平面阵线性约束自适应单脉冲测角算法

平面阵单脉冲测角具有方位、俯仰二维方向信息耦合的特点,需要利用二维单脉冲信息联合进行角度估计。本文基于最大似然单脉冲测角算法,采用线性约束自适应波束形成方法,将二维角度联合估计简化为一维角度估计,具有较好的可实现性。计算机仿真结果验证了该方法的有效性。     

A Physically Constrained Maximum-Likelihood Method for Snapshot-Deficient Adaptive Array Processing

This paper presents a physically constrained maximum-likelihood (PCML) method for spatial covariance matrix and power spectral density estimation as a reduced-rank adaptive array processing algorithm. The physical constraints of propagating energy imposed by the wave equation and the statistical nature of the snapshots are exploited to estimate the ldquotruerdquo maximum-likelihood covariance matrix that is full rank and physically realizable. The resultant matrix may then be used in adaptive processing for interference cancellation and improved power estimation in nonstationary environments where the amount of available data is limited. Minimum variance distortionless response (MVDR) power estimates are computed for a given environment at different levels of snapshot support using the PCML method and several other reduced-rank techniques. The MVDR power estimates from the PCML method are shown to have less bias and lower standard deviation at a given level of snapshot support than any of the other reduced-rank methods used. Furthermore, the estimated power spectral density from the PCML method is shown to offer better low-level source detection than the MVDR power estimates.     

基于子空间的线性约束最小二乘恒模算法

线性约束最小二乘恒模算法能够有效克服恒模算法中存在的干扰捕获问题,然而,在实际的系统中,由于受到来自噪声子空间分量的影响,从而导致性能下降。针对这一问题,该文提出了一种基于子空间方法的线性约束最小二乘恒模(SLSCM)多用户检测算法并对其性能进行了理论分析。该算法收敛速度快,信道跟踪能力强,有较好的输出信干噪比和误码率性能。理论分析和仿真实验验证了该文算法的有效性和优越性。     

一种降秩线性约束自适应波束形成算法

线性约束最小方差法可在多项约束条件下提供较好的干扰抑制能力,然而在低快拍数、固定零点和自适应干扰零点重合的条件下,该算法性能严重下降。该文提出了一种新的线性约束自适应波束形成算法———基于变换的线性正交投影算法,可较好地克服上述缺点,且计算量较小。理论分析与仿真结果验证了算法的有效性。     

线性受限最小二乘恒模盲多用户检测算法

本文将线性受限条件运用于最小二乘恒模算法,提出了一种线性受限最小二乘恒模盲多用户检测算法,称为LCLSCMA,并将它与传统的最小二乘恒模算法(LSCMA)进行了仿真比较。仿真结果表明,本文提出的LCLSCMA算法比传统的最小二乘恒模算法的信干比性能和误码率性能要好,特别在低信噪比强多址干扰情况下性能仍优于LSCMA。     

The Linearly Constrained LSCMA for Blind Multi-user Detection

The least square constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. The proposed algorithm ensures the algorithm convergence to the desired user. Thus the performance of the system is improved. The simulation results demonstrate that the proposed algorithm offers faster convergence rate and provides better output signal-to-interference-plus-noise-ratio (SINR) and bit error rate (BER) performance compared with the traditional LSCMA.     

基于特征空间的线性约束最小方差波束形成器

该文把线性约束和特征空间技术相结合,提出了基于特征空间的线性约束最小方差波束形成器(ELCMVB),该波束形成器是把线性约束最小方差波束形成器(LCMVB)的预定约束方向导向矢量向信号子空间投影,再结合线性约束用LCMVB来得到权矢量。经分析表明,ELCMVB的性能优于GEIB的性能。与GEIB相比,ELCMVB不用计算修改的信号子空间,避免了因计算修改信号子空间而容易造成的计算不稳定性,且其波束形成性能基本上不受零点约束位置的影响。最后,给出了计算机仿真结果,并证实了ELCMVB的优越性。     

MC-CDMA系统中一种预处理线性受限多用户检测算法

本文提出了MC-CDMA系统中一种预处理线性受限最小二乘恒模多用户检测算法(Pre-processed Linearly Con- strained Least Squares Constant Modulus Multi-user detection Algorithm),称为P-LCLSCMA。它是对MC-CDMA接收信号进行预处理,即在每次迭代中,用已估信道参数先消除信道在各个子载波上对期望用户信号的影响,然后对处理后的信号用P- LCLSCMA算法进行多用户检测,从而确保了算法收敛于期望信号,提高了系统的检测性能。我们对这种新算法分别在下行链路和上行链路进行了仿真,仿真结果表明,本文提出的P-LCLSCMA算法的误码率和信干比性能得到了提高。     

A Practical Simple Geometry and Gain/Phase Calibration Technique for Antenna Array Processing

A calibration technique is proposed in this paper for an arbitrary array. This technique estimates the array sensor gain/phase and geometry with a set of simultaneous equations formed by using the MUSIC null spectrum property. Note that the technique does not use iterative calculation in estimating the array parameters and hence it has no convergent problem; however, it requires that $n$ directions of arrival (DOAs) of signal sources to be known to calibrate the array which is perturbed $n$-dimensionally. The efficacy of the method is demonstrated by means of simulations and on experimental data collected with an antenna array operating in high-frequency radio band.      

光纤光栅传感器阵列时域地址查询的信号处理技术

用电光调制器将连续光波调制成脉冲光波 ,借助非平衡Michelson扫描干涉仪将波长漂移信息变为相移信息 ,来自各传感元的信号利用程控延时信号对电子开关通导的控制来实现时域地址查询 ,经带通滤波处理 ,用相位计监测相移进而判断应变的大小。实验证实分辨率为 6nε的 4光栅系统传感灵敏度的实验值为 1 63Deg/με。     

一种改进的特征空间线性约束波束形成器

当期望信号相对干扰信号较弱时,基于特征空间的线性约束最小方差波束形成器（ELCMVB）易把期望信号当成噪声,导致信号子空间的降维,波束形成效果不理想。文章提出了一种基于信号子空间扩展的方法来弥补信号子空间的降维,理论与计算机仿真都说明了该方法的可行性。     

Low-Complexity Iterative Adaptive Linearly Constrained Minimum Variance Beamformer

The closed-form solution of linearly constrained minimum variance (CF-LCMV) suffers heavy computational burden from two-matrix inversion when computing the optimal vector. CF-LCMV is not an adaptive beamformer and performs poorly with low signal-to-interference-plus-noise-ratio (SINR) and small number of snapshots. In this study, we derive a low-complexity iterative adaptive LCMV (IA-LCMV) algorithm based on conjugate gradient (CG) technique with threefold advantages: first, IA-LCMV can remarkably alleviate the complexity of CF-LCMV; second, IA-LCMV can adjust output adaptively with comparable convergence speed. Finally, it shows robust performance against low SINR and small number of snapshots. Simulation results demonstrate the efficacy of our proposed algorithms.     

线性约束自适应单脉冲测角算法约束条件研究

介绍平面阵线性约束自适应单脉冲测角算法原理。分析了选取约束条件的方法。最后给出一种提高线性约束自适应单脉冲测角算法性能的途径。     

一种改进的线性受限共轭梯度常模盲多用户检测算法

提出了一种改进的线性受限共轭梯度常模算法(M-LCCGCMA),其核心是采用在最优自适应步长的方法对算法进行优化,并推导出步长的解析形式.将本文算法在加性白高斯和多径衰落信道的环境中进行了仿真.仿真结果表明,该算法的信干比性能和误码率性能均比现有的自适应步长常模算法要好.     

A Non‐Canonical Linearly Constrained Constant Modulus Algorithm for a Blind Multiuser Detector

We investigate an alternative blind adaptive multiuser detection scheme based on a non‐canonical linearly constrained constant modulus (LCCM) criterion and prove that, under the constrained condition, the non‐canonical linearly constrained constant modulus algorithm (LCCMA) can completely remove multiple‐access interference. We further demonstrate that the non‐canonical LCCM criterion function is strictly convex in the noise‐free state, and that under the constrained condition, it is also strictly convex even where small noise is present. We present a simple method for selecting the constant as well as a stochastic gradient algorithm for implementing our scheme. Numerical simulation results verify the scheme's efficiency.     

改进的线性受限共轭梯度常模算法

提出了一种改进的线性受限共轭梯度常模算法,称之为M—LCCGCMA。其核心是采用最优自适应步长的方法对已有算法进行优化,并推导出步长的解析形式,确保了算法收敛于期望用户,提高了系统性能。通过对算法代价函数的理论分析,得出了算法收敛的条件。将算法在加性白高斯和多径衰落信道的环境中进行了仿真,结果表明,该算法的信干比性能和误码率性能均比现有的自适应步长常模算法要好。     

Processing for Aberrant Sampled Intensities in Linearly Coded Profilometry

Aiming at the local intensity anamorphosis in general linearly coded profilometry, the post treatment methods under different decoding conditions are proposed, in which the distorted intensities are rehabilitated or eliminated by our routines.The experimental results show that the measuring error and system cost can be effectively reduced while the reliability can be improved by introducing those methods into decoding process.     

On the Implementation of the Linearly Constrained Minimum Variance Beamformer

The linearly constrained minimum variance (LCMV) method, which allows multiple linear constraints, is an extension of the classical minimum variance distortionless response filter. Depending on the spatial filter length and the desired frequency grid, a direct computation of the resulting spatial beam pattern may be prohibitive. This brief exploits the rich structure of the LCMV expression to find a nonrecursive computationally efficient implementation of the LCMV beamformer with fixed constraints. The implementation is formed via the use of the matrix inversion lemma and the fast Fourier transform. Numerical simulations indicate a dramatic computational gain, especially for fine frequency grids and multiple constraints     

一种提高阵列处理分辨力的新算法

为了改进分辨力,本文提出了一种新的阵处理方法,此法基于一种新的阵几何与空间滤波器,这种阵几何有效地增加了阵孔径,为了避免空间频率的混叠,空间滤波器能提高有效信噪比(SNR)。模拟结果表明本文方法较之现有谱估计方法有明显改进。     

A Linear Systolic Array for Real-Time Morphological Image Processing

Mathematical morphology has proven to be a very useful tool for applications such as smoothing, image skeletonization, pattern recognition, machine vision, etc. In this paper we present a 1-dimensional systolic architecture for the basic gray-scale morphology operations: dilation and erosion. Most other morphological operations like opening and closing, are also supported by the architecture since these operations are combinations of the basic ones. The advantages of our design stem from the fact that it has pipeline period = 1 (i.e., 100% processor utilization), it requires simple communications, and it is exploiting the simplicity of the morphological operations to make it possible to implement them in a linear target machine although the starting algorithm is a generalized 2-D convolution. We also propose a Locally Parallel Globally Sequential (LPGS) partitioning strategy for the best mapping of the algorithm onto the architecture. We conclude that for this particular problem LPGS is better than LSGP in a practical sense (pinout, memory requirement, etc.). Furthermore, we propose a chip design for the basic component of the array that will allow real-time video processing for 8- and 16-bit gray-level frames of size 512 × 512, using only 32 processors in parallel. The design is easily scalable so it can be custom-taylored to fit the requirement of each particular application.