorithm, the PAST and PASTd algorithms, and the FST algorithm. An extension to multicolumn updates for the FAST algorithm is also discussed     

Adaptive model updating for robust object tracking

In this paper, we exploit features extracted from convolutional neural network (CNN) to be better utilized for visual tracking. It is observed that CNN features in higher levels provide semantic information which is robust to appearance variations. Thus we integrate the hierarchical features in different layers of a deep model to correlation filter tracking framework. More specifically, correlation filters are learned on each layer to encode the object appearance. The peak-to-sidelobe ratio (PSR) is employed to measure the differences between image patches. To leverage the robustness of our model, we develop an adaptive model updating scheme to train the correlation filters according to different response maps. Extensive experimental results on three large scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods.     

Bi-iteration SVD subspace tracking algorithms

We present a class of fast subspace tracking algorithms that arise from a straightforward extension of Bauer's (1957) classical bi-iteration to the sequential processing case. The bi-iteration concept has an unexpected potential in subspace tracking. Our new bi-SVD subspace trackers are well structured and show excellent convergence properties. They outperform the TQR-SVD subspace tracking algorithm. Detailed comparisons confirm our claims. An application to rank and data adaptive signal reconstruction is also discussed     

Feedback assisted transmission subspace tracking for MIMO systems

This paper describes a feedback assisted stochastic gradient algorithm for transmission tracking of the dominant channel subspaces for multiple-input-multiple-output (MIMO) communications systems. Subspace tracking is introduced as a means of tracking multiple transmission weights, being the MIMO generalization of beam steering in the familiar multiple-input-single-output case. The subspace solution approximates that of water filling (WF) in some cases, without the complete rate/power allocation required by WF. The gain of subspace tracking in low rank systems is demonstrated, particularly, in the case where the number of transmit antennas exceeds the number of receive antennas. Simulations of ergodic capacity show the utility of both subspace tracking in general and of the specific adaptation algorithm, and simulations of frame-error rates show the utility in a specific coding example.