基于数值拟合的PCMA系统自干扰信号幅度估计算法

针对卫星成对载波复用(PCMA)系统中的自干扰信号,提出基于数值拟合方法的幅度估计算法,并对其性能进行了分析。该算法适用于非对称和对称PCMA系统,可以有效抵消自干扰信号与有用信号互相关项不为零引起的误差,并且不会引入相位噪声,更符合工程应用实际。仿真结果表明,在干信比为12 dB的非对称PCMA系统中,该算法仍能获得较好的幅度估计精度,信噪比损失始终在0.1 dB左右。     

PCMA系统自干扰频率估计新算法

成对载波多址（PCMA）是一种新兴的频率重用技术,通过对自干扰信号的重构和抑制能够有效提高系统容量,其关键技术在于自干扰信号的参数估计。针对PCMA系统特点提出了一种无需单独训练的自干扰信号频率估计新方法,利用本地信号与对方信号的弱相关性,基于最小均方误差拟合准则拟合出相关函数的估计曲线,从而获得频偏估计值。仿真结果表明该算法能够得到比较小的估计误差和较为理想的误码率性能。     

基于性能边界和量化数据的WSN目标跟踪传感器选择算法

对能量和带宽受限的无线传感器网络下的目标跟踪问题,基于量化的观测数据和条件后验克拉美-罗下界提出一种传感器选择方法.为了节约网络能量和带宽,对传感器接收到的观测数据进行量化压缩,推导了传感器量化数据下目标状态估计的条件后验克拉美-罗下界,将其作为传感器选择和优化的准则,并且利用粒子滤波器给出一种条件后验克拉美-罗下界的近似计算方法.与基于无条件后验克拉美-罗下界和互信息的传感器选择方法进行了对比仿真,结果表明了条件后验克拉美-罗下界作为传感器选择准则的有效性以及对跟踪性能的改进.     

MSK信号信噪比估计的研究

针对 MSK 调制信号，分别研究了二阶距四阶距和基于同步头相关的信噪比估计方法，对两种方法的估计均方误差性能进行了仿真，并与克拉美-罗下界作对比，获得了两种方法的估计性能特点，最后根据两种方法的特点，给出了各自的适用范围。     

基于周期平稳的盲信噪比估计方法

过采样和成型滤波引入了信号的周期平稳性。基于对信号的周期平稳统计量的分析，提出了一种高斯白噪声信道下的盲信噪比估计方法。对信号的调制方式没有要求，也不需要发送端发送已知数据。蒙特卡罗仿真结果证明，在较宽的信噪比范围内，该方法的性能优于二阶四阶矩方法（M2M4）、信号方差比（SVR）方法等其他经典的盲信噪比估计方法。分析了估计的克拉美-罗下界，作为估计的绝对性能的参照。     

相干激光探测中微动参数估计的克拉美-罗界

为选择最佳参数估计方法估计目标微多普勒特征,需要研究参数估计的克拉美-罗界,来评价各估计方法的性能。以相干激光探测为背景,考虑噪声方差未知的影响,严格推导了高斯白噪声环境下微动目标回波信号各参数估计的克拉美-罗界的闭合表达式,仿真分析了目标相对于雷达的位置信息、数据处理长度以及回波信噪比与参数估计方差下界的关系。结果表明,克拉美-罗界与噪声方差无关,目标相对于雷达的方位角、俯仰角越小,数据长度和信噪比越大,参数估计的方差下界越小。对目前常用的两种微动参数估计方法方差进行了计算,并与推导克拉美-罗界进行了对比。最后,与通过近似处理方法得到的克拉美-罗界进行了对比,指出了精确推导方差下界的意义。     

PCMA系统中位定时误差对干扰抵消性能的影响

首先介绍了PCMA技术的基本概念 ,并建立了PCMA系统基带传输模型 ;在此基础上研究了PCMA系统中位同步误差对干扰抵消性能的影响 ,并给出了相应的仿真结果。根据系统对干扰抑制比的要求 ,仿真结果可以为位定时估计算法的设计提供可靠的依据     

多雷达跟踪定位精度分析

多传感器组网的数据融合,可以提高目标的定位精度.文中提出了一种雷达网空中目标的空间位置融合的加权最小二乘算法,在目标位置估计Fisher信息矩阵基础上给出了目标位置估计误差的克拉美-罗下限.经蒙特卡罗仿真实验表明,该融合算法已逼近了克拉美-罗限,是一种理想的多雷达跟踪定位算法.     

非确知目标先验知识条件下MIMO雷达稳健波形设计

考虑稳健波形优化问题以提高多输入多输出雷达最差条件下参数估计精度。基于最小-最大方法,将初始参数误差模型显式包含进波形优化问题,并基于克拉美-罗界得到稳健波形设计。为求解得到的复杂非线性优化问题,基于哈达玛不等式将其转化为半定规划问题,从而可高效求解。仿真结果表明,与不相关发射波形以及非稳健方法相比,所提方法可显著改善最差条件下参数的估计性能。     

PCMA系统中干扰信号的载波相位估计

文章研究了PCMA系统中干扰信号的载波相位估计问题。首先导出了干扰抑制比与载波相位估计方差的关系；然后通过理论分析得到了干扰信号载波相位估计的最佳观测长度与归一化载波剩余频率偏差的关系表达式，并得到了最佳观测长度下载波相位估计的最小方差；最后研究了干扰抵消时载波相位的内插问题。     

Estimation of amplitude and phase parameters of multicomponentsignals

This paper considers the problem of estimating signals consisting of one or more components of the form a(t)e/sup jφ(t/), where the amplitude and phase functions are represented by a linear parametric model. The Cramer-Rao bound (CRB) on the accuracy of estimating the phase and amplitude parameters is derived. By analyzing the CRB for the single-component case, if is shown that the estimation of the amplitude and the phase are decoupled. Numerical evaluation of the CRB provides further insight into the dependence of estimation accuracy on signal-to-noise ratio (SNR) and the frequency separation of the signal components. A maximum likelihood algorithm for estimating the phase and amplitude parameters is also presented. Its performance is illustrated by Monte-Carlo simulations, and its statistical efficiency is verified     

Doppler frequency estimation and the Cramer-Rao bound

Addresses the problem of Doppler frequency estimation in the presence of speckle and receiver noise. An ultimate accuracy bound for Doppler frequency estimation is derived from the Cramer-Rao inequality. It is shown that estimates based on the correlation of the signal power spectra with an arbitrary weighting function are approximately Gaussian-distributed. Their variance is derived in terms of the weighting function. It is shown that a special case of a correlation-based estimator is a maximum-likelihood estimator that reaches the Cramer-Rao bound. These general results are applied to the problem of Doppler centroid estimation from SAR (synthetic aperture radar) data     

一种新的正弦信号频率和初相估计方法

提出了一种新的正弦信号频率和初相估计方法——频谱遍历法.该方法通过改变理想正弦信号频谱峰值实现对采样信号频谱峰值的遍历.分析了噪声对信号频谱幅度的影响,并以此给出了谱线遍历范围的选取准则.先估计频率,采用移频操作达到了良好的频域稳定性;再估计相位,避免了相位测量模糊的问题.在信噪比为6dB、采样点数为1024的情况下,频率估计均方根误差约为DFT频率分辨率的0.8％,初相估计均方根误差约为1.5°.Monte Carlo仿真表明,在达到一定信噪比或采样长度时,该方法的频率估计精度可突破CR下限,初相估计精度基本达到CR下限.     

Analytic and Asymptotic Analysis of Bayesian CramÉr–Rao Bound for Dynamical Phase Offset Estimation

In this paper, we present a closed-form expression of a Bayesian Cramer-Rao lower bound for the estimation of a dynamical phase offset in a non-data-aided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an offline context, and then a second expression in an online context logically follows. The SNR-asymptotic expressions of this bound drive us to introduce a new asymptotic bound, namely the asymptotic Bayesian Cramer-Rao Bound. This bound is close to the classical Bayesian bound but is easier to evaluate.     

Parameter estimation of 2-D random amplitude polynomial-phasesignals

Phase information has fundamental importance in many two-dimensional (2-D) signal processing problems. In this paper, we consider 2-D signals with random amplitude and a continuous deterministic phase. The signal is represented by a random amplitude polynomial phase model. A computationally efficient estimation algorithm for the signal parameters is presented. The algorithm is based on the properties of the mean phase differencing operator, which is introduced and analyzed. Assuming that the signal is observed in additive white Gaussian noise and that the amplitude field is Gaussian as well, we derive the Cramer-Rao lower bound (CRB) on the error variance in jointly estimating the model parameters. The performance of the algorithm in the presence of additive white Gaussian noise is illustrated by numerical examples and compared with the CRB     

一种单自旋回波串信号参数估计方法

针对存在加性高斯白噪声多参数变量的单自旋回波串信号参数估计问题,提出一种参数分离化的2-D参数估计方法.利用2-D数据矩阵秩为1的特性,依照迭代加权最小二乘方法,从左、右主奇异值向量中以参数分离的方式分别估计出衰减因子和频率,基于最小二乘方法进一步获得信号幅度估计.该方法在相对高信噪比和/或大数据样本下可达到克拉美罗下界,且计算复杂度较低.仿真数据结果证明了算法的有效性.     

On the cramer-rao bound for carrier frequency estimation in the presence of phase noise

We consider the carrier frequency offset estimation in a digital burst-mode satellite transmission affected by phase noise. The corresponding Cramer-Rao lower bound is analyzed for linear modulations under a Wiener phase noise model and in the hypothesis of knowledge of the transmitted data. Even if we resort to a Monte Carlo average, from a computational point of view the evaluation of the Cramer-Rao bound is very hard. We introduce a simple but very accurate approximation that allows to carry out this task in a very easy way. As it will be shown, the presence of the phase noise produces a remarkable performance degradation of. the frequency estimation accuracy. In addition, we provide asymptotic expressions of the Cramer-Rao bound, from which the effect of the phase noise and the dependence on the system parameters of the frequency offset estimation accuracy clearly result. Finally, as a by-product of our derivations and approximations, we derive a couple of estimators specifically tailored for the phase noise channel that will be compared with the classical Rife and Boorstyn algorithm, gaining in this way some important hints on the estimators to be used in this scenario     

On the Cramer- Rao bound for time delay and Doppler estimation (Corresp.)

Using a theorem due to Whittle, simple derivations of the Cramer-Rao lower bound are presented for some delay estimation problems related to a single source, multiple sources, and multipath. The problem of Doppler estimation is briefly discussed.     

A new bound and algorithm for Star 16-QAM carrier phase estimation

The true Cramer-Rao lower bound (CRLB) is derived and evaluated for the estimation of carrier phase of Star 16-quadrature amplitude modulation (QAM) and can be simply applied to carrier frequency estimation. Different geometries are investigated by varying the ring ratio (RR). For signal-to-noise ratios (SNRs) between 6-15 dB, the CRLB with RR=3 is lower than that of Square 16-QAM. A modified phase estimator is presented, which closely follows the new CRLB. Investigation of symbol error performance in short packet length reveals Star 16-QAM to be superior to Square 16-QAM for SNR<13 dB, which is a reasonable operating range for a coded system. Although Square 16-QAM and Star RR=1.8 are optimum for a perfect receiver, when the effect of phase estimation is considered, we find Star RR=3 to be better for SNR below 10 dB.