基于杂波自由度的STAR模型参数估计方法

空时自回归(STAR)方法是一种基于自回归(AR)模型的参数化杂波抑制方法,该方法所需运算量小,在极少训练样本数条件下可获得良好的杂波抑制性能,但性能受AR模型参数影响较大.本文分析了STAR方法抑制杂波的基本原理,从子空间杂波对消的角度提出了一种基于杂波自由度的AR模型参数估计算法,该算法可准确、快速的确定AR模型参数,大大减少了传统AR模型参数估计方法的复杂度.最后,通过仿真实验验证了本文所提参数估计方法的有效性.     

短波多音并行副载波跳变抗干扰通信建模仿真

论文建立了短波数据链多音并行体制的通信仿真模型,在此模型的基础上分析研究了其抗窄带干扰和宽带干扰的性能。提出了一种针对窄带多音干扰的短波数据链副载波跳变抗干扰新方法,并通过建立的通信仿真模型多音副载波在信道带宽内、信道带宽外跳变等多种条件下进行了仿真。仿真结果表明,基于多音并行体制短波战术数据链通信使用该方法能够有效躲避多音等窄带干扰。     

多变量自回归信号信息融合辨识方法

近年来,为了提高系统模型和状态估计的精度,多传感器数据融合引起了广泛关注。对于带白色公共干扰噪声和有色观测噪声的多传感器多变量自回归（AR）模型,当AR模型参数和噪声方差未知时,提出了一种信息融合多段辨识方法,其中采用多维递推辅助变量（MRIV）方法得到AR模型参数的局部和融合估值器,再用相关方法得到局部和融合噪声方差估值器。这些估值器具有一致性,通过一个信号仿真例子验证了其有效性。     

卫星导航信号频域抗干扰后相关峰建模及对捕获性能影响分析

卫星导航信号容易受到强干扰的影响,频域抗干扰算法是常用的抗干扰算法之一,但该方法实现有效干扰抑制的同时也会导致信号相关峰的畸变,从而造成导航信号接收性能下降.为了分析频域抗干扰算法对导航信号接收性能的影响,建立了抗干扰后信号相关峰畸变模型,并从信噪比损耗、第一旁瓣位置、峰值旁瓣比三个参数对相关峰模型进行深入分析,进一步建立了抗干扰后信号的捕获性能模型,同时对比了不同捕获策略下的捕获概率.理论分析和数值仿真结果表明,频域抗干扰后的相关峰的信噪比下降且出现了旁瓣,畸变参数与干扰参数密切相关,抗干扰滤波器会导致信号捕获性能下降.该研究成果对干扰场景下提高导航信号捕获性能提供了重要的模型支撑.     

一种基于频谱估计的跳频系统干扰检测算法

跳额通信系统由于具有良好的抗干扰和抗衰落性能以及保密性强等优点,已被广泛应用于空间通信系统中.跳频通信系统的干扰检测是空间通信系统信道质量评估的一个关键环节,也是自适应跳频系统的关键核心技术.本文在跳频宽带接收模型基础上,分析对比了多种频谱估计能量检测算法,提出了一种基于AR模型的跳频干扰检测算法,并详细分析了其算法原理和门限设置算法,对多音干扰和部分频带干扰进行检测.经过仿真验证,在无线电静默期和通信过程中该算法都具有很好的检测性能.     

无线瑞利衰落信道的自回归模型研究

在不同多普勒谱下分析自回归(AR)模型参数与系统极点位置、幅度响应关系,采用极点位置累试法对模型参数进行修正,改善了仿真功率谱,在通带衰落方面不满足理论要求的缺陷,提高了模型对瑞利衰落信道功率谱的仿真精度。仿真结果表明,通过累试法调整系统极点分布,可以提高仿真结果与理论值的匹配度,方法简单且精度较高,降低了仿真分析的难度。     

2.4 GHz宽带圆极化微带天线的研究与实现

针对当前已有圆极化微带天线有效带宽窄、尺寸大等缺陷的现状，设计了一种应用于2.4 GHz无线传感网络的宽带圆极化微带天线.采用Ansoft HFSS建立了天线的模型，并对其主要结构参数进行了仿真分析，最终推导出了天线的最优结构参数.最优结构参数下的仿真结果显示，天线的-10 dB阻抗带宽达到了63.5%，3 dB轴比（axial ratio，AR）带宽达到了17.5%.同时，采用矢量网络分析仪对天线实物进行了回波损耗测试，测试结果与仿真结果吻合.最后，将设计的天线加载到CY2420通信节点上进行通信性能的测试，测试结果表明:加载了该宽带圆极化微带天线的节点在150 m处的平均丢包率为0.36%，且天线任意方向下的丢包率基本相同.从测试结果可以看出该天线具有良好的圆极化特性和实用特性.     

时分噪声调频干扰性能分析与验证

时分噪声调频干扰是一种常用的多目标干扰样式,对其性能的分析研究具有重要意义。构建了时分噪声调频干扰信号的产生模型,统计了不同的干扰重复周期、有效调制带宽和占空比下时分噪声调频干扰信号的功率和干扰带宽,分析出这些参数对时分噪声调频干扰信号功率和干扰带宽的影响,指出了关于脉冲信号带宽认识上的误区。对时分多目标干扰模拟设备生成的信号进行了测试,验证了相关结论。     

战术跳频系统智能抗干扰决策

作为一种有效的抗干扰方法,跳频（Frequency hopping, FH）技术已被广泛应用于战术通信系统来提高在强对抗环境下军事通信网络的可靠性。跳频通信网络面临的主要威胁是具有灵敏的频谱侦察和频率捷变能力的跟踪干扰机。为提高战术跳频通信系统在跟踪干扰攻击环境中的抗脆性和吞吐量,本文提出了一种基于双深度Q网络（Double deep Q-network, DDQN）的功率和跳速联合抗干扰决策方法。该算法将战术电台发射机与跟踪干扰机之间的对抗建模为马尔可夫决策过程（Markov decision process, MDP）,其中干扰器通过调整频谱扫描速率提高干扰效能,战术电台终端则将接收状态反馈信息作为算法输入,根据决策网络的输出调整数据传输的发射功率和跳频速率。该算法模型在未知环境状态和干扰参数的情况下,通过与环境的交互学习更新网络参数,逐渐收敛于最佳功率和跳速联合控制策略,以使跳频通信系统的平均吞吐量最大化。仿真结果表明,相比传统的无模型抗干扰方法,本文所提算法在跟踪干扰环境下能够更有效改善跳频系统的抗干扰性能。     

基于分数低阶协方差的AR SαS模型α谱估计

根据自回归(AR) SαS模型的α谱,分析了基于分数低阶矩(FLOM)法估计AR SαS模型参数的不足.提出了一种基于分数低阶协方差(FLOC)的AR SαS模型参数估计方法,并给出了基于FLOC的AR SαS模型α谱方法.分别对AR SαS模型参数的估计、α稳定分布噪声中单一正弦信号的估计和两个正弦信号的分辨进行了仿真.仿真结果表明,基于FLOC的AR SαS模型α谱估计方法对于不同的α值均具有较好的韧性.特别是在α值较小,即α稳定分布噪声概率密度函数(PDF)拖尾比较严重时,本文所提出的基于FLOC的AR SαS模型α谱估计方法,其性能明显优于基于FLOM的AR SαS模型α谱估计方法.     

Interference mitigation in STAP using the two-dimensional Wold decomposition model

We propose a novel parametric approach for modeling, estimation, and detection in space-time adaptive processing (STAP) radar systems. The proposed parametric interference mitigation procedures can be applied even when information in only a single range gate is available, thus achieving high performance gain when the data in the different range gates cannot be assumed stationary. The model is based on the Wold-like decomposition of two-dimensional (2D) random fields. It is first shown that the same parametric model that results from the 2D Wold-like orthogonal decomposition naturally arises as the physical model in the problem of space-time processing of airborne radar data. We exploit this correspondence to derive computationally efficient fully adaptive and partially adaptive detection algorithms. Having estimated the models of the noise and interference components of the field, the estimated parameters are substituted into the parametric expression of the interference-plus-noise covariance matrix. Hence, an estimate of the fully adaptive weight vector is obtained, and a corresponding test is derived. Moreover, we prove that it is sufficient to estimate only the spectral support parameters of each interference component in order to obtain a projection matrix onto the subspace orthogonal to the interference subspace. The resulting partially adaptive detector is simple to implement, as only a very small number of unknown parameters need to be estimated, rather than the field covariance matrix. The performance of the proposed methods is illustrated using numerical examples.     

模拟深海采矿环境下混响的局部平稳性分析

针对深海采矿环境中强混响干扰下目标回波提取困难的问题,提出了海底强混响环境的模拟方法,在此基础上建立了采矿模拟环境中混响干扰的AR模型,并利用建立的AR模型计算了模拟环境中信号的Itakura距离.以建立的AR模型及Itakura距离为基础,建立了海底强混响仿真模型,对不同Itakura距离下的仿真信号及己知Itakura距离的大量实验数据分段预白化处理.仿真和实验数据处理结果表明模拟采矿环境下混响具有局部平稳性.     

常见的功率谱估计方法及其Matlab仿真

功率谱估计是数字信号处理的重要内容之一,又分为经典谱估计和现代谱估计。经典谱估计主要是周期图法及其改进方法,现代谱估计则有参数模型与非参数模型谱估计之分。文中主要介绍了几种常见的功率谱估计方法的原理、特点,并进行了Matlab仿真分析,发现基于AR参数模型的Burg法效果较好。     

Parametric SNR Estimation Based on Auto-Regressive Model in AWGN Channels

Signal-to-noise ratio（SNR）estimation for signal which can be modeled by Auto-regressive（AR）process is studied in this paper.First,the conventional frequency domain method is introduced to estimate the SNR for the received signal in additive white Gauss noise（AWGN）channel.Then a parametric SNR estimation algorithm is proposed by taking advantage of the AR model information of the received signal.The simulation results show that the proposed parametric method has better performance than the conventional frequency doma in method in case of AWGN channel.     

Identification of Autoregressive Systems in Noise Based on a Ramp-Cepstrum Model

In this paper, a new approach for the identification of a minimum-phase autoregressive (AR) system in the presence of a heavy noise is presented. First, a model, valid for both white noise and periodic impulse-train excitations, for the ramp-cepstrum (RC) of the one-sided autocorrelation function of an AR signal is proposed. A residue-based least-squares optimization technique is then employed in conjunction with the RC model to estimate the AR parameters from a noisy output, with a guaranteed system stability. The proposed ramp-cepstral model fitting combines the good features of both the correlation and cepstral domains, and thus provides a more accurate estimate of the parameters in a noisy environment. Extensive simulations are carried out on synthetic AR systems of different orders in the presence of white as well as colored noise. Simulation results demonstrate quite a satisfactory identification performance even for a signal-to-noise ratio of $-$5 dB, a level at which most of the existing methods fail to provide accurate estimation. To illustrate the suitability of the proposed technique in practical applications, a spectral estimation of a human vocal-tract system is carried out using noise-corrupted natural speech signals.      

一种直扩通信系统窄带干扰变换域抑制方法

本文提出一种基于AR模型谱估计和变换域处理相结合的直接序列扩频（DS-SS）通信系统窄带干扰抑制方法。该方法采用AR模型估计信号的频谱,并由此构造频域窄带干扰抑制滤波器。由于谱估计的同时也估计了最小误差功率,这为频域抑制滤波器的门限提供了参考,从而实现了自动门限设置。与时域AR预测型窄带干扰抑制方法相比,频域处理利用FFT实现了并行处理,提高了处理速度。     

灯光干扰对微光成像系统性能的影响

根据灯光干扰源作用微光成像系统的两种途径，建立了灯光干扰辐射传输模型；分析了灯光干扰对微光成像系统响应特性的影响，建立了灯光干扰辐射能量与系统响应特性的量化关系模型；以实测和理论计算数据为基础构建了仿真平台，实现了灯光干扰下微光成像的模拟仿真，并对仿真图像的灰度均值、均方误差和相关函数进行了计算，结果表明微光成像质量与灯光干扰源位置和亮度有直接关系，且灯光干扰源位置的不同会导致灰度相关函数变化趋势的差异；进行了灯光干扰实验，实验结果和模拟结果的成像质量变化趋势基本一致，同时实验结果表明灯光干扰会导致目标与背景成像对比度增大，且随着干扰距离增大到一定距离后，逐渐减小至小于无干扰时。     

Effective Parametric Estimation of Non-Gaussian Autoregressive Moving Average Processes Exhibiting Noise with Impulses

In statistical signal processing, parametric modeling of non-Gaussian processes experiencing noise interference is a very important research topic. Particularly challenging to some researchers is how to estimate signals encountering stochastic noise process exhibiting sharp spikes. The authors propose the use of systems with impulse effect along with the classic autoregressive moving average model as a novel parametric modeling tool to successfully estimate these specific processes. The proficiency of this original system is illustrated in a performance table.

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Parametric Characteristic Analysis for Generalized Frequency Response Functions of Nonlinear Systems

In order to explicitly reveal the relationship between system frequency response functions and model parameters which define system nonlinearities, and consequently unveil a direct connection from model parameters to system frequency response characteristics, a parametric characteristic analysis approach is proposed for Volterra systems described by a nonlinear differential equation (NDE). The parametric characteristics of the generalized frequency response functions (GFRFs) for the NDE model are established, and some important properties are discussed, which can explicitly reveal what model parameters contribute and how these parameters affect the GFRFs. Based on the parametric characteristic analysis, it is demonstrated how the system frequency domain characteristics are related to the system time domain model parameters and how the output frequency response function can now be determined explicitly with a detailed polynomial structure. These new results provide a significant and novel insight into the analysis and design of nonlinear systems in the frequency domain. Several examples are included to illustrate the results.