多循环频率循环时延估计方法及性能分析

由于单循环频率循环估计子估计性能对循环频率的选择有较大的依赖性,使其在实际应用中受到了较大的限制.为解决该问题,文章研究并给出了多循环频率循环时延估计方法,还详细推导得到了多循环频率估计子组的估计均方误差.在平稳高斯白噪声和慢变化时变高斯白噪声条件下对BPSK和QPSK信号的仿真结果表明,较高信噪比时(SNR＞-6dB)循环估计子组的估计理论精度与仿真精度基本上是一致的,并且三循环频率循环估计子组估计性能要优于双循环频率循环估计子组,而双循环频率循环估计子组的估计性能要优于单循环频率循环估计子.仿真结果充分说明了文章理论分析的正确性,也说明了多循环频率循环时延估计方法的有效性及估计算子的稳健性.     

双基地MIMO雷达参数估计

文中探讨了双基地MIMO雷达进行参数估计的方法。针对时域噪声为高斯白噪声,存在空间高斯有色噪声的背景,引入时间延迟这个因素构造旋转因子,同时利用匹配滤波器和ESPRIT子空间算法实现目标角度以及多普勒频率的参数联合估计。仿真结果表明这种算法可以消除系统中空间高斯有色噪声的影响,得到较高的估计精度。     

8PSK/16APSK信号的频偏估计及误比特率分析

针对8移相键控(PSK)和16振幅移相键控(APSK)信号的频偏估计及误比特率,介绍了高阶的恒包络数字调制方式8PSK和16APSK,重点研究2种调制信号的频偏估计,并搭建通信链路模型。采用Matlab对8PSK,16APSK调制信号在加性高斯白噪声下的频偏估计进行仿真分析,并使用频偏估计得到的载波频率进行相干解调,最后对比分析实际误比特率与理论误比特率。仿真结果表明：在不同信噪比的加性高斯白噪声信道条件下,8PSK的抗噪声性能比16APSK好;采用本文给出的频偏估计方法能够较好地接近理论误比特率,为设计通信系统提供依据。     

基于高阶循环累积量的OFDM子载波盲估计

该文针对通信对抗和OFDM信号解调的实际应用,提出一种利用高阶循环累积量估计OFDM信号子载波的方法。由于高阶循环累积量能有效抑制平稳噪声和非平稳高斯噪声,通过理论分析可证明在上述噪声背景下,子载波采用2/4PSK调制的OFDM信号的特定四阶循环累积量仅在子载波处存在,因此可以通过检测此循环频率实现子载波的估计。该算法还可推广到采用星形QAM和高阶PSK调制的OFDM信号。仿真实验验证了该算法的有效性。     

四阶累积量稀疏表示的DOA估计方法

针对现有稀疏重构DOA估计算法不能抑制噪声项以及在高斯色噪声背景下不再适用的问题,本文提出了基于四阶累积量稀疏重构的DOA估计方法。首先,利用接收数据的四阶累积量构建了稀疏表示模型,该模型抑制了噪声项;其次对四阶累计量矩阵进行奇异值分解,化简了稀疏表示模型,通过奇异值分解,不仅减小了数据规模,而且进一步抑制了噪声。对于稀疏表示模型的求解,先利用信号子空间与噪声子空间的正交特性选取权值矢量,然后利用加权l1范数法对模型求解实现DOA估计。理论分析和仿真实验表明本文算法在高斯白噪声和色噪声背景下均适用;能够处理非相干和相干信号,且在低信噪比条件下,对相干信号有更高的估计精度;较之同类的稀疏重构算法,本文算法具有较低的算法复杂度和更高的角度分辨力。      

二进制移相键控信号信噪比估计研究

通过使用最大似然估计算法,对二相移相键控（2PSK）信号在高斯白噪声条件下的信噪比估计进行了研究,推导出信号功率、噪声功率的估计公式,依据该公式构建了2PSK的信噪比估计仿真模型,并在Matlab中进行了仿真计算,仿真结果表明在数据长度达到2000个左右时,信噪比的估计性能较好。     

一种新的OFDM采样频偏盲估计算法

针对OFDM技术对系统的时钟同步要求非常高的问题,提出了一种新的采样频偏盲估计算法,该算法可较好地解决估计精度和估计复杂度的折中问题。通过仿真结果表明,在高斯白噪声信道下,与同类算法相比,该算法不仅估计精度高,而且估计复杂度低,符合实际工程应用。     

窄带通信信号时延估计的循环谱组合切片法

循环时延估计方法只使用单一循环频率进行时延估计,对信号的循环平稳信息利用不充分,且估计性能对循环频率的选择有较大依赖性.针对此问题,研究了利用多循环频率组合进行时延估计的方法,为减小循环谱密度函数估计的计算量并提高时延估计的精度,阐述了利用循环谱切片组合进行时延估计的方法,在不同信噪比的高斯噪声和同频干扰条件下分别进行了仿真实验.仿真结果表明,多循环频率时延估计法较单循环频率时延估计法有更高的估计性能.     

基于循环前缀的OFDM同步算法研究

针对传统基于循环前缀的定时同步算法在多径信道下定时估计精度明显下降的问题,在经典的定时同步算法最大似然估计算法的基础上,研究了一种整体相关的OFDM定时估计算法,最后结合两者提出了一种有效利用截短循环前缀进行定时估计的改进算法,避免了多径环境下ISI带来的定时估计误差。对算法进行了仿真,表明在高斯白噪声信道和多径衰落信道下改进的算法都具有较好的性能。     

基于M估计变步长自适应仿射投影算法的稳健时延估计

该文提出了一种基于M估计变步长自适应仿射投影方法的稳健时延估计(TDE)算法。该算法将自适应仿射投影算法应用于时延估计,无须事先假定信号和噪声的统计特性,自适应调整自身参数;应用稳健M估计理论,抵消重尾噪声干扰。数值仿真表明,在高斯噪声、非高斯噪声甚至冲激噪声的干扰下,该文算法比高阶统计量法和最小均方自适应法有更强的稳健性和更高的估计精度。     

White Gaussian Noise Energy Estimation and Wavelet Multi-threshold De-noising for Heart Sound Signals

White Gaussian noise (WGN) commonly exists in acquisition and transmission of heart sound (HS) signals. The energy distributions of WGN and HS in wavelet decomposition levels (WDLs) are explored. The statistical analysis indicates that for WGN, energy proportions of detail portions of WDLs and energy proportion of the 2nd WDL are fixed. This finding is verified by using Monte Carlo test. Moreover, for a HS signal recorded under sampling frequency of 4 kHz, energies of the 1st and 2nd WDL are almost the same, which are validated by theoretical analysis and practical observation on three HS benchmark datasets. Based on these findings, equations estimating WGN energy and signal to noise ratio (SNR) for a noisy HS signal are created. In addition, a novel energy distribution-based wavelet multi-threshold de-noising approach (ED-WMTD) is proposed to reduce WGN. In which, firstly based on the energy distribution in WDLs and estimated energy of WGN, WGN energy in detail portion of each WDL is figured out. Then, soft-threshold method is adopted. The best threshold in a WDL is defined as the one by which the energy loss of noisy HS signal in this WDL is most similar to the energy of WGN in detail portion of this WDL. The accuracy of such a WGN energy estimation method is evaluated by average error of SNR estimation. ED-WMTD is assessed using mean square error and compared with four generally used WMTD methods. Experimental results show that this novel HS de-noising approach not only filters out HS noise effectively but also well retains its pathological information.     

贝叶斯估计器先验模型参数的迭代感知方法

充分利用先验信息是提高统计推断性能的有效途径之一。贝叶斯估计的先验信息模型参数必须在设计阶段确定下来,与待探测环境模型参数之间必然存在不一致性,从而有可能导致估计质量的下降。该文首先给出了基于估计性能的先验模型参数感知的一般性框架。基于该框架,针对白高斯噪声中直流信号的贝叶斯估计器,分析了先验失配条件下的估计性能,给出了一种先验模型参数迭代感知的算法。利用计算机仿真分析了该估计器性能对先验模型参数的敏感性和稳健性,分析了不同条件下的迭代感知过程。计算机仿真结果表明,该文给出的迭代感知方法建立了从估计性能到先验模型参数的反馈,通过估计器与待探测场景的多次交互,可以使得先验模型与当前场景模型匹配。     

一种基于二倍钟采样的OFDM载波偏差估计算法

本文从OFDM信号的二倍钟采样出发,给出了一种载波偏差估计算法,该算法直接利用时域信号的采样来估计载波偏差,克服了频域实现算法受载波偏差影响大的缺点,使得算法在不同的载波偏移下具有相同的估计精度,载波偏差估计范围大,同时该算法充分利用信号的有效能量,估计精度高,优于采用循环前缀的算法;论文同时给出了算法性能的理论分析和计算机仿真结果.     

Frequency estimation of distorted non-stationary signals using complex H∞ filter

Mechanical vibration signals are always composed of harmonics of different order. A novel estimator is proposed for estimating the frequency of sinusoidal signals from measurements corrupted by White Gaussian noise with zero mean. Also low frequency sinusoidal signal is considered along with third and fifth order harmonics in presence of noise for estimating amplitudes and phases of different harmonics. The proposed estimator known as complex H_∞ filter is applied to a noisy sinusoidal signal model. State space modeling with two and three state variables is used for estimation of frequency in presence of white noise. Various comparisons in terms of simulation results for time varying frequency reveal that the proposed adaptive filter has significant improvement in noise rejection and estimation accuracy. Comparison in performance between two and three states modeling is presented in terms of mean square error (MSE) under different SNR conditions .The computer simulations clearly indicate that two states modeling based on Hilbert transform performs better than three states modeling under high noisy condition. Frequency estimation performance of the proposed filter is also being compared with extended complex Kalman filter (ECKF) under same noisy conditions through simulations.     

OFDM系统相位噪声的Huber ML抑制方法

OFDM系统对相位噪声(PHN)非常敏感,由其引起的子载波相位旋转和载波间干扰(ICI)会严重恶化系统性能。该文提出了一种新的OFDM接收机PHN估计和抑制方法,利用判决数据采用Huber ML估计方法对PHN引起的ICI分量进行迭代估计,将估计的ICI系数变换到时域来校正每个样点的相位误差,从而达到抑制PHN的目的。与传统的基于判决的估计方法相比,该方法对判决的错误传播具有鲁棒性,可进一步提高ICI抑制性能。对算法在AWGN和多径信道环境下的仿真结果表明所提方法对PHN引起的ICI有较好的抑制性能。     

Refining inaccurate sensor positions using target at unknown location

This paper addresses the problem of improving the receiver positions in a sensor array using the positioning measurements of targets at unknown locations. Using an efficient estimate of the target locations, a computationally efficient estimator is proposed to refine the sensor positions using the same set of measurements from the targets. The considered measurements include TOA and TDOA. The proposed estimator has closed-form and is able to reach the CRLB accuracy under small Gaussian noise which is supported by theoretical analysis and simulation studies.     

采用Lanczos算法快速估计噪声子空间

提出一种采用Lanczos算法估计噪声子空间的新方法。该方法采用传统的空间平滑技术解相干,然后由多级维纳滤波器的预滤波器的性质可知,多级维纳滤波器的冗余分解级的预滤波器可以构成一个噪声子空间。由此可以采用Lanczos算法快速估计到噪声子空间。由于不需要对协方差矩阵作特征值分解,而且所要求的冗余分解的级数较少,其运算量比基于特征值分解方法要小得多。此外,采用Lanczos算法计算降维矩阵和冗余矩阵只构成多级维纳滤波器的前向递推,从而使得算法的复杂度大大降低。最后,计算机仿真验证了该方法的有效性。     

Tracking moving objects in television images

The estimation of the velocity of objects imaged by television cameras is useful in different areas of image processing.The problem is solved by means of a linear estimation algorithm and the effects of noise superimposed to the signal are analyzed. The structure of a real-time estimator is then presented. Experimental results show that a very fine accuracy is obtained. They encourage its application to image coding for redundancy reduction using movement compensation.     

Convergence of the maximum a posteriori path estimator in hiddenMarkov models

In a hidden Markov model (HMM) the underlying finite-state Markov chain cannot be observed directly but only by an additional process. We are interested in estimating the unknown path of the Markov chain. The most widely used estimator is the maximum a posteriori path estimator (MAP path estimator). It can be calculated effectively by the Viterbi (1967) algorithm as is, e.g., frequently done in the field of coding theory, correction of intersymbol interference, and speech recognition. We investigate (component-wise) convergence of the MAP path estimator. Convergence is shown under the condition of unbounded likelihood ratios. This condition is satisfied in the important case of HMMs with additive white Gaussian noise. We also prove convergence, if the Markov chain has two states. The so-called Viterbi paths are an important tool for obtaining these results