阵列天线系统的最小互信息盲接收器

提出了平衰落信道中阵列天线多输入多输出(MIMO)系统的基于广义高斯分布近似的最小互信息盲接收器(GGMIR)。该接收器采用输出信号的广义高斯分布近似，基于互信息目标函数最小化的方法自适应调整接收器的系数。比较了基于广义高斯分布近似和非线性变换(NLMIR)的两种最小互信息盲接收算法。实验表明基于广义高斯分布近似自适应盲接收算法GGMIR比NLMIR算法有更快的收敛速率，得到的信号星座图有更大的距离和更好的误码性能。     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

多径衰落信道MPSK信号超指数迭代盲解调算法

为了提高多径衰落信道下的盲解调性能,提出了一种结构简单的MPSK信号盲解调算法。首先利用超指数迭代分数间隔盲均衡器实现联合定时同步与均衡,然后对均衡器输出信号进行非线性变换实现载波频偏的估计,最后利用二阶数字判决锁相环跟踪相位变化纠正剩余频偏和相偏。仿真结果表明,在多径衰落信道条件下,与现有算法相比,基于超指数迭代分数间隔盲均衡器的盲解调算法实现简单,误码率低,而且具有收敛速度快、性能稳定等优点。     

基于Hammerstein模型的非线性信道广义线性盲均衡算法

为了提高非线性信道盲均衡的性能、降低运算复杂度,本文以Hammerstein模型代替传统的Volterra级数模型来模拟非线性信道,利用非线性信道接收信号呈现非圆性的特点,构造了一种新的基于Wiener非线性模型的广义线性盲均衡器,并在常模准则的基础上提出了NCWL-CMA和NCWL-CMA Newton-like两种非线性信道广义线性盲均衡器抽头系数更新算法.理论分析和仿真实验结果表明,与传统盲均衡算法相比,新算法显著地降低了剩余码间干扰,提高了收敛速度.     

最小互信息量用于单输入单输出的盲解卷问题

本文利用最小互信息量标准处理盲解卷问题.首先,分别通过Edgeworth拓展和Gram-Charlier拓展方法近似输出的概率密度函数.继而估计互信息量.最后利用最小化互信息量原理求得优化的解卷滤波器系数,仿真实验证明了本文算法的有效性.     

基于最小二乘支持向量机的自适应盲均衡器

提出了一种普适性较强的基于最小二乘支持向量机(LSSVM)的自适应盲均衡器(ABSVME)。该方法根据信号的特征恢复思想,将LSSVM均衡器的输出进行过采样,构造具有时间去相关特性的代价函数,结合Kumar快速算法和静态迭代学习算法在线跟踪信道。通过仿真实验,并与传统恒模盲均衡器和最大似然序列估计均衡器进行比较,结果证明该方法具有优良的非线性均衡能力。     

对多个斯威林4型目标的检测及估计

首先分析了两个斯威林4型目标情况下单脉冲雷达接收机输出信号近似为高斯分布的条件,得到了与现有文献稍有不同的结论;接着研究了多个目标下输出信号近似为高斯分布的条件;在近似条件下,提出采用最大似然估计加最小描述长度算法对多目标进行检测及分辨.仿真结果证实在得到的近似条件下采用上述检测及估计算法是可行的.     

应用粒子滤波器实现混沌通信系统的盲信道均衡

粒子滤波器(Particle filter,PF)是一种结合重要性权重抽样的序贯蒙特卡罗方法,能够应用到任意状态空间模型,并且能较好地估计经过非线性变化后的随机变量的统计特性.本文应用粒子滤波器和信号建模技术研究混沌通信系统的盲信道均衡问题,发展基于混沌的通信系统的盲均衡技术.仿真结果证实了,当Logistic映射作为混沌发生器和通信场景为固定参数与时变衰落信道时,该盲信道均衡器与基于扩展卡尔曼滤波算法的盲均衡器和基于无先导变换的自适应盲均衡器相比,有较好的均衡实现.此外,利用本文的盲均衡算法,实现了一种混沌调制通信系统的解调.     

混合高斯噪声背景下基于多目标优化的节点选择方法

为解决非高斯噪声背景下,基于贝叶斯Fisher信息矩阵和基于互信息的节点选择不一致的问题,该文提出一种基于多目标优化的节点选择方法.推导出节点噪声为混合高斯分布时的贝叶斯Fisher信息矩阵和互信息,将节点个数、选择的节点对应的Fisher信息矩阵和互信息共同作为优化的目标函数.提出利用基于分解的多目标优化方法寻找Pa...     

简化的复数非圆信号广义线性盲均衡算法

为了提高复数非圆信号的盲均衡性能,本文深入分析广义线性滤波理论,利用常模准则的简便性和稳健性,针对低阶复数非圆信号构造了简化的广义线性盲均衡器,并提出了一种简化的广义线性递归最小二乘常模盲均衡算法。简化的广义线性盲均衡器直接利用接收信号的实部和虚部作为均衡器输入,从而得到接收信号完整的实部和虚部的二阶统计量信息。新算法将标准的广义线性均衡算法的复数运算变成实数运算,有效地降低了标准广义线性均衡器的复杂度。仿真实验结果表明,与传统常模盲均衡算法相比,新算法在不提高计算复杂度的基础上,能够有效降低剩余码间干扰和误码率。      

Threshold region performance of maximum likelihood direction of arrival estimators

This paper presents a performance analysis of the maximum likelihood (ML) estimator for finding the directions of arrival (DOAs) with a sensor array. The asymptotic properties of this estimator are well known. In this paper, the performance under conditions of low signal-to-noise ratio (SNR) and a small number of array snapshots is investigated. It is well known that the ML estimator exhibits a threshold effect, i.e., a rapid deterioration of estimation accuracy below a certain SNR or number of snapshots. This effect is caused by outliers and is not captured by standard techniques such as the Crame/spl acute/r-Rao bound and asymptotic analysis. In this paper, approximations to the mean square estimation error and probability of outlier are derived that can be used to predict the threshold region performance of the ML estimator with high accuracy. Both the deterministic ML and stochastic ML estimators are treated for the single-source and multisource estimation problems. These approximations alleviate the need for time-consuming computer simulations when evaluating the threshold region performance. For the special case of a single stochastic source signal and a single snapshot, it is shown that the ML estimator is not statistically efficient as SNR/spl rarr//spl infin/ due to the effect of outliers.     

Timing estimation for a filtered Poisson process in Gaussian noise

The problem of estimation of time shift of an inhomogeneous casually filtered Poisson process in the presence of additive Gaussian noise is discussed. Approximate expressions for the likelihood function, the MAP estimator, and the MMSE estimator that becomes increasingly accurate as the per-unit-time density of superimposed filter responses becomes small are obtained. The optimal MAP estimator takes the form of a cascade of linear and memoryless nonlinear components. For smooth point process intensities, the performance of the MAP estimator is studied via local bias and local variance. A rate distortion type lower bound on the MSE of any estimator of time delay is then derived by identification of a communications channel that accounts for the mapping from time delay to observation process. Results of numerical studies of estimator performance are presented. Based on the examples considered it is concluded: (1) the small-error MSE of the nonlinear MAP estimator can be significantly better than the small-error MSE of the optimal linear estimator: (2) the rate distortion lower bound can be significantly tighter than the Poisson limited bounds determined in previous studies     

Parameter Estimation for Locally Linear FM Signals Using a Time-Frequency Hough Transform

An estimator for the phase parameters of mono- and multicomponent FM signals, with both good numerical properties and statistical performance is proposed. The proposed approach is based on the Hough transform of the pseudo-Wigner-Ville time-frequency distribution (PWVD). It is shown that the numerical properties of the estimator can be improved by varying the PWVD window length. The effect of the window time extent on the statistical performance of the estimator is delineated. Experimental data is used for validation of the statistical properties.      

Computationally efficient angle estimation for signals with knownwaveforms

This paper presents a large sample decoupled maximum likelihood (DEML) angle estimator for uncorrelated narrowband plane waves with known waveforms and unknown amplitudes arriving at a sensor array in the presence of unknown and arbitrary spatially colored noise. The DEML estimator decouples the multidimensional problem of the exact ML estimator to a set of 1-D problems and, hence, is computationally efficient. We shall derive the asymptotic statistical performance of the DEML estimator and compare the performance with its Cramer-Rao bound (CRB), i.e., the best possible performance for the class of asymptotically unbiased estimators. We will show that the DEML estimator is asymptotically statistically efficient for uncorrelated signals with known waveforms. We will also show that for moderately correlated signals with known waveforms, the DEML estimator is no longer a large sample maximum likelihood (ML) estimator, but the DEML estimator may still be used for angle estimation, and the performance degradation relative to the CRB is small. We shall show that the DEML estimator can also be used to estimate the arrival angles of desired signals with known waveforms in the presence of interfering or jamming signals by modeling the interfering or jamming signals as random processes with an unknown spatial covariance matrix. Finally, several numerical examples showing the performance of the DEML estimator are presented in this paper     

Pilot Based Channel Estimation in IEEE 802 16a OFDM System

1　Introdution　IEEE 802 16[1] is a specification for fixed broadbandwireless Metropolitan Access Networks (MAN). The stan dard is expected to bring low cost and more bandwidth prod ucts for broadband wireless access in the next years. Thisstandard specifies the physical (PHY) and Medium AccessControl layer (MAC) of the air interface of interoperablepoint to multipoint and optional Mesh topology broadbandwireless access system. The specification enables access todata, …     

Statistical Assessment of Eigenvector-Based Target Decomposition Theorems in Radar Polarimetry

The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecompostion of the covariance or coherency matrices and the associated$H/underlinealpha/A$decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry.     

Acceleration estimator for low-velocity and low-accelerationregions based on encoder position data

Acceleration computation based on simple numerical differentiation from an optical encoder signal may be very erroneous, especially in the low-velocity and low-acceleration regions. To overcome this problem, a novel approach to estimating acceleration in these regions is proposed in this paper. This low-acceleration estimator, which is a computer algorithm, is based on the fact that the displacement signal from the encoder is accurate. Since the bandwidth of this estimator is rather limited, it can be used in combination with the traditional numerical differentiation approach in order to cover a wide velocity range. It was shown in various simulations and experiments that this combined acceleration estimator can yield accurate acceleration estimates over a wide range of velocities. Furthermore, when this estimator is applied to a friction compensation system, the effect of low-velocity friction can be reduced significantly by its capability to detect small changes in acceleration caused by friction     

An efficient code-timing estimator for DS-CDMA signals

We present an efficient algorithm for estimating the code timing of a known training sequence in an asynchronous direct-sequence code division multiple access (DS-CDMA) system. The algorithm is a large sample maximum likelihood (LSML) estimator that is derived by modeling the known training sequence as the desired signal and all other signals including the interfering signals and thermal noise as unknown colored Gaussian noise that is uncorrelated with the desired signal. The LSML estimator is shown to be robust against the near-far problem and is also compared with several other code timing estimators via numerical examples. It is found that the LSML approach can offer noticeable performance improvement, especially when the loading of the system is heavy     

Asymptotically near-optimal blind estimation of multipath CDMA channels

In this paper, correlation matching techniques are applied to estimate multipath code division multiple access (CDMA) channels. We arrange unknown multipath parameters for each of J active users in a vector. Then, the output correlation matrix is parameterized by J unknown rank one matrices, with each one formulated from the corresponding channel vector. This correlation matrix is further compared with its sample average. The resulting error can be first minimized to obtain unbiased estimates of J unknown rank one matrices in closed forms. Thus, our estimator for each channel vector is derived by singular value decomposition (SVD) on the associated rank one matrix within a scalar ambiguity. It turns out that the performance of our estimator can be improved by introducing an asymptotically optimal weighting matrix in our cost function. This weighting matrix can be estimated directly from data samples only with a small penalty on the asymptotic performance. The asymptotic covariance of our estimator is also derived and can be compared with the Cramer-Rao lower bound, both in closed forms. Simulation results show the applicability of the proposed methods and consistency with our theoretical analysis     

A Blind Frequency Offset Estimator for Coherent M-PSK System in Autonomous Radio

This paper presents a novel blind frequency offset estimator for coherent M-PSK systems in an autonomous radio. The proposed estimator is based on the spectrum of the signal’s argument. A data removal block is developed. We derive the distribution of the instantaneous phase, which is applied to indicate that the proposed estimator can be considered as a class of nonlinear least-squares estimator. We provide a method to analyze the asymptotic performance of the proposed estimator. This enable us to predict the mean-square error on frequency offset estimation for all signal-to-noise ratio (SNR) values. Computer simulations indicate that the proposed estimator achieves better performance than the original estimator. The performance of the proposed estimator as a blind estimator is also illustrated.