基于嵌套张量模型的MIMO中继系统组合接收算法

目前在单向双跳多输入多输出(MIMO)中继系统中,基于嵌套张量模型的接收算法主要采用单步交替最小二乘(ALS)和KRF(Khatri-Rao Factorization)算法.在时变信道且实时性要求较高场景下,计算复杂度高是制约其应用的主要因素.为此,在对单向双跳MIMO中继系统建模基础上,提出了基于嵌套张量模型的双步组合接收算法.该算法通过对接收的数据张量进行重建,将符号估计和信道估计分离,充分利用ALS和KRF的算法优势,有效降低了计算复杂度.同时,对算法的可辨识性进行了分析.仿真结果表明,该算法保持了与传统嵌套PARAFAC的最小二乘(Nested PARAFAC ALS)算法的相同估计性能,在源天线个数变化时,计算复杂度降低了80%以上;在中继天线个数变化时,计算复杂度降低了50%以上.     

基于最小范数的四种相位解包裹算法比较

为了快速准确地对含有噪声及欠采样区域的包裹相位图进行展开,采用理论分析与计算机模拟及实验验证相结合的方法,对基于快速傅里叶变换(FFT)的最小二乘法(FFT-LS)、基于离散余弦变换(DCT)的最小二乘法(DCT-LS)、基于横向剪切干涉的最小二乘法(LS-LS)和预条件共轭梯度法(PCG)的四种相位解包裹算法作了对比研究。结果表明:DCT-LS算法运行速度最快,LS-LS算法次之,PCG算法速度最慢,PCG算法对于噪声的免疫力最强,LS-LS算法处理欠采样的效果最好。     

一种新的双基地MIMO雷达快速多目标定位算法

针对双基地MIMO雷达目标定位问题,该文提出一种基于三阶张量分解的快速多目标定位算法。该算法首先将匹配滤波输出转化为三阶张量,并对其进行降维预处理,然后利用交替最小二乘(ALS)算法估计收发阵列流型矩阵和多普勒矩阵,最后通过谱估计算法恢复目标收发角和多普勒频率。同时利用线性搜索加快ALS算法的收敛速度。与现有算法相比,该算法避免了2维谱峰搜索和协方差矩阵估计,得到的目标三参数自动配对,不仅提高了估计性能,而且有效降低了运算量和存储量。仿真结果证明了所提算法的有效性和优越性。     

数字预失真器模型参数辨识算法研究

数字基带预失真技术是补偿功放非线性的最有效方法之一,而自适应算法的选择很大程度上影响着系统的预失真性能。其中,最小均方误差(LMS)算法和递归最小二乘法(RLS)是最常用的自适应收敛算法,在综合分析两种算法优缺点的基础上,提出了一种基于正弦函数的归一化LMS(NLMS)算法和RLS算法的组合算法。仿真结果表明,该组合算法在达到与RLS同样预失真精度的前提下,可大大减小计算量。     

基于有限记忆最小二乘的雷达误差配准算法

针对非合作目标多雷达组网中的系统误差配准问题,介绍了基于地心地固坐标系的三维空间配准模型,以及常用的雷达误差配准方法——最小二乘法。为了能够实时估计出系统偏差,并解决最小二乘等批处理算法在求解系统误差过程中数据量、计算量和存储量随时间递增的问题,提出了基于广义最小二乘法的有限记忆最小二乘法。通过一个实例,对新算法进行Matlab仿真,结果证明了算法的正确性和有效性。     

基于最小类内绝对差和最大差的图像阈值分割

阈值分割是图像处理中一种简单有效的图像分割方法,应用极为广泛.阈值选取是阈值分割的关键.最小类内方差法(Otsu法)因其分割精确,适用范围广而成为广泛采用的一种图像阈值分割方法,它实质上是最小二乘法(基于L2范数).与此不同,本文提出了基于最小类内绝对差(基于L1范数)及最小类内最大差(基于L∞范数)的图像阈值分割算法,并导出了这两种方法的二维算法形式.文中给出了实验结果,并进行了分析与比较.结果表明,这两种方法在某些类型图像下,阈值分割效果明显优于最小类内方差法,而其二维算法的分割效果普遍优于相应的一维算法.     

基于Item-User网络的概率矩阵分解推荐算法

传统的矩阵分解算法过分依赖于用户-评分矩阵,导致推荐的准确性不高。为了进一步提高推荐的准确性,文中提出了一种基于Item-User网络的概率矩阵分解推荐算法。该算法不仅通过有向图和信任关系的传递性对用户信任矩阵进行改进,而且引入了物品相似矩阵,全面考虑用户与项目的关系,并对这三个矩阵建立联合概率分解模型。最后构建目标函数,通过最小二乘法求出误差值和预测评分。实验结果表明,该算法相对其他算法有较好的预测结果和解释机制。     

空域色噪声下基于张量Vandermonde因子矩阵重构的MIMO雷达角度估计

本文提出了一种空域色噪声环境下基于张量Vandermonde因子矩阵重构的多输入多输出(Multiple Input Multiple Output, MIMO)雷达角度估计方法。该方法首先将不同脉冲的匹配滤波输出进行互相关以实现对接收信号的去噪处理;然后,根据因子矩阵先验结构信息建立具有Vandermonde约束的四阶张量典范分解/并行因子分析(Canonical Decomposition/Parallel Factor Analysis, CANDECOMP/PARAFAC)模型,并推导了基于约束交替最小二乘(Alternating Least Squares, ALS)的迭代求解方法,在交替迭代过程中充分利用因子矩阵的Vandermonde结构,通过构造Toeplitz矩阵并进行Vandermonde分解的方式获得因子矩阵估计值;最后,通过最小二乘拟合方法估计目标角度。仿真结果表明本文算法能够有效提高MIMO雷达在空域色噪声下的角度估计性能。     

RLS、VSSLMS算法应用于天线组阵的分析及仿真

针对天线组阵常用相关算法Simple、Sumple、Eigen(基于本征值)和LS(最小二乘法)的不足,提出递推式最小二乘法(RLS)和变步长最小均方法 (VSSLMS)这2种新型算法,对其应用于深空网天线组阵进行了研究。对这2种算法进行了仿真,结果表明,RLS算法存在信噪比合成性能随组阵天线数目增加而下降的缺陷,VSSLMS算法具有强信号时信噪比合成性能迅速提高的优点;在接收信号弱并且组阵天线数目较少情况下,RLS算法的信噪比合成性能略好于VSSLMS算法;当接收信号较强尤其是组阵天线数目较多时,VSSLMS算法的信噪比合成性能反而比RLS算法更好一些。     

基于TLS的主动全向声呐浮标目标定位与跟踪

采用最小二乘( LS)拟和法进行主动全向浮标目标定位时容易受测量误差的影响,为此提出一种基于总体最小二乘法( TLS)的定位算法,首先对主动全向浮标的定位方程和测速方程进行变换形成线性的量测方程,然后利用潜艇测距误差和浮标定位误差的随机性,基于总体最小二乘法对量测方程进行递归解算,解算过程根据潜艇运动的物理限制,取运动模型的状态预测值作为递归过程的初值,最终实现了测量误差平滑化和航迹的整体优化。仿真结果显示算法较之最小二乘法具有更好的抑制噪声能力和机动目标跟踪能力。     

QR methods of O(N) complexity in adaptive parameter estimation

Recent attention in adaptive least squares parameter estimation has been focused on methods derived from the QR factorization owing to the fact that the QR-based algorithms are much more numerically stable and accurate than the traditional pseudo-inverse-based algorithms, also known as normal equation-based algorithms, even though the former is usually much slower than the latter. This paper presents a fast adaptive least squares algorithm for the parameter estimation of linear and some nonlinear time-varying systems. The algorithm is based on Householder transformations. As verified by simulation results, this algorithm exhibits good numerical stability and accuracy. In addition, the new algorithm requires computation and storage with order of O(N) rather than O(N²) where N is the number of unknown parameters to be estimated. This algorithm can be easily extended to construct other kinds of algorithms, such as the generalized adaptive least squares algorithm, the augmented matrix algorithm, and the maximum likelihood algorithm     

Online identification of inertial parameters of a robot with partially combined links using IMU sensing

Accurate calculation of joint acceleration online is critical for detecting robot collisions when used in inverse dynamics to calculate joint torques. The conventional method for calculating joint acceleration is to employ the twice differentiation based on encoder data, which suffers from the problem of causing joint acceleration with excessive noise. To address this problem, an extended Kalman filter (EKF) sensor fusion method is proposed in this study, which combines data from encoder and inertial measurement unit (IMU) sensors to estimate joint motion information accurately. In an inertial parameter identification experiment, the first three links of a seven-degree-of-freedom (DoF) robot remain stationary and unexcited, so that the results of the identification of the last four links will be affected by their initial positions. To examine the effect of the initial positions of the first three links without introducing an excessive number of variables, joints 4+5 and 6+7 were combined. Furthermore, to improve the accuracy of the calculated joint torques, the fmincon() function is used to optimize a constrained nonlinear multivariable equation containing the joint position of the first three links, and the inertial parameters of the combined links are determined using the recursive least squares algorithm. The simulation and experimental results demonstrate that the joint motion information estimated by EKF is more accurate than conventional differentiation based on encoder output. In addition, the inertial parameters of the two combined links are calculated using an online least squares algorithm, which is computationally more efficient and practical for real-world scenarios than the conventional offline least squares algorithm.     

Joint recommendation algorithm based on tensor completion and user preference

Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average.     

Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.     

Low-complexity tensor-based blind receivers for MIMO systems

In the tensor-based MIMO receivers, the multidimensional MIMO signals first are expressed as a third-order tensor model, wherein the factor matrices of tensor model are corresponding time/frequency, symbols, code/diversity of signals. A algorithm then is used for fitting this tensor mode, in which the symbols are estimated as a independent factor matrix. Although the performance of tensor-based receivers strongly depends on the initializations of the factor matrices. However, due to the absence of a priori on channels, these initializations are done randomly in alternating least squares (ALS), a basic algorithm for fitting the tensor models. In order to avoid these random initializations, this paper proposes two algorithms for fitting the tensor models. The first one, called delta bilinear ALS (DBALS) algorithm, where we exploit the increment values between two iterations of the factor matrices, refine these predictions by using the enhanced line search and use these refined values to initialize for two factor matrices. The second one, called orthogonal DBALS algorithm that takes into account the potential orthogonal in factor matrix for the DBALS algorithm, to provide the initialization for this factor matrix. By this way, we avoid random initializations for three factor matrices of tensor model. The performance of proposed receivers is illustrated by means of simulation results and a comparison is made with traditional ALS algorithm and other receivers. Beside a performance improving, our receivers give a lower complexity due to avoid random initializations.     

Finite word-length effects in recursive least squares algorithms with application to adaptive equalization

In this paper we provide a summary of recent and new results on finite word length effects in recursive least squares adaptive algorithms. We define the numerical accuracy and numerical stability of adaptive recursive least squares algorithms and show that these two properties are related to each other, but are not equivalent. The numerical stability of adaptive recursive least squares algorithms is analyzed theoretically and the numerical accuracy with finite word length is investigated by computer simulation. It is shown that the conventional recursive least squares algorithm gives poor numerical accuracy when a short word length is used. A new form of a recursive least squares lattice algorithm is presented which is more robust to round-off errors compared to the conventional form. Optimum scaling of recursive least squares algorithms for fixedpoint implementation is also considered.     

Use of adaptive RLS, LMS, and NLMS algorithms for nonlinearity modeling in a modified laser interferometer

Laser heterodyne interferometer is one kind of nano-metrology systems which has been widely used in industry for high-accuracy displacement measurements. The accuracy of the nano-metrology systems based on the laser heterodyne interferometers can be effectively limited by the periodic nonlinearity. In this paper, we present the nonlinearity modeling of the nano-metrology interferometric system using some adaptive filters. The adaptive algorithms consist of the least mean squares (LMS), normalized least mean squares (NLMS), and recursive least squares (RLS). It is shown that the RLS algorithm can obtain optimal modeling parameters of nonlinearity.     

一种改进的基于物质扩散理论的Item-based协同过滤算法

推荐系统和其使用的推荐算法是电子商务系统中最重要的技术之一。推荐算法的准确性是评价算法优劣的一个重要指标。为了设计准确性较高的推荐算法,提出了一种基于物质扩散理论的协同过滤推荐算法(MD-ICF)。实验数据分析表明,这种改进后的算法在排序得分上比传统的ICF（iterative case filtering）算法有14%的提高,证明该算法是有效和实用的。     

A Block Component Model-Based Blind DS-CDMA Receiver

In this paper, we consider the problem of blind multiuser separation-equalization in the uplink of a wideband DS-CDMA system, in a multipath propagation environment with intersymbol-interference (ISI). To solve this problem, we propose a multilinear algebraic receiver that relies on a new third-order tensor decomposition and generalizes the parallel factor (PARAFAC) model. Our method is deterministic and exploits the temporal, spatial and spectral diversities to collect the received data in a third-order tensor. The specific algebraic structure of this tensor is then used to decompose it in a sum of user's contributions. The so-called Block Component Model (BCM) receiver does not require knowledge of the spreading codes, the propagation parameters, nor statistical independence of the sources but relies instead on a fundamental uniqueness condition of the decomposition that guarantees identifiability of every user's contribution. The development of fast and reliable techniques to calculate this decomposition is important. We propose a blind receiver based either on an alternating least squares (ALS) algorithm or on a Levenberg-Marquardt (LM) algorithm. Simulations illustrate the performance of the algorithms.