基于FFT系数的正弦信号频率估计算法

针对基于FFT系数实部的频率插值算法在峰值谱线相位接近于±π/2时频率估计误差较大的问题,提出了一种改进的正弦信号频率估计算法。该算法首先利用FFT系数的实部和虚部序列索引出峰值谱线位置,然后根据峰值谱线的相位,选取实部与虚部序列中幅度较大的序列进行频率插值。仿真结果表明：在信噪比为3 dB、采样点为128的情况下,整个频段上归一化频率估计误差均方根小于0.02,接近Cramer Rao下限,整体性能优于基于FFT系数实部的频率插值算法和Rife算法。改进的算法频率估计精度高,计算量小,易于硬件实现。     

基于FFT的高精度FMCW雷达频率估计算法

在FMCW(调频连续波)雷达测距测速系统中,通常采用FFT(快速傅里叶变换)算法对差频信号进行频率测量。针对Rife算法在信号频率位于FFT量化频率附近时频率估计精度较低的问题,提出了一种改进的频率估计算法。该算法首先对信号作FFT并索引峰值谱线位置,然后对主瓣内幅度最大的两条谱线的系数进行线性组合,来等效实现加Hanning窗FFT,最后进行频率估计。仿真结果表明:该方法具有更高的精度,可应用于FMCW(调频连续波)雷达差频信号频率的测量,能有效地减小频率测量误差,提高距离分辨率。     

基于最大似然频率精细估计的载波捕获算法

深空通信中,星上对载波的主动捕获主要利用频率估计的方法。在实际载波捕获电路中,精确的频率估计值导入锁相环,使得锁相环捕获带余量充足。在锁相环带宽一定的情况下,估计精度的提高可以减少FFT实现点数。在FFT频率粗估计的基础上,通过频率精细估计算法可提高估计精度。为获得估计精度更高的频率精细估计算法,利用最大似然泛函不变性推导了频率精细估计的最大似然算法。载噪比在5 dB时,估计精度可以提高到FFT分辨率的10-4。仿真结果表明,该算法估计性能优于其他频率精细估计算法。     

毫米波/红外成像系统中的精确极大似然误差估计算法

为精确估计毫米波雷达/红外成像复合系统中传感器的系统误差,提出了一种基于无偏转换测量的精确极大似然(UCM-EML)误差估计算法.根据极坐标系下的测量噪声建立误差估计模型,据此推导似然函数和准则函数,采用高斯-牛顿迭代法进行准则函数的优化.仿真实验结果表明,UCM-EML算法在误差估计精度和收敛速度上都优于精确极大似然估计算法和修正的精确极大似然算法.     

基于多通道的跳频信号欠采样频率估计

为实现跳频信号频率跟踪估计,本文提出一种基于多通道的跳频信号欠采样频率估计方法。基于快速傅里叶变换（FFT）,提出了一种3谱线方程的频率校正算法,提高了基于中国余数定理的频率估计方法对短序列信号的频率估计精度,与现有的两种基于离散傅里叶变换（DFT）的频率校正算法相比,序列补零数量灵活。给出了一种频率估计检错机制,可以提高算法可靠性。仿真结果表明,本文所提频率估计算法的精度优于现有算法,增加序列补零数量可进一步提高算法的估计精度和信噪比阈值,降低误差平台;检错机制在-23 dB至8 dB信噪比范围内的准确率高于95.5%。      

基于半定松弛的长码DSSS信号扩频波形估计

针对非合作通信中的长码DSSS信号,提出了一种基于半定松弛的扩频波形估计方法,并在确定信号模型下推导了扩频波形估计的CRB.首先推导了长码DSSS信号扩频波形的极大似然估计,由于该极大似然估计为非凸的组合优化问题,提出通过松弛约束条件将其转化为具有多项式计算复杂度的半定规划问题,实现对该极大似然估计问题的近似求解.仿真表明本文提出方法性能优于现有方法,并随着信噪比的提高逐渐逼近CRB.     

基于LFM模型的高动态弱GPS信号载波捕获

根据极大似然估计理论,通过载波频率和频率变化率分段对消、快速傅里叶变换(FFT)结果选大估计载波频率和频率变化率,实现载波信号捕获,建立了高动态全球定位系统(GPS)载波的线性调频(LFM)信号模型,并给出了算法的具体实现。详细分析了该算法中检测统计量的分布特性,推导了恒虚警准则下的检测门限,并讨论了其中关键参数的设计。仿真结果表明,该算法可以实现对18 dBHz的GPS高动态信号的载波捕获。     

引入蝙蝠算法的最大似然DOA估计

DOA估计理论的传统算法中,最大似然DOA估计方法能准确地估计出目标方向角度,性能优良,并且具有很好的稳定性。与MUSIC及其他的子空间分解类算法相比,在信噪比较低、小快拍信号时,最大似然DOA估计算法优势更为突出。但是由于其自身算法复杂度较高的缺陷而碍于工程上的应用。针对这一问题,将蝙蝠算法与最大似然算法相结合,应用于信号的DOA估计,利用蝙蝠搜索算法搜索路径优、寻优能力强的优点,快速搜索到似然函数的全局最优值,优化多维非线性的估计谱函数。仿真结果表明,蝙蝠搜索算法有效地克服最大似然DOA估计中存在的运算量大,计算复杂度高等问题,通过与其他经典的仿生智能优化算法相比较,该方法体现出更好的收敛性。     

基于非圆信号波达方向估计的最大似然算法

现有的非圆信号波达方向(DOA)估计算法基本都是子空间分解类的,如NC-MUSIC、NC-ESPRIT算法等。基于子空间拟合类的非圆信号DOA估计算法研究的不多。文中依据非圆信号的DOA估计数学模型,提出了基于非圆信号的DOA估计最大似然算法。通过计算机仿真,将基于非圆信号的最大似然算法与常规最大似然算法及基于非圆信号的MUSIC算法进行了性能比较。结果表明,该算法提高了方位估计的估计性能,对于估计精度要求很高、用户比较密集的场合,能发挥很大的作用。     

双通道接收的相位差估算方法

分析了双通道接收相位差的时域、频域计算方法，基于DFT的频域估算方法，充分利用了DFT、对信噪比的改善作用，克服了时域方法要求较高信噪比的缺点，能有效地抑制噪声，提高测量精度。但是离散的DFT输出谱限制了信噪比的改善，文中利用截断DFT的输出谱特性来估计信号频率，再用一次最大似然估计得到信号相位估计，减小运算量。分析和仿真表明文中方法可以得到接近最大似然法的测量精度。     

切换ARMAX系统切换时刻检测算法

辨识切换线性系统的主要难点在于参数估计问题与子系统划分问题耦合.针对该问题,利用卡尔曼滤波与递推扩展最小二乘的联系,证明当所辨识的带外源输入的自回归滑动平均(AR-MAX)切换系统在满足严正实条件下,当且仅当输入输出数据来源于同一个子ARMA系统时所构造的新息序列具有白色性.基于此,提出了一种切换ARMAX系统切换时刻检测算法.仿真计算结果验证了所提算法的有效性.     

大动态PCM/FM信号的载波频率同步

针对大动态脉冲编码-频率调制(PCM/FM)遥测信号的载波频率同步,提出了基于快速傅里叶变换(FFT)及频谱重心的载波频率估计方法,并采用了频谱叠加及频谱截取的优化方法提高算法估计精度.相对于其他基于FFT的频率估计算法,频谱重心法有着更高的估计精度及更好的抗噪声性能,而且复杂度代价很小.仿真的均方误差结果表明,基于FFT长度为2048和2块叠加以及保留信号99.9%能量的频谱截取方案有最好的估计性能.在最大多普勒频率、多普勒一阶变化率及二阶变化率分别为0.5倍、0.3倍及0.2倍符号率的大动态条件下,基于频谱重心法的二阶锁频环能够较好地完成载波频率跟踪.误码率曲线表明,经过频偏校正后的多符号非相干解调(MSD)性能与无频偏情况相比,无性能损失.     

BLIND AND COMPLETE MODELING OF LINEAR SYSTEMS USING THIRD ORDER CUMULANTS

This paper presents a novel approach to structure determination of linear systems along with the choice of system orders and parameters.AutoRegressive (AR),Moving Average (MA) or AutoRegressive-Moving Average (ARMA) model structure can be extracted blindly from the Third Order Cumulants (TOC) of the system output measurements,where the unknown system is driven by an unobservable stationary independent identically distributed (i.i.d.) non-Gaussian signal.By means of the system order recursion,whether the system has an AR structure or has AR part of an ARMA structure is firstly investigated.MA features in the TOC domain is then applied as a threshold to decide if the system is an MA model or has MA part of an ARMA model.Numerical simulations illustrate the generality of the proposed blind structure identification methodology that may serve as a guideline for blind linear system modeling.     

一种基于三阶累积量的准则及自适应滤波算法

该文提出了一个基于三阶累积量的优度准则。基于此准则,利用最速下降法,得到一种新的基于三阶累积的梯度型自适应滤波算法,该算法用于平稳和非平稳的MA(Moving Average)模型系统辨识的计算机模拟仿真结果表明:该算法有良好的收敛性能及对时变系统的跟踪能力。     

Information fusion steady-state white noise deconvolution estimators with time-delayed measurements and colored measurement noises

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.     

一种基于高阶循环累积量的改进型VAD算法

根据语音信号的短段循环平稳(CycloStationary, CS)特征,该文提出了一种应用于复杂背景噪声条件下的基于高阶循环累积量的改进型VAD(Voice Activity Detection)算法,算法采用MA(Moving Average)模型对语音信号建模,并选择平均幅度差(Average Magnitude Difference Function,AMDF)的方法来估算循环频率以降低算法复杂度。经VoIP(Voice over Internet Protocol)平台测试,算法对高斯(白色或有色)噪声以及其它平稳噪声自适应能力强、检测性能突出, 且恢复后语音质量损失较小,对于非对称噪声也具备可检测能力。     

A State-Space Approach to Multiuser Parameters Estimation Using Central Difference for CDMA Systems

In this paper, it is shown that a state-space model applies to the code-division multiple-access (CDMA) channel, and Central Difference Filter (CDF) produces channel estimates with the minimum mean-square error (MMSE). This result may be used as compare to Extended Kalman Filter (EKF) which used as channel estimator in CDMA system. The main purpose of this paper is to compare robustness of channel estimator for realistic rapidly time-varying Rayleigh fading channels. To overcome the highly nonlinear nature of time delay estimation and also improve the accuracy, consistency and efficiency of channel estimation, an iterative nonlinear filtering algorithm, called the CDF has been applied in the field of CDMA System. The proposed channel estimator has a more near-far resistant property than the conventional Extended Kalman Filter (EKF). Thus, it is believed that the proposed estimator can replace well-known filters, such as the EKF. The Cramer-Rao lower bound (CRLB) is derived for the estimator, and simulation result show that it is nearly near-far resistant and clearly outperforms the EKF. Jang Sub Kim was born June 15, 1974, in Yeongdeok, Korea. He received the M.S. degree in school of electrical and computer engineering from Sungkyunkwan University, Seoul, Korea. He is currently with the School of Information and Communication Engineering, Sungkyunkwan University, where he was a Ph. D. student since 1999. His research interests include code-division multiple access, channel estimation, position location, and wireless communications. Seokho Yoon (S‘99–M‘1) received the B.S.E. (summa cum laude), M.S.E., and Ph.D. degrees in electrical engineering from KAIST, Daejeon, Korea, in 1997, 1999, and 2002, respectively. From April 2002 to June 2002, he was with the Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge, MA, and from July 2002 to February 2003, he was with the Department of Electrical Engineering, Harvard University, Cambridge, MA, as a Postdoctoral Research Fellow. In March 2003, he joined the School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea, where he is currently an Assistant Professor. His research interests include spread spectrum systems, mobile communications, detection and estimation theory, and statistical signal processing. Dr. Yoon is a member of the IEEK and KICS. He was the recipient of a Bronze Prize at Samsung Humantech Paper Contest in 2000. Dong-Ryeol Shin (M‘97) was born in Seoul, Korea, in 1957. He received the B.S., M.S. and Ph.D degree in electrical engineering from the Sungkyunkwan University in 1980, and the Korea Advanced Institute of Science and Technology (KAIST) in 1982 and the Georgia Institute of Technology in 1992, respectively. During 1992-1994, he had worked for Samsung Data Systems, Ltd., Korea. Since 1994, he has been with network research group at the Sungkyunkwan University, Korea, as a professor. His current research interests include wireless communications and ubiquitous computing.     

ARMA-cepstrum Recursion Algorithm for the Estimation of the MA Parameters of 2-D ARMA Models

This paper considers the problem of estimating the moving average (MA) parameters of a two-dimensional autoregressive moving average (2-D ARMA) model. To solve this problem, a new algorithm that is based on a recursion relating the ARMA parameters and cepstral coefficients of a 2-D ARMA process is proposed. On the basis of this recursion, a recursive equation is derived to estimate the MA parameters from the cepstral coefficients and the autoregressive (AR) parameters of a 2-D ARMA process. The cepstral coefficients are computed benefiting from the 2-D FFT technique. Estimation of the AR parameters is performed by the 2-D modified Yule–Walker (MYW) equation approach. The development presented here includes the formulation for real-valued homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. The proposed algorithm is computationally efficient especially for the higher-order 2-D ARMA models, and has the advantage that it does not require any matrix inversion for the calculation of the MA parameters. The performance of the new algorithm is illustrated by some numerical examples, and is compared with another existing 2-D MA parameter estimation procedure, according to three performance criteria. As a result of these comparisons, it is observed that the MA parameters and the 2-D ARMA power spectra estimated by using the proposed algorithm are converged to the original ones     

OFDM系统中多导频的FFT信道估计算法

论文提出了OFDM系统中基于FFT的信道估计方法,包括基于时域插值及变换域插值方法。时域插值算法的理论基础是利用FFT频域采样定理,可由频域有限频点的采样值经过IFFT／FFT得到整个频域传输函数的估计值,而不发生混叠。变换域插值算法的理论基础是利用FFT时域抽样定理,利用OFDM信号特点和信道特性,经过FFT／IFFT将信号和噪声分离,并在此基础上进行加窗改进算法,以减小插值中的频谱泄漏,提高估计效果。仿真结果说明,加窗的基于FFT变换域的方法性能有了很大改善。     

Optimization-based online estimation of vehicle mass and road grade: Theoretical analysis and experimental validation

The gross vehicle mass (GVM) and the road grade are two factors that both have a substantial influence on the performance of a vehicle’s powertrain. In this paper, we propose a novel model-based estimation method for the GVM and the road grade that exploits entire sequences of powertrain measurements at once and is formulated as a nonlinear program (NLP). The estimator is based on a simple model for the vehicle’s longitudinal dynamics with only few intuitive vehicle parameters. By assuming the GVM to remain constant during certain sections of the trip and by describing the road grade profile in the distance domain, we achieve a separation of scales, which enhances disturbance rejection and significantly lowers the number of optimization variables. The resulting estimator is thoroughly analyzed both analytically and numerically. We show that a closed-form solution exists for the grade profile as a function of the GVM. Furthermore, if the GVM can be assumed to be constant during the journey considered, the estimation problem can be translated to a scalar NLP for finding the GVM. Although a rigorous proof is missing, our experiments show that in practice, the objective function is quasi-convex on a reasonable interval of GVM values and that thus a unique solution exists. Furthermore, robustness and sensitivity studies are conducted, where various perturbations are considered in a controlled environment, including uncorrelated and correlated noise, sensor offset, and model mismatches. Compared to two well-known recursive filters described in literature, our estimator shows significantly higher robustness with respect to all perturbations. Finally, we validate the estimator and the two recursive filters on real data from an electric city bus. The proposed estimator outperforms the recursive algorithms and achieves an average relative GVM estimation error of 3.4%. On a standard personal computer, the NLP for a driving phase of around one hour is solved in roughly 7.5 s, while the scalar NLP representing a driving phase of around 75 s is solved in roughly 12 ms. Both results indicate the real-time applicability of our algorithm.