全相位FFT在导弹自动驾驶仪测试中的应用

自动驾驶仪是导弹的姿态控制系统,其性能好坏决定了导弹飞行的稳定性。为了精确测量其性能,对导弹自动驾驶仪测试原理和方法进行了研究,进行了正弦信号测量方法的分析,提出了基于全相位FFT数据处理方法,并结合时移相位差校正方法实现正弦形式信号的频率、幅度和相位等参数的精确测量。利用LabVIEW图形化编程软件设计了算法软件,通过仿真试验考察了该算法在无噪、低噪和大噪环境下信号测量精度,结果表明全相位FFT方法在信号频谱测量方面具有精度高、抗噪能力强等优点,满足自动驾驶仪的测试需求。     

基于自适应陷波的非均匀采样信号处理系统

非均匀采样可以突破奈奎斯特采样定理的限制,检测出超过采样频率的信号频率,但非均匀采样引起信号的频谱噪声,使得非均匀采样的小信号检测难于实现。研制了一种实时非均匀采样信号处理系统,采用自适应陷波方法计算非均匀采样信号的频率,逐步滤除幅度较大的信号,从而检测出小幅度信号。详细说明了自适应陷波方法的原理和实现方法,并介绍了基于数字信号处理器(DSP)的非均匀采样信号处理系统。     

自适应线性预测卡尔曼滤波压缩感知算法

针对压缩感知中时变稀疏信号的重建问题,提出一种基于自适应线性预测的卡尔曼滤波恢复算法.该算法采用滑动窗口对信号进行观测,基于前后窗信号之间的相关性并利用自适应线性预测方法,建立前后窗口信号的状态转移方程,与修正后的观测方程共同构成系统状态空间模型.在信号重构时,采用贪婪算法确定信号支撑集、降阶卡尔曼滤波算法迭代得到其精确解.对调频信号、调幅信号、WiFi射频信号和语音采样信号进行仿真实验验证,仿真结果表明,所提出算法在不显著增加计算复杂度的情况下,重建精度比改进前的循环平移模型算法提高约5%,且远高于其他同类算法;同时在不同的噪声环境下,重建后的信号比原信号信噪比提高$1\sim 8$dB,表明算法具有较强的抗噪声性能.     

基于检测的脉冲噪声滤除方法

为了将柴油机瞬时转速信号中的脉冲噪声滤除,使用了基于检测的脉冲噪声滤除方法。在已有的检测方法基础上,计算被检测点与中值之差将已有的检测方法可能漏掉的脉冲噪声检测出来,改进了脉冲噪声的检测方法来保护信号的细节信息。而且此方法对脉冲噪声干扰较为严重的信号特别有效。使用本方法对含有大量脉冲噪声的柴油机瞬时转速信号的滤波结果表明,改进的脉冲噪声检测法能够更准确地检测出脉冲噪声,更好地保护信号中的细节信息。     

基于混合型MUSIC算法对相干信源DOA估计

在实际通信环境中,由于传播环境的复杂性使空间中存在大量的相干信号,从而导致信源协方差矩阵的秩亏缺。为使得矩阵的秩恢复到等于信号源数并解决相干信源波达方向（direction of arrival ,DOA）估计问题,提出了一种混合型MUSIC算法。该算法通过前后向空间平滑技术对天线阵列进行预处理,并将得到的新协方差矢量矩阵应用于改进的IMUSIC算法进行信号数据处理分析,得到相干信号的DOA角度估计。仿真结果表明,在信噪比低的情况下,信号间隔很小且存在相关信号时,混合型MUSIC算法能准确地估计出信源的DOA,验证了该算法的高分辨率和高性能。     

脑电情感信号正确提取仿真

脑电信号是一种微伏级信号,从头皮上采集的脑电信号包含眼电信号、心电信号以及各种环境噪音。针对情感识别如何有效处理脑电信号的问题,本文首先对实验采集的脑电信号应用小波分析和独立分量分析进行预处理去除干扰;其次为了有效地提取脑电特征,应用幅值直方图、标准差在时域上定性地找出2种情感的脑电差异;最后应用功率谱对2种情感脑电的γ波节律进行谱分析。仿真实验结果表明,将脑电信号的γ波节律用于情感识别是可行的。     

基于连续混沌系统的低旁瓣雷达波形设计*

线性调频波(LFM)为了降低旁瓣而进行加窗处理,这导致信噪比损失;目前的非线性调频(NLFM)信号中有很多是离散混沌调频信号,自相关旁瓣也较高。针对这一问题,提出利用初始值优化过后的连续混沌系统的变量进行调频,可获得自相函数旁瓣低至-3953 dB的调频信号。仿真结果表明,相比于加窗后的线性调频信号、离散混沌调频信号和离散混沌调相信号,连续混沌调频信号具有更好的旁瓣抑制效果,并且保持了混沌信号固有的抗干扰性能（ECCM）。     

周期谱相关在连续波信号时差提取中的应用

以周期谱相关理论为基础，分析了信号的二阶周期平稳特性及其在连续波信号时差提取中的应用，介绍了四种时差提取的算法模型，这些算法比传统的互相关方法具有更强的抑制噪声和干扰的能力，适用于低信噪比的情况。为了验证这些模型所具有的强噪声抑制能力，同时为工程实现选择最佳的算法模型，在MATLAB中对所给的几种模型进行了计算机仿真实验，并且作了定性和定量的分析比较，结果显示周期谱相关时差提取算法具有极强的抑制噪声的能力，为工程实现探索了可行的途径。     

基于相关检测的光纤倏逝波生物传感器研究

构建了一种以光纤和光纤束作为系统信号传输通道的光纤倏逝波生物传感器系统,根据光纤探针的倏逝波理论和模式匹配理论对光纤探针进行了设计与制作,然后应用直接结合分析的方法对该传感器系统的探测性能进行了实验检测.实验针对光纤探针置于空气中和蒸馏水中两种情况进行,同时将系统检测到的信号通过数据采集卡采集到计算机中,并进行相关检测分析.对光纤探针置于空气中和蒸馏水中两种情况,系统分别获得了16.7和20.5的探测信噪比.实验结果表明,该相关检测方法能很好的区分系统的噪声信号和所探测的荧光信号,整个系统具有良好的可行性.     

航空发动机转子振动信号的早期故障分析

由于飞机涡扇发动机是旋转动力机械,早期故障振动信号具有周期性的特点,可将多重自相关用于检测微弱周期性振动信号;针对某型航空发动机空中停车前的早期故障振动信号,通过多重自相关运算,再对信号进行频谱分析,就能准确检测出淹没于强噪声中的微弱周期性信号的频率信息和幅值信息;多重自相关检测方法能够从复杂的航空发动机振动信号中提取出早期的微弱故障信号,从而对早期故障进行有效的分析。     

Time–frequency analysis of signals using support adaptive Hermite–Gaussian expansions

Since Hermite–Gaussian (HG) functions provide an orthonormal basis with the most compact time–frequency supports (TFSs), they are ideally suited for time–frequency component analysis of finite energy signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG function expansion provides optimal representation by using the fewest number of basis functions. However, for signal components whose TFS has a non-circular shape away from the origin, straight forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot provide reliable estimates to the individual signal components. To alleviate these problems, by using expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which identifies and transforms TFSs of individual signal components to circular regions centered around the origin so that reliable signal estimates for the signal components can be obtained. The HG expansion order for each signal component is determined by using a robust estimation technique. Then, the estimated components are post-processed to transform their TFSs back to their original positions. The proposed technique can be used to analyze signals with overlapping components as long as the overlapped supports of the components have an area smaller than the effective support of a Gaussian atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap region is larger than this threshold, the components cannot be uniquely identified. Obtained results on the synthetic and real signals demonstrate the effectiveness for the proposed time–frequency analysis technique under severe noise cases.     

基于FRFT的非线性调频信号双特征检测方法

强噪声中的未知非线性调频信号检测一直是实际检测领域中的一个难点问题.非线性调频信号的相位历史是关于时间的未知平滑函数,并且该函数不能或者很难用少量参数来建模.由于相位模型的缺失,我们提出一种基于接连分数阶Fourier变换和双特征检测的非参数化检测方法.检测方案包含3部分:首先,计算一个周期内接连角度的分数阶Fourier变换,将这些不同角度的变换结果构造成二维图像;然后,利用硬阈值处理获得二值图像,并利用多重中值滤波来去除该二值图像中的孤立噪声点,从而获得精炼的二值图像;最后,根据精炼图像提取2个互补的检测统计量,并通过双特征检测器判决目标是否存在.本文针对3类不同阶的多项式相位信号以及正弦相位信号的对比实验结果表明,提出的方法是有效且鲁棒的,并且获得了满意的检测性能.     

Double-characters detection of nonlinear frequency modulated signals based on FRFT

In many practical applications,signals to be detected are unknown nonlinear frequency modulated (FM)and are corrupted by strong noise.The phase histories of the nonlinear FM signals are assumed to be unknown smooth functions of time,which are usually poorly modeled or cannot be modeled at all by a small number of parameters.Because of the lack of phase model,a nonparametric detection method is proposed based on successive fractional Fourier transform and double-characters detection.The detection process goe...     

DCSK的反相叠加相关解调方案

基于DCSK的类重传特点提出了反相叠加相关解调方案。第一种方案将同一码元前后半段的信号反相叠加后与前半个码元信号进行相关运算,其结果作为判决量;第二种方案将第一种解调方案的判决量与前后半个码元信号直接相关运算的结果进行反相叠加后再作为判决量。只要同一码元的前后半段内的噪声具有正相关性,与将前后半个码元信号直接相关运算的结果作为判决量的现有解调方案相比,所提方案均可有效抑制噪声的影响。而第二种方案在噪声相关系数小于0.5的条件下进一步提高了系统的噪声容限。     

声学参量换能器阵及其测试系统设计

根据声学参量阵原理,设计了一个3×3的九元矩形换能器阵及其测试系统,并在空气进行验证性实验;系统包括正弦信号产生电路、信号控制电路、功率放大电路以及回波信号接收电路等,辅以LabVIEW平台,实现正弦信号和脉冲控制信号的产生,发射信号时间长度控制以及功率放大,并最终驱动换能器阵;实验表明,当有85kHz和90kHz的正弦信号产生时,系统各部分均能正常工作,能够听到天花板处有声响,且回波信号中含有5kHz的频率成分,即证明换能器阵及其系统的设计均是可行的.     

空间有色噪声背景下多目标检测性能研究

重点对一些典型的多目标检测方法在空间有色噪声背景下的检测性能进行分析,并利用统一的仿真模型对它们的检测能力进行比较。每种方法都运用了两个分离的线列阵,来接收多个从不同方向入射的平面波和噪声。仿真采用ARMA模型来产生空间有色噪声,使噪声功率谱相似于实际工程应用中的噪声特性。并且分别研究了各种方法的优缺点,对比了这些方法在有色噪声背景下检测信号源数目的效果。     

Detection of nonlinear FM signals via forward–backward cost-reference particle filter

Many applications require the detection of unknown nonlinear frequency modulated (FM) signals in noise. In this paper, a nonlinear FM signal in one time interval is approximated by linear FM (LFM) segments in successive subintervals. Each LFM segment is parameterized by a 2-dimensional (2D) state vector and its evolution from a subinterval to the next one is modeled as a dynamic system of unknown statistics with linear state transition equations and nonlinear measurement equations. A forward–backward cost-reference particle filter (FB-CRPF) is proposed to estimate the state sequence. From the estimated state sequence, the generalized likelihood ratio test (GLRT) statistic and the total variation (TV) statistic are computed for signal detection. In the 2D feature plane of the GLRT versus TV, the decision region of the null hypothesis at a given false alarm rate is determined by the 2D convexhull learning algorithm from noise-only training samples. Two kinds of simulated signals are used to test the proposed detector and results show that the proposed detector attains better performance than the two existing detectors.     

Decorrelation of quantized signals induced by filtering and requantization

In Very Long Baseline Interferometry, signals from far radio sources are simultaneously recorded at different antennas, with the purpose of investigating their physical properties. The recorded signals are generally modeled as realizations of Gaussian processes, whose power is dominated by the system noise at the receiving antennas. The actual signal coming from the radio source can be detected only after cross-correlation of the various data-streams. The signals received at each antenna are digitized after low noise amplification and frequency down-conversion, in order to allow subsequent digital post-processing. The applied quantization is coarse, 1 or 2 bits being generally associated to the signal amplitude. In modern applications the sampling is typically performed at a high rate, and subchannels are then generated by filtering, followed by decimation and requantization of the signal streams. The redigitized streams are then cross-correlated to extract the physical observables. While the classical effect of quantization has widely been studied in the past, the decorrelation induced by the filtering and requantization process is still characterized experimentally, mainly due to its inherent mathematical complexity. In the present work we analyze the above problem, and provide algorithms and analytical formulas aimed at predicting the induced decorrelation for a wide class of quantization schemes, with the unique assumption of weakly correlated signals, typically fulfilled in VLBI and radio astronomy applications.     

基于级联双稳随机共振的微弱信号检测方法研究

微弱信号采集、检测与提取是航天测控领域研究的热点之一,学者们不断探索与研究微弱信号检测的新理论、新方法,以期能更快速、更准确地从大噪声背景中检测出微弱信号;文章介绍了随机共振的基本原理,分析了基于随机共振的微弱信号检测和故障诊断的工程实例,进一步指出了随机共振技术在微弱信号的增强放大和检测中的独特优势,为微弱信号的分析和噪声控制提供了一个新的处理思路。     

基于屏蔽驱动的工频干扰抑制技术研究

采集心电、肌电和脑电等生理电信号时,总是存在工频干扰等环境噪声,且噪声的幅度通常远大于生理电信号本身的幅度,给信号的分析和处理带来了很大的困难。目前常用的方法是利用软件算法对采集到的信号进行滤波处理,这种方法虽然能在一定程度上降低工频干扰的影响,但同时会造成目标信号的衰减和畸变。针对此问题,文章设计了一种独特的硬件屏蔽驱动技术,实现在模拟前端最大限度地抑制原始信号中的工频干扰噪声。实验结果表明,生理电极引入屏蔽层驱动后,心电和肌电采集过程中的工频干扰可以得到明显的抑制;通过对屏蔽层接入不同的驱动信号的比较发现,利用各电极自身的信号对输入端进行屏蔽的效果最好,对工频干扰的抑制幅度高达35 dB。文章提出的屏蔽驱动硬件电路设计技术,可广泛用于各种生理电信号的采集,实现在源头上抑制工频干扰的影响,从而在根本上提高生理电信号的信噪比。