利用飞机散射信号查找干扰源的定位算法

主要分析了利用民航飞机散射信号的多普勒频移定位地面干扰源的2种定位算法。第1种算法通过零误差等高线相交来进行定位,第2种算法通过比对理论和实际的时频曲线最大互相关值进行定位。对这2种定位算法的优缺点进行了分析,理论仿真和实际测试结果表明,2种定位算法的误差较小,均能较好地完成定位工作。因此,通过民航飞机的散射信号来定位地面干扰源是完全可行的。     

基于PCA改进的粒子滤波算法研究

本文首先对几种经典的航迹滤波算法进行说明,包括扩展卡尔曼滤波算法、无迹卡尔曼滤波算法、粒子滤波算法,并针对无迹卡尔曼滤波算法以及卡尔曼滤波算法要求系统是近似高斯后验分布模型,对系统要求较高,以及粒子滤波算法实验过程中出现的粒子退化的问题提出一种改进的粒子滤波算法,并设置仿真实验,通过对比实验验证改进的算法的可行性.     

高斯条件下基于粒子滤波的声源定位

介绍了粒子滤波的基本思想和具体算法实现步骤,并将粒子滤波算法应用在声源定位中,解决了在高斯噪声环境下的声源定位问题。所提出的基于粒子滤波的声源定位方法,在高斯噪声情况下,甚至在低信噪比（SNR〈-20dB）情况下,定位的均方根误差RMSE值均小于0．2m。     

气象雷达超低副瓣失配滤波处理技术研究

在雷达信号处理中,通过匹配滤波进行脉冲压缩可以获得最大化的信噪比,有效地减小了雷达回波中噪声对信号的影响。然而,脉冲压缩的输出具有较高的距离旁瓣,在气象雷达探测中,由于空间分布的散射粒子之间反射强度相差较大,弱散射粒子的回波容易掩没在强散射粒子的旁瓣中,因此有超低旁瓣的需求。本文主要研究了能够降低旁瓣的非线性调频（NLFM）波形和最小积分旁瓣水平（ISL）失配滤波器,分析了多普勒频移对其性能的影响,并在最小ISL滤波器的基础上通过进一步对滤波器系数加权的方法,使得在回波具有多普勒频移的情况下也能达到超低旁瓣的性能。      

外辐射源定位中的修正增益扩展卡尔曼滤波

针对基于外辐射源的固定单站无源定位问题,给出了一种对运动目标进行定位跟踪的改进型滤波算法。文中首先建立目标角度信息、直达波与散射波的时间差信息以及多普勒频移信息的观测方程,并推导了每个观测方程的修正增益函数,然后利用多个时刻的观测值扩充了观测方程,从而给出了一种基于扩充观测方程的修正增益扩展卡尔曼滤波（MGEKF）算法。经过计算机仿真验证,该算法可以提高定位精度,并能有效地抑制滤波发散。     

粒子滤波和正则粒子滤波算法性能研究

主要介绍了解决系统状态估计问题的滤波算法。在提出非线性高斯系统模型的基础上着重阐述了扩展卡尔曼滤波(EKF)、粒子滤波(PF)和正则粒子滤波(RPF)算法。对这三种算法在不同的噪声条件下的估计性能进行了仿真分析。结果表明,在非线性高斯系统中,PF和RPF的估计性能远比EKF的估计性能要好,由于RPF是从离散分布中重构其近似连续分布,再从该连续分布中采样粒子,估计性能比PF要好,尤其在小噪声的环境下,估计性能更加稳定。     

采样滤波算法在单站无源定位中的应用

讨论了用采样的方法近似非线性分布来解决无源定位中的非线性问题,提出了一种简单的正则粒子滤波,克服了标准粒子滤波用于单站无源定位中出现的粒子贫乏现象,将粒子滤波成功应用到无源定位中,计算机仿真表明该算法的定位精度较Unscented卡尔曼滤波（UKF）有一定的提高。     

对称Alpha稳定分布噪声下单天线干扰消除算法

蜂窝移动通信系统性能受限于码间干扰、同频干扰和脉冲噪声等因素。本文提出一种基于粒子滤波的单天线干扰消除算法。首先,对脉冲噪声采用Alpha稳定分布进行建模,并对该模型进行高斯近似,递推得到多个未知信道参数的联合后验概率。其次,提出基于延迟粒子滤波的同信道传输码元最大后验估计方法。理论推导和仿真实验结果表明本文算法能够消除码间干扰和同频干扰对码元检测的影响,与其他干扰消除算法相比,特别是在强脉冲噪声和未知信道参数情况下,具有一定的优势。      

粒子滤波在目标跟踪算法中的应用研究

针对非高斯、强噪声背景下的高机动目标实施跟踪时,卡尔曼滤波、扩展卡尔曼滤波等算法将出现滤波精度下降甚至发散现象。粒子滤波方法作为一种基于贝叶斯估计的非线性滤波算法,在处理非高斯非线性时变系统的参数估计和状态滤波问题方面有独到的优势。以目标跟踪问题为背景,将粒子滤波与卡尔曼滤波算法进行了对比研究。     

复杂环境下基于噪声匹配的最大多普勒频移估计

本文针对城市复杂信道环境下的最大多普勒频移估计需求,根据莱斯衰落信道中电平通过率(LCR)算法的理论推导,提出了一种基于噪声匹配的最大多普勒频移估计算法.所提算法通过对接收信号进行低通滤波处理,实现干扰噪声与多普勒检测器之间的匹配,从而有效提高最大多普勒频移的估计性能.而且基于莱斯衰落信道下最佳滤波比值的分析和推导,得...     

稳定分布噪声下基于粒子滤波的多径时变信道盲均衡算法

提出了一种基于粒子滤波的多径时变信道盲均衡算法,并在此基础上进行扩展,提出了一种基于延迟抽样的盲均衡算法。新算法的贡献可总结为：推导出对称α稳定分布（SαS）噪声下对传输码元进行最大后验估计的盲贯序算法;对SαS分布噪声进行高斯近似并递推出信道及噪声未知参数的联合后验分布。仿真结果表明,所提出的算法是有效的,特别是在较强脉冲噪声情况下要优于其他算法。     

Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature

In this paper, a new version of the quadrature Kalman filter (QKF) is developed theoretically and tested experimentally. We first derive the new QKF for nonlinear systems with additive Gaussian noise by linearizing the process and measurement functions using statistical linear regression (SLR) through a set of Gauss-Hermite quadrature points that parameterize the Gaussian density. Moreover, we discuss how the new QKF can be extended and modified to take into account specific details of a given application. We then go on to extend the use of the new QKF to discrete-time, nonlinear systems with additive, possibly non-Gaussian noise. A bank of parallel QKFs, called the Gaussian sum-quadrature Kalman filter (GS-QKF) approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. The weights are obtained from the residuals of the QKFs. Three different Gaussian mixture reduction techniques are presented to alleviate the growing number of the Gaussian sum terms inherent to the GS-QKFs. Simulation results exhibit a significant improvement of the GS-QKFs over other nonlinear filtering approaches, namely, the basic bootstrap (particle) filters and Gaussian-sum extended Kalman filters, to solve nonlinear non- Gaussian filtering problems.     

利用时变噪声贝叶斯卡尔曼滤波的室内移动目标定位

基于射频识别的指纹滤波定位技术是当前室内定位中常使用的技术之一。针对该技术存在的卡尔曼滤波算法不能准确适应环境噪声变化,致使定位精度不高的问题,提出了一种适应时变噪声的贝叶斯卡尔曼滤波算法。所提算法结合Sage-Husa滤波模型和贝叶斯模型,实现了过程和测量协方差矩阵的最优化,有效地降低了噪声,提高了指纹滤波定位的精度。实验结果表明,与变分贝叶斯卡尔曼滤波和Sage-Husa滤波相比,无障碍情况下,基于改进算法的定位精度提高了6%以上;有障碍干扰下,则提高了14. 6%以上。     

Adaptive nonlinear filters for narrow-band interference suppressionin spread-spectrum CDMA systems

This paper presents a novel nonlinear filter and parameter estimator for narrow band interference suppression in code division multiple access spread-spectrum systems. As in the article by Rusch and Poor (1994), the received sampled signal is modeled as the sum of the spread-spectrum signal (modeled as a finite state independently identically distributed (i.i.d.) process-here we generalize to a finite state Markov chain), narrow-band interference (modeled as a Gaussian autoregressive process), and observation noise (modeled as a zero-mean white Gaussian process). The proposed algorithm combines a recursive hidden Markov model (HMM) estimator, Kalman filter (KF), and the recursive expectation maximization algorithm. The nonlinear filtering techniques for narrow-band interference suppression presented in Rusch and Poor and our proposed HMM-KF algorithm have the same computational cost. Detailed simulation studies show that the HMM-KF algorithm outperforms the filtering techniques in Rusch and Poor. In particular, significant improvements in the bit error rate and signal-to-noise ratio (SNR) enhancement are obtained in low to medium SNR. Furthermore, in simulation studies we investigate the effect on the performance of the HMM-KF and the approximate conditional mean (ACM) filter in the paper by Rusch and Poor, when the observation noise variance is increased. As expected, the performance of the HMM-KF and ACM algorithms worsen with increasing observation noise and number of users. However, HMM-KF significantly outperforms ACM in medium to high observation noise     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods     

一种基于二维处理的OFDM信道估计算法

该文提出了一种新的信道估计算法,用于无线移动信道下的正交频分复用(OFDM)系统。该算法对接收的导频信号(Pilot)分别在多径展宽域和多普勒展宽域进行处理,显著地降低了子载波间干扰和高斯白噪声的影响。此外,多普勒展宽域处理的滤波器是动态设计的,具有良好的自适应性。仿真结果表明,在不同的多普勒频偏下,该算法都有良好的性能。     

一种目标特性辅助的积分粒子滤波新方法

针对非均匀稀疏采样环境下目标跟踪中的非线性滤波问题,提出了一种基于Gauss-Hermite积分和目标特性辅助的积分粒子滤波新方法(AQPF).在该方法中,构建了基于Gauss-Hermite积分的积分点概率密度函数作为重要性密度函数,同时,在时间更新阶段引入目标观测、目标观测的有效时间间隔、目标速度等目标特性,综合改善滤波器中预测粒子和预测协方差估计的准确性和粒子的多样性,有效提高目标状态的估计性能.实验结果表明,提出方法的估计性能要明显好于无迹kalman滤波(UKF)、积分kalman滤波(QKF)、粒子滤波(PF)、辅助粒子滤波(APF)和高斯粒子滤波(GPF),能够有效对目标状态进行估计.