分布式多天线信道时变特征参数的联合估计

针对分布式多天线信道随机时变特征参数的获取问题,通过参数化建模方法建立信道时变参数的自回归模型,将由频率偏置和复信道衰落构成的强非线性观测方程在估计值处展开成泰勒级数进而线性化观测方程后,运用扩展卡尔曼滤波算法联合估计未知参数。仿真结果表明,该方法可在序贯的观测值下对信道时变参数进行联合估计和跟踪,能获得逼近克拉默—拉奥下界的估计精度。     

基于序贯蒙特卡罗算法的多天线快时变信道的盲跟踪

针对通信系统时变信道采用蒙特卡罗算法进行盲信道跟踪,并将该盲跟踪算法用于多天线信道及空时分组编码的情况,在相同的系统条件下与卡尔曼滤波跟踪算法进行了性能比较,并讨论了系统存在载波频偏情况下的跟踪性能。仿真结果表明,序贯蒙特卡罗算法可以对时变信道进行很好的跟踪。     

基于PSP均衡技术的CPM检测算法

针对时变频率选择性衰落信道,研究了连续相位调制（CPM）信号的逐幸存检测算法。该算法在未知信道状态的条件下,利用训练序列对信道参数进行初始估计。在对CPM信号进行Viterbi解调过程中,采用PSP技术实现信道的无延时跟踪。基于频域均衡的CPM检测算法虽然可以有效抗多径干扰且计算复杂度较低,但不能对时变信道进行跟踪。仿真结果表明,在时变多径信道下,基于PSP均衡的CPM检测算法能有效地进行信道参数估计,比频域均衡算法具有更好的误码性能。     

机载雷达红外传感器集中式融合与管理

提出一种机载多传感器集中式序贯融合与管理的方法.传统扩展卡尔曼滤波融合算法滤波精度不高,因此先利用雷达传感器的量测,采用修正扩展卡尔曼滤波算法对目标状态进行估计,再把估计值作为红外传感器的预测值进行序贯融合.在此基础上采用分辨力增益的方法对传感器进行管理.仿真结果表明该方法能够提高对目标的跟踪精度,增强跟踪系统对环境变化的适应能力.     

时变条件下MIMO-OFDM系统中的信道估计算法

本文设计了时变多径衰落条件下MIMO-OFDM系统中一种新的信道估计算法.该算法结合递归EM算法和Kalman预测对时变信道进行跟踪.借助软球形译码器(List Sphere Decoder,LSD)产生的搜索列表,递归EM算法序贯遍历搜索列表中可能的符号组合来估计各个子载波上的信道频率响应;基于获得的信道频率响应估计,Kalman预测器利用衰落信道的时域二阶统计特性进一步跟踪信道时变.仿真结果表明:本文设计的算法可以有效跟踪信道时变,性能优于传统的软输入Kalman滤波算法.     

基于顺序蒙特卡罗方法的窄带MIMO信道盲跟踪

目前流行的空时处理技术的关键假设在于接收机已知MIMO信道状态信息,而MIMO信道是时变的,所以如何跟踪MIMO信道就变得十分重要。该文提出了一种基于顺序蒙特卡罗技术的窄带MIMO信道盲跟踪方法,仿真结果表明该方法能够很好地跟踪信道。     

基于标签多伯努利滤波器的机动小目标检测前跟踪

标签多伯努利(LMB)滤波器在传统多伯努利滤波器基础上引入标签空间,能够实现真正意义上的多目标轨迹级滤波.文章对红外小目标的运动和量测进行建模,将标签多伯努利应用到红外小目标检测前跟踪领域.在此基础上,为了实现对运动模型时变目标的检测前跟踪,将交互式多模型(IMM)与LMB检测前跟踪算法相结合,提出IMM-LMB检测前跟踪算法.此外,给出了该算法的序贯蒙特卡罗实现.仿真结果表明,所提算法能够从输入的原始图像中直接实现轨迹级多目标检测和跟踪,且能够在线更新多模型概率,更好的适应多机动目标场景.     

一种新的红外弱小目标检测与跟踪算法

针对低信噪比下红外序列图像中弱小目标的检测与跟踪问题,提出了一种新的基于双边滤波的方法.首先将传统的二维双边滤波扩展为空-时三维双边滤波,由于同时利用了红外序列的空域信息和时域信息,该三维双边滤波能在抑制噪声的同时增强目标和背景之间的对比度.用其实现红外图像的预处理,再用门限分割检测出红外序列中的弱小目标.同时,用序贯蒙特卡洛方法对检测到的弱小目标进行跟踪.实验中,用实际红外序列图像对算法进行了验证,结果表明,在低信噪比下,所提算法能对红外弱小目标进行实时检测和跟踪.     

基于导频和修正Kalman滤波的MIMO-OFDM信道估计方法

提出了一种适用于时间频率选择性衰落信道的MIMO-OFDM系统的组合信道估计方法。采用AR过程对信道进行建模,利用基于导频的低维Kalman滤波算法进行信道估计,并采用LS算法估计时变的信道衰减因子。Kalman滤波跟踪了信道的时域相关性,为了同时跟踪信道的频域相关性,采用了一种基于MMSE(minimum mean square error)的合并器对Kalman滤波算法进行修正。仿真表明,提出的这种组合算法降低了传统的Kalman滤波结构的复杂度,能够跟踪信道的时频变化,改进了基于LS准则的信道估计算法,并且与复杂的高维Kalman滤波算法的信道估计性能相当。     

莱斯信道的MLSD技术研究

主要研究在时变多径衰落信道下,基于Viterbi算法的最大似然序列检测技术（MLSD）.为了解决MLSD信道估计问题,使均衡器能够及时跟踪信道的变化,提出了基于逐幸存处理（PSP）和最小存活路径（MSP）的MLSD算法.在莱斯信道环境下,对上述算法进行了仿真.仿真结果表明,将RLS-MSP算法与减状态MLSD算法相结合的信道均衡技术具有对复杂信道的强跟踪能力,同时保证了较高的均衡性能和合理的复杂度.     

A Method of Time-Varying Rayleigh Channel Tracking in MIMO Radio System

Multi-input multi-output (MIMO) technique, which uses multiple antennas at transmitter and receiver, is the essential technique of the next generation mobile communication system. It can greatly increase the system capacity and spectral efficiency without additional bandwidth[1]. Because of the time-varying nature of the wireless channels and the interference (including inter-symbol interference and inter-user interference), the equalization at the receiver is more difficult to achieve. Amon…     

Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model the effects of inter-symbol interference (ISI), co-channel interference (CCI), and noise. A low-order autoregressive model approximates the MIMO channel variation and facilitates tracking via a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay Δ > 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate     

Joint Time-Domain Tracking of Channel and Frequency Offsets for MIMO OFDM Systems

In this paper we address the problem of joint channel and frequency offset estimation and tracking in multiple-input multiple-output (MIMO) OFDM systems for mobile users. The proposed method stems from extended Kalman filtering and is suitable for time-frequency-space selective channels. Separate offset for each MIMO channel branch is considered because of the mobility and rich scattering. The channel taps and the frequency offsets are estimated in time-domain while the equalization is performed in frequency domain. Simulation results demonstrate that the proposed method tracks time-varying channels and frequency offsets with high fidelity. Realistic channel models are used in mobile scenarios. The proposed time-domain approach has improved performance and robustness in comparison to purely frequency domain processing. Computational complexity is lower as well.     

Bayesian sequential state estimation for MIMO wireless communications

This paper explores the use of particle filters, rooted in Bayesian estimation, as a device for tracking statistical variations in the channel matrix of a narrowband multiple-input, multiple-output (MIMO) wireless channel. The motivation is to permit the receiver to acquire channel state information through a semiblind strategy and thereby improve the receiver performance of the wireless communication system. To that end, the paper compares the particle filter as well as an improved version of the particle filter using gradient information, to the conventional Kalman filter and mixture Kalman filter with two metrics in mind: receiver performance curves and computational complexity. The comparisons, also including differential phase modulation, are carried out using real-life recorded MIMO wireless data.     

MIMO Channel Estimation and Tracking Based on Polynomial Prediction With Application to Equalization

This paper presents a multiple-input-multiple-output (MIMO) receiver design with integrated channel estimation and tracking for a time-varying frequency-selective Rician or Rayleigh fading environment. It first extends a polynomial-predictor-based channel estimation and tracking approach to a MIMO system. The structure and complexity of the estimator are similar to that of an optimum estimator using a Kalman filter, but it does not require a priori knowledge of the channel statistics. It employs a fixed-state transition matrix using precomputed polynomial coefficients and can be used in a Rician fading environment without reconfiguration. It is integrated with a MIMO minimum-mean-squared-error decision feedback equalizer, and simulation results show that the system performance using the estimator can be made comparable to that employing a Kalman estimator under a broad range of channel conditions.     

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。     

一种可变遗忘因子的卡尔曼滤波信道跟踪

在快衰落信道下,时变特性会使导频时段与数据时段的信道冲激响应存在误差,所以需要引入信道跟踪。实践证明,系统建模与实际模型存在一定偏差时,标准卡尔曼滤波的鲁棒性较差。因此,文中提出了一种可变遗忘因子的卡尔曼滤波算法。仿真表明,与标准卡尔曼滤波相比,可变遗忘因子的卡尔曼滤波信道跟踪具有更好的鲁棒性。     

基于扩展卡尔曼滤波的MIMO迭代信道估计方法

针对高速移动场景下信道快衰落、非平稳等特性导致下行链路信道估计性能受限的问题,提出了一种适用于高速移动环境下行链路的MIMO信道估计方法.采用自回归过程对信道建模,构造自反馈的扩展卡尔曼滤波器(EKF)追踪信道响应及其时域相关系数.采用迭代接收机的结构解决了在MIMO环境下观测方程欠定的问题.仿真结果表明,在高速移动环境下所提方法相较于最小二乘估计等传统方法提升了信道估计的均方误差和系统的误码率性能,可应用于高速列车无线通信设备的接收机基带信号处理系统.     

IMM-based Kalman filter for channel estimation in UWB OFDM systems

Ultra-wide band (UWB) communication is one of the most promising technologies for high-data rate wireless networks for short-range applications. This paper proposes a blind channel estimation method namely Interactive Multiple Model (IMM)-based Kalman algorithm for UWB OFDM systems. IMM-based Kalman filter is proposed to estimate frequency selective time-varying channel. In the proposed method, two Kalman filters are concurrently estimating channel parameters. The first Kalman filter, namely the Static Model Filter (SMF) gives an accurate result when the user is static while the second Kalman filter namely the Dynamic Model Filter (DMF) gives an accurate result when the receiver is in moving state. The static transition matrix in SMF is assumed as an Identity matrix where as in DMF, it is computed using Yule–Walker equations. The resultant filter estimate is computed as a weighted sum of individual filter estimates. The proposed method is compared with other existing channel estimation methods.     

基于空间分集的MIMO雷达目标跟踪算法研究

针对基于空间分集的多输入多输出(MIMO)雷达多发多收的特殊结构,建立了基于逆协方差形式扩展卡尔曼滤波算法的MIMO雷达目标跟踪滤波模型,分析了影响算法滤波精度的4个因素,分别为信噪比、信号带宽、天线数以及天线与目标间的位置关系。通过使目标位置状态估计均方误差最小,在特定天线数条件下利用数值方法得到了使滤波精度达到最优的天线放置形式。仿真实验给出了不同信噪比、信号带宽、天线数以及天线与目标间不同位置关系时的MIMO雷达跟踪性能。