雷达与导航、声纳与对抗

0323021盲均衡算法抵抗水声信道多途干扰的实验研究[刊]/王峰//电声技术.—2003,(5).—4～6(L)介绍了盲均衡器对具有多途效应的 BPSK 相干水声通信数据均衡的实验结果。采用 Matlab 编程,获得了符合盲均衡要求的信源信号,通过在水池发射具有低频载波的信号,获得了具有多途水声信道特性的BPSK 数据,经过解调、同步等处理后,检验了隐含和直接使用高阶统计量的盲均衡算法对实际多途水声信道的均衡效果,并与传统的自适应均衡算法进行了性     

基于盲源分离的水声信道盲均衡处理方法

提出了一种基于盲源分离的水声信道讯均衡处理方法,通过对接收信号过采样构成源信号,采用了基于信息最大化原理(Infomax)在线分离算法进行了水声信道的盲均衡,并研究了时变水声信道条件下算法的均衡情况,仿真实验结果表明,该处理方法对多径水声信道具有较好的均衡效果,同时不受最小相位的条件限制。     

单通道带通信号的复基带盲解卷积算法

该文提出了一种基于多途信道单通道接收的带通数据(波束或传感器输出)自相关函数的盲解卷积算法。该算法先通过复解调将带通信号频谱搬移到0频率附近,然后进行低通滤波和降采样率,得到其复基带信号。再针对单通道复基带信号,以其自相关函数在零延时之外某区间内的实部平方和最小为准则,推导出复基带多途信号盲解卷积的LMS自适应迭代算法。该方法能够适用于带通高斯信号和非高斯信号,与基于高阶统计量的方法相比,对源信号概率分布具有较宽的适用面,计算机仿真结果验证了该方法的正确性。     

基于支持向量机的水声信道盲均衡算法研究

水声信道的带宽极为有限,与需要训练序列的自适应均衡算法相比盲均衡技术节省了带宽,特别适合高速水声通信和多点通信。针对基于高阶统计量的盲均衡算法收敛速度慢的缺点,研究了一种基于支持向量机的盲均衡算法,它收敛速度快并具有全局最优解。通过对浅海信道触发通信信号的计算机仿真,证明了算法的有效性。     

一种抵抗水声信道多途干扰的新的盲均衡算法

由于在时变水声信道中会产生随机相位失真，CMA算法的误码性能严重下降。在双模式盲均衡算法的基础上，提出了一种适用于水声信道的新的盲均衡算法——NCMA算法。计算机仿真结果表明，在时变水声信道中，该算法性能优于双模式算法，能够达到对信号相位失真的恢复，并且收敛性能优于双模式算法。     

基于多途模型匹配的浅海噪声源聚焦定位方法

 本文在建立符合水声相干多途信道特点的阵列信号模型的基础上,提出了基于多途模型匹配的浅海噪声源聚焦定位方法.该方法在本质上利用了基于模型和数据匹配的相干处理思想,充分利用了多途信道信息,生成与实际声传播特性相匹配的空间聚焦导向矢量,从而有效克服了多途效应的影响,提高了定位精度.进而结合基于最差性能优化的稳健聚焦处理器设计方法,对空间聚焦导向矢量实施约束,并划归为具有单一非线性约束的二次最小化问题,求解最优权矢量,修正得到新的空间谱形式.通过对空间谱、定位误差、-3dB波束宽度及主峰与最大旁瓣比性能进行对比分析,讨论了约束参数选取与失配误差程度之间的关系,并证明了新算法在存在失配误差的情况下,可有效提高MVDR(Minimum Variance Distortionless Response)高分辨算法的稳健性,保持较尖锐的谱峰及较强的背景起伏抑制能力.     

低信噪比的水声稀疏信道估计算法

研究了水声多途稀疏信道的估计方法,在此基础上提出了低信噪比环境下,具有很好的延迟分辨能力的优化算法。所提出的信道估计算法(相干-POCS)基于最大似然估计—匹配滤波和凸优化算法,提高信噪比的同时,利用水声信道的稀疏特性和残余的方差约束条件进行信道估计。计算机仿真结果表明,在低信噪比的情况下,使用所提出的算法(相干-POCS)能够正确估计水声信道的多径衰落,其分辨率和性能都优于目前所使用的匹配追踪法和凸集投影算法,可用于远距离水声信道研究。     

稀疏水声信道判决反馈盲均衡算法研究

针对高速水声通信中信道的稀疏特性,提出了一种基于常数模准则的稀疏水声信道判决反馈盲均衡算法。该算法将改进的常数模算法与一种变化的判决反馈均衡器结构(部分反馈均衡器)有机结合,利用水声信道的稀疏特性,不但很好地实现了稀疏水声信道的盲均衡,而且简化了计算,易于算法的硬件实现。用典型稀疏水声信道进行了计算机仿真。结果表明,该算法性能稳定,计算量小,稳态均方误差低,整体性能与基于自适应LMS的稀疏迭代算法接近。该研究为高速水声通信中稀疏信道的均衡提供了一种可实现的方法。     

LDPC码在浅海水声通信中的应用研究

LDPC码具有编码增益高、译码速度快、可并行译码等特点,是当前编码界的一个研究热点。针对复杂多变、强多途和大起伏干扰的浅海水声信道,建立了LDPC码在浅海水声信道中仿真的模型,仿真研究了浅海水声信道下采用和一积译码算法的LDPC码性能。仿真结果表明,LDPC码在3径多途信道下性能优越;当译码迭代3—5次,码长1000bit左右时基本上能满足水声通信对误码率为10^-4的基本要求。     

基于Chebyshev混沌序列的正交宽带调制解调方法

针对常规的相干解调和非相干解调在水声信道通信中可靠性差的问题,文中提出了一种基于Chebyshev混沌序列的正交宽带调制解调方法。该方法基于海洋水声信道环境与传播特点,采用统计特性接近环境噪声且具有尖锐自相关、宽带特性的Chebyshev混沌序列作为信息载体和同步信号,并利用欧氏距离分类器对接收信号进行非相干分类判决解调。该方法可有效抑制水声信道因多途传播而引起的频率选择性衰落和码间干扰,从而更好地实现低信噪比条件下可靠水声通信。仿真实验证明,在-10 dB以上信噪比环境下,文中所提通信方法在具有多途效应的水声信道上具有良好的通信性能。     

基于盲判决反馈均衡的GPS信号多径干扰抑制方法

给出了存在多径效应时的GPS接收信号模型,分析了多径干扰对GPS接收机跟踪环路的影响,将均衡技术应用于跟踪环路,提出了一种GPS信号多径干扰抑制方法.该方法在GPS信号跟踪环路中引入了判决反馈分数间隔均衡器,同时,根据GPS信号为BPSK调制且发送的伪码序列被导航电文调制后存在正负反转不确定性的特点,均衡器系数更新时选...     

Instantaneous Mixture Channel Selection for Blind Equalization Using Cumulant Features in MIMO Systems

Cognitive radio combined with multiple-input multiple-output (MIMO) communications provides high data rate, efficiency and high reliability. One of the most important challenges in MIMO communication is combating MIMO multipath channel. MIMO blind equalizers and channel estimators combat MIMO multipath channels without the use of training or pilot sequences. First, the multipath channel is converted into instantaneous mixture channel (IMC), using second-order statistics of the data. Then using higher-order statistics, these mixtures are separated. However, proper selection of IMC is a major challenge. In this paper, a novel blind algorithm for choosing the best IMC is proposed. The proposed algorithm is based on the cumulant value of the received signal.     

On Blind Equalization of Biorthogonal Signaling

Motivated by increasing interest in energy efficient modulations, we provide the first look at adaptive equalization of biorthogonal signaling. While this modulation has historically been considered only for use in narrowband systems without intersymbol interference (ISI), recent attention has been given to its use in ISI channels. Due to the fact that biorthogonal modulation (BOM) results in a source that is not i.i.d., however, classical blind adaptive equalization techniques cannot be directly applied to equalization of BOM signals. We first examine minimum mean-squared error (MMSE) and least mean squares (LMS)-based equalizers, and identify some peculiarities that arise in equalization of BOM signals when compared to more traditional modulations like binary phase shift keying (BPSK). Next, we present two novel blind algorithms for the adaptive equalization of BOM signals: LTBOMB and TROMBONE. We discuss the convergence properties of these algorithms, and demonstrate their performance with numerical simulations     

多径衰落环境中具有调制识别能力的盲均衡新算法

常数模盲均衡算法在对非常模信号进行均衡时稳态均方误差不能收敛至零,且不具备信号调制识别能力.因此,依照常数模算法代价函数的构造方法定义了一种余弦代价函数,提出了基于该代价函数的盲均衡新算法.该余弦代价函数可将BPSK、M-PAM、M-QAM信号的不同星座点映射至原点,从而使新算法在对上述常模或非常模信号进行均衡时稳态均方误差均能收敛至零,更重要的是新算法能够在多径衰落环境下实现对上述信号的调制识别.理论分析和仿真结果证明了新算法的优良性能.     

Blind identification and equalization based on second-orderstatistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel     

Fast Recovery Blind Equalization for Time-Varying Channels Using “Run-and-Go” Approach

In this paper, we propose a blind adaptive equalization scheme for time-varying channels, which combines a blind algorithm based on high order statistics (HOS) and decision-directed (DD) LMS algorithm. In contrast to the ldquostop-and-gordquo algorithm , where DD-LMS adaptation is stopped for unreliable decisions, the proposed algorithm applies a blind algorithm based on HOS for the unreliable decisions. Furthermore, the region of reliable decisions is updated corresponding to the estimated signal quality. Hence, the proposed ldquorun-and-gordquo algorithm inherits MMSE performance of DD-LMS and the (re)acquisition ability of the blind algorithm. Especially, for decision feedback equalizers (DFEs) the proposed algorithm provides robust blind initialization and reacquisition ability under time varying multipath environments.     

Blind equalizer for constant-modulus signals based on Gaussian process regression

A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.     

Downlink Specific Linear Equalization for Frequency Selective CDMA Cellular Systems

We derive and compare several linear equalizers for the CDMA downlink under frequency selective multipath conditions: minimum mean-square error (MMSE), zero-forcing (ZF), and RAKE. MMSE and ZF equalizers are designed based on perfect knowledge of the channel. The downlink specific structure involves first inverting the multipath channel to restore the synchronous multi-user signal transmitted from the base-station at the chip-rate, and then correlating with the product of the desired user's channel code times the base-station specific scrambling code once per symbol to decode the symbols. ZF equalization restores orthogonality of the Walsh-Hadamard channel codes on the downlink but often suffers from noise gain because certain channel conditions (no common zeros) are not met; MMSE restores orthogonality only approximately but avoids excessive noise gain. We compare MMSE and ZF to the traditional matched filter (also known as the RAKE receiver). Our formulation generalizes for the multi-channel case as might be derived from multiple antennas and/or over-sampling with respect to the chip-rate. The optimal symbol-level MMSE equalizer is derived and slightly out-performs the chip-level but at greater computational cost. An MMSE soft hand-off receiver is derived and simulated. Average BER for a class of multi-path channels is presented under varying operating conditions of single-cell and edge-of-cell, coded and un-coded BPSK data symbols, and uncoded 16-QAM. These simulations indicate large performance gains compared to the RAKE receiver, especially when the cell is fully loaded with users. Bit error rate (BER) performance for the chip-level equalizers is well predicted by approximate SINR expressions and a Gaussian interference assumption.     

Modulation Recognition in Maritime Multipath Channels: A Blind Equalization-Aided Deep Learning Approach

Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required.     

Deterministic approaches for blind equalization of time-varyingchannels with antenna arrays

In this paper, we study the blind equalization problem of time-varying (TV) systems where the channel variations are too rapid to be tracked with conventional adaptive equalizers. We show that using a finite Fourier basis expansion, a TV antenna array system can be cast into a time-invariant multi-input, multi-output (MIMO) framework. The multiple inputs are related through the bases, thereby allowing blind equalization to be accomplished without the use of higher order statistics. Two deterministic blind equalization approaches are presented: one determines the channels first and then the equalizers, whereas the other estimates the equalizers directly. Related issues such as order determination are addressed briefly. The proposed algorithms are illustrated using simulations