基于泰勒加权最小二乘算法的水下TDOA/FDOA联合定位方法

针对水下复杂的定位场景中,两阶段加权最小二乘算法因为忽视噪声平方项而造成的定位不精确问题,本文提出了一种基于泰勒加权最小二乘算法的水下到达时间差和到达频率差(Time difference of arrival and frequency difference of arrival, TDOA/FDOA)联合定位方法。该方法首先通过加权最小二乘算法求解目标粗估计位置和速度;然后通过求解TDOA/FDOA测量值的泰勒展开式构造定位误差方程,用迭代的方法不断更新目标估计位置和速度;最后,当定位误差足够小或达到最大迭代次数的时候,算法停止运行并输出目标估计位置和速度。仿真表明,在噪声方差小于10分贝时,本文算法的位置和速度估计的均方根误差能够接近或约等于克拉美罗下界。      

两次一维维纳滤波信道估计的 一种噪声方差优化方法

 本文提出了一种基于OFDM(Orthogonal Frequency Division Multiplexing)系统的两次一维(2×1-D)维纳滤波信道估计的噪声方差优化方法.对于2×1-D维纳滤波信道估计,维纳滤波将先后应用于频域维和时域维,而两次滤波时的噪声方差实际是不相同的,但现有的2×1-D维纳滤波信道估计方法没有考虑噪声的变化.本文首先分析出了第一次滤波后残余的噪声方差,并将其优化的结果应用于第二次滤波中,然后根据不同的优化准则对信道估计性能进行了评估.仿真结果表明,同未对噪声方差优化的信道估计方法相比,本方法具有更优的性能,且非常接近两维维纳(2-D)滤波方法.     

脉冲噪声下基于稀疏表示的韧性DOA估计方法

受相关熵启发,本文提出了一种脉冲噪声下基于稀疏表示的韧性DOA估计新方法.为实现多测量向量下联合稀疏信号的重建,本文提出了一种基于归一化迭代硬阈值的优化算法,讨论了最优步长的选择问题,证明了优化算法的收敛性.仿真结果表明：本文算法能够实现脉冲噪声下多信源的DOA估计,具有比已有算法更高的可分辨概率和估计精度.     

基于噪声方差确定非线性扩散除噪声的最优停止时间

该文采用非线性扩散进行图像除噪声并在这个计算框架下提出利用噪声方差选择最优停止时间的方法。在利用非线性扩散进行图像除噪声时,每次迭代平滑掉的图像的方差大于平滑掉的噪声的方差时,迭代应该停止。为了在除噪声过程中正确地估计噪声的方差,该文构造一幅纯噪声图像跟实际的观测图像同步进行迭代计算,并把纯噪声图像的方差作为图像中噪声方差的估计值来辅助计算最优停止时间。针对非线性扩散的各项异性,提出了能够保持两种噪声同步变化的特殊的规整化项。新的规整化项在迭代纯粹噪声图像时使用,这样确保每次迭代都可以保持合成噪声与实际图像噪声的统计特性相一致。实验证明新的算法可以非常有效地选择合适的停止时间。     

基于样条迭代随机建模的高精度星间相对定位方法

利用GPS确定星间相对位置,在无法得到精确的卫星相对运动动力学模型时,事后处理一般采用最小二乘方法,但实际应用时该方法采用的随机模型没有考虑实测GPS数据的异方差、时间和空间相关特性.为提高相对定位精度,本文提出了一种利用样条函数模型进行迭代随机建模的相对位置确定方法,即先根据相对位置参数的连续特性,建立基于样条表示的函数模型;然后根据历元差分原理消除原始双差GPS观测量的时间相关性;再利用MINQUE方法求解去相关GPS观测量的方差-协方差分量,最后利用LAMBDA方法固定整周模糊度并确定相对位置.实验仿真得到两个结论:(1)函数模型的样条表示不仅有利于消除动态条件下测量量的时间相关性,而且能起到节省参数、平滑噪声的作用,因此可大大提高相对定位的精度;(2)在样条函数模型的基础上,采用MINQUE方法迭代随机建模,能进一步提高相对定位精度.     

点目标图像信噪比计算方法

目前图像信噪比定义方法多种多样,对常见信噪比定义进行了分类,总结了不同定义之间的内在联系、常用解法以及使用范围。针对常见图像信噪比求解方法误差较大的问题,提出了一种基于最小二乘拟合的高斯背景噪声参数估计方法。首先根据全部灰度级估计均值和方差,然后通过迭代的方式,排除高灰度非背景像素的干扰,逐步缩小统计范围,最后选择具有最小均方误差的统计参数作为背景灰度和噪声方差的估值。仿真实验证明,与常见的"局部最小方差法"和"小于均值方差法"相比,用该方法得到的背景噪声参数计算的信噪比具有更高的精度和稳定性。     

白噪声中复指数序列参数估计的一种新方法

本文提出估计白噪声中一个或多个复指数序列的参数(复指数序列的频率和功率、白噪声的方差)的一种新方法,该方法根据数据的2p个自相关函数值,相继求解两个线性方程组,即可得到对白噪声中P个复指数序列的参数估计.这种方法的算法简单,并具有很高的频率估计精度.     

未知噪声信息多径衰落信道的软判决度量生成

信道解码中的软判决技术需要利用信道噪声方差信息来产生软判决度量,而通常情况下,这一信息是未知和时变的。本文首先简要分析了信道噪声方差对软判决度量的影响,进而提出一种新的在多径衰落信道中估计时变噪声方差的方法,并利用该方法的估计结果为信道解码器生成软判决度量。将该算法应用于中国地面数字电视广播传输系统外接收机的仿真结果表明：与已有的信道噪声方差估计算法相比,本文算法具有收敛速度快、估计结果准确的优点,生成的软判决度量逼近于多径衰落信道的最优软判决度量。     

基于自适应EKF的相对导航算法研究

针对测距测角相对导航中测量噪声不可精确获知往往导致相对定位精度下降的问题,本文研究了基于自适应扩展卡尔曼滤波(EKF)的相对导航算法。利用泰勒级数展开对测量矩阵进行线性化处理,并利用自适应时变噪声估计方法对测量噪声方差阵进行动态估计,状态噪声方差阵通过惯导特性的先验值获得。仿真结果表明,基于自适应EKF的相对导航算法可获得高精度且连续平滑的相对定位信息,尤其在测量噪声发生变化时更是表现出良好的导航参数估计性能。     

同道干扰下放大转发协同OFDM系统中的信道估计

该文针对放大转发(Amplify-and-forward,AF)协同OFDM系统,在导频和数据子载波都受到多个严重的同道干扰(Co-channel Interference,CCI)影响的前提下,提出了一种基于导频辅助的迭代信道估计方法,并推导了信道估计的Cramér-Rao 界(Cramer-Rao Bound,CRB).将等效信道、干扰信道和直传信道的信道状态信息(Channel State Information,CSI)估计值以及等效噪声方差矩阵的估计值进行线性合并,进一步消除CCI产生的影响,从而有效地提高了接收机的检测性能.理论分析和仿真结果表明:对于AF协同OFDM系统,在有多个同道干扰存在的情况下,本文提出的方法能够有效估计出所有等效信道和干扰信道的信道状态信息以及等效噪声方差矩阵,可以获得较好的均方误差(Mean Square Error,MSE)性能,且运算复杂度远远低于传统的线性最小均方误差(Linear Minimum Mean Square Error,LMMSE)信道估计方法.     

信道噪声对无线远程迭代学习控制系统的影响

无线远程控制系统的主要特征是信号通过无线网络在传感器与控制器,控制器与执行器之间传输,但信号无线传输过程中受到的信道噪声干扰会严重影响系统的控制性能。针对一类控制器采用迭代学习控制方式的无线远程控制系统,首先得到系统输出误差和信道噪声在迭代域内的关系表达式,然后在此基础上就信道噪声对输出误差信号 范数平方期望收敛性能的影响进行分析。由分析可知,该期望是关于信道噪声方差和系统参数的函数,并将随迭代次数的增加收敛于一个有限的误差值。在系统参数一定的情况下,该误差值将随信道噪声方差的增大而增大,即信道噪声方差越大,其对系统跟踪性能的影响越明显。最后,通过仿真实验证明了分析结果的正确性。      

On-line parameter identification of SPM motors based on MRAS technique

This article proposes a new parameter estimation method for surface-mounted permanent magnet (SPM) motors by employing the model reference adaptive system (MRAS) technique. The proposed algorithm is applied to an SPM motor used in washing machines. To determine washing and dewatering patterns, a washer tries to detect the size of load in the beginning of the operation. Hence it requires an accurate estimation of system parameters. The proposed estimation method identifies stator inductance and flux-linkage by using d- or q-axis current error, and its value is used for estimation independently with respect to each parameter. Therefore, the proposed scheme can get rid of the instability in inductance and flux estimation caused by the cross-coupling between d- and q- quantities. In this article, the stator resistance is detected by using the recursive least square (RLS) method to reduce measurement noises and to eliminate dead-time effect. Simulation results show that the proposed scheme tracks the parameters well even when there is transient states in rotor speed and the load conditions, and experimental results verify the validity and efficacy of the proposed algorithm.     

Active contours driven by local Gaussian distribution fitting energy

This paper presents a new region-based active contour model in a variational level set formulation for image segmentation. In our model, the local image intensities are described by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with a level set function and local means and variances as variables. The energy minimization is achieved by an interleaved level set evolution and estimation of local intensity means and variances in an iterative process. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities and noise of spatially varying strength (e.g. multiplicative noise). In addition, our model is able to distinguish regions with similar intensity means but different variances. This is demonstrated by applying our method on noisy and texture images in which the texture patterns of different regions can be distinguished from the local intensity variance. Comparative experiments show the advantages of the proposed method.     

基于抗差估计的激光测高数据处理

激光测高仪是一种空间对地探测装置,在众多领域中有重要的应用前景.由于大气传输介质以及系统噪声的影响,需要对测得的数据做精确化处理.采用了抗差估计(IGGⅢ方案)来处理激光测高的数据,与最小二乘法相比,抗差估计既能减小模型偏差又能抗拒异常观测值干扰.     

多传感器分布式融合Kalman预报器

应用现代时间序列分析方法,基于ARMA新息模型,在线性最小方差最优信息融合准则下,对于输入噪声与观测噪声相关且观测噪声相关的多传感器系统,分别提出了按矩阵加权、按标量加权和按对角阵加权的3种分布式融合稳态Kalman 预报器。其中提出了基于Lyapunov方程的局部预报估值误差方差阵和协方差阵计算公式。它们被用于计算最优加权,与单传感器情形相比,可提高估值器的精度。一个跟踪系统的仿真例子说明了其有效性,且说明了3种加权融合预报器的精度无显著差别。但标量加权融合预报器可显著减小计算负担,提供一种快速实时信息融合估计算法。     

Estimating the parameters of a nonhomogeneous Poisson process with linear rate

Motivated by telecommunication applications, we investigate ways to estimate the parameters of a nonhomogeneous Poisson process with linear rate over a finite interval, based on the number of counts in measurement subintervals. Such a linear arrival-rate function can serve as a component of a piecewise-linear approximation to a general arrival-rate function. We consider ordinary least squares (OLS), iterative weighted least squares (IWLS) and maximum likelihood (ML), all constrained to yield a nonnegative rate function. We prove that ML coincides with IWLS. As a reference point, we also consider the theoretically optimal weighted least squares (TWLS), which is least squares with weights inversely proportional to the variances (which would not be known with data). Overall, ML performs almost as well as TWLS. We describe computer simulations conducted to evaluate these estimation procedures. None of the procedures differ greatly when the rate function is not near 0 at either end, but when the rate function is near 0 at one end, TWLS and ML are significantly more effective than OLS. The number of measurement subintervals (with fixed total interval) makes surprisingly little difference when the rate function is not near 0 at either end. The variances are higher with only two or three subintervals, but there usually is little benefit from going above ten. In contrast, more measurement intervals help TWLS and ML when the rate function is near 0 at one end. We derive explicit formulas for the OLS variances and the asymptotic TWLS variances (as the number of measurement intervals increases), assuming the nonnegativity constraints are not violated. These formulas reveal the statistical precision of the estimators and the influence of the parameters and the method. Knowing how the variance depends on the interval length can help determine how to approximate general arrival-rate functions by piecewise-linear ones. We also develop statistical tests to determine whether the linear Poisson model is appropriate.This work was done at AT&T Bell Laboratories as part of an AT&T Summer Research Program fellowship.     

Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method.     

A position-and-velocity sensorless control for brushless DC motorsusing an adaptive sliding mode observer

The sliding mode observer is robust to measurement noises. Since the switching signals of the sliding mode observer contain the induced voltages of the motors, it is possible to obtain the position and velocity of the motors directly from the switching signals. Although the estimated position can be used for locating the position of the rotor, the estimated velocity is heavily contaminated by noises from the switching signals. This direct method nullifies the merit of the sliding mode observer. Thus, the authors also present an adaptive scheme for robust estimation of the velocity of brushless DC motors. Stability of the adaptive scheme is assured, and estimation errors due to parameter deviations are analyzed. A method of parameter adjustment is described     

Self-tuning measurement fusion Kalman filter with correlated measurement noises

For the multisensor system with correlated measurement noises and unknown noise sta-tistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.     

无CP的OFDM联合信道估计和干扰抵消

无循环前缀OFDM无线通信系统可以提高频谱利用效率。提出一种基于无循环前缀(CP)OFDM系统的联合信道估计和干扰抵消算法。采用最小二乘法(Least Square)估计信道时域矢量,通过离散傅里叶变换(DFT)得到相应的OFDM信道的频域特性,由迭代算法对信号进行联合检测和干扰抵消。仿真结果表明,信道估计结果能够达到较高的精度,误码率也接近有循环前缀的OFDM无线通信系统。