一种复域压缩感知目标方位估计方法

针对频域压缩感知目标方位估计方法的性能退化问题,本文通过对线列阵接收信号进行复解析变换,按预估方位在复域对各阵元信号进行时延补偿、相关和累积处理,构建复域感知矩阵和测量值,采用复域压缩感知方法实现空间谱合成和目标方位估计.数值仿真和实测数据处理结果表明,在同一检测概率下,相比频域压缩感知方法,该方法对输入信噪比的最低要求得到近10lgMdB(M为通道数)的降低,提升了对弱目标的检测能力.     

一种抑制符号速率估计背景色噪声的非线性滤波算法

数字调制信号符号速率估计的依据是循环平稳理论,由于信号的符号速率就是其基本循环频率,因此可以通过提取信号非线性变换(例如循环自相关函数)的循环频率获知符号速率.但是,非线性变换不仅能产生对应于符号速率的正弦分量及其各次谐波,还会将信号自身转变成不利于谱线提取的连续有色噪声(其能量主要分布在低频部分).当观察数据长度有限时,自噪声对谱线提取的影响尤其明显.本文深入研究了数字调制信号非线性变换的频域特征,充分利用离散频率分量不同于连续噪声而在其邻域内突起的显著特点,提出一种能够有效抑制背景色噪声的非线性滤波算法.文中详尽的Monte Carlo仿真验证了算法的有效性.     

基于变换域的OFDM信道估计算法分析

首先对目前OFDM系统中常用的信道估计算法进行了介绍,其次对离散采样信道做出说明,并以此为基础,着重分析传统的基于离散傅里叶变换的信道估计滤波算法,即对接收导频LS(最小二乘)算法估计,通过IDFT转换到时域滤波后,由DFT转换到频域获取信道系数.最后给出了一种基于变换域滤波估计算法.由理论分析和工程实践平台中的仿真验证得出,基于提出的滤波矩阵,此DFT变换域算法性能较传统的DFT滤波方法有较大提升.     

基于小波变换的压电陶瓷动态迟滞特性建模

为了解决压电陶瓷迟滞系统的建模问题,提出了一种新迟滞建模方法--在小波域(时频域)对迟滞特性进行建模.该模型通过一维离散小波变换,实现输入信号从时域到小波域的转换;对转换后得到的小波域元素利用RBF神经网络进行逼近;再通过一维离散小波逆变换,使逼近后的小波域元素重新回到时域,整个变换过程间接地实现了对时域迟滞特性的描述.通过仿真表明,该建模方法是有效的,并具有较高的精度.     

基于时频域联合滤波的中段群目标信号分离

该文针对中段群目标回波信号分离问题,提出了基于时频域联合滤波的信号分离算法.该算法首先基于自相关原理估计出某一目标的微动周期;然后根据所估计周期对回波进行分段,利用S变换提取各段信号在时频域的强能量区域,各段信号强能量区域的交集即对应了某一目标在时频面上的支撑域.基于该支撑域对回波进行时频域联合滤波即可得到某一目标的回波信号,实现回波信号的分离.仿真结果验证了该文所提算法的有效性.      

DCT变换域滤波码捕获方法

 针对较小时频不确定度的卫星信号捕获,提出了一种结合离散余弦变换(DCT)的码捕获算法.首先对信号进行部分匹配滤波(PMF),然后对各个码相位对应的PMF输出矢量进行DCT变换域滤波及信号重构,最后对信号进行基于能量的检测.由于PMF输出信号和噪声时变特性不同,滤波重构后信号能量几乎无损,而噪声能量得到了明显降低,从而提高了相同虚警概率下的捕获概率.理论分析和仿真结果表明本文检测算法可以有效提升检测概率,并且具有较低的复杂度.     

一种中频抗窄带干扰自适应滤波方法

根据实际的技术需求,针对信号传输带内可能存在领近频率的干扰,采用自适应滤波技术,抑制带内的窄带干扰。该方法是一种数字滤波方法,对中频时域信号进行A/D采样,通过FFT(快速傅里叶变换),变换为相应的离散频域信号,应用抗窄带干扰的自适应滤波算法,消除其频带内的窄带干扰,而同时基本保留有效信号,通过IFFT(反变换)将滤波后的频域信号转换为时域信号,并D/A输出。     

一种基于频域的QPSK窄带干扰抑制算法

在QPSK系统中,当存在单频干扰或者窄带干扰时,会对系统的解调性能产生不利影响,导致误码率升高。提出了一种在变换域基于DFT变换的窄带干扰抑制技术,该方法将输入信号经FFT变换到频域,根据动态调整的门限找出认为存在干扰的频率谱线,将其置零,达到抑制窄带干扰的目的。仿真结果表明该抑制算法可以较好地抑制窄带干扰,同时又能对有用信号最大限度地保留,具有较高的工程使用价值。     

一种用于数字音频广播的自适应水印算法

为了解决数字音频广播版权认证,提出一种基于频域和MPEG压缩域的自适应盲检测水印算法。通过量化处理数字音频信号的离散傅里叶变换系数的幅度和MPEG音频流的比例因子的方法嵌入水印。仿真表明,该算法对数字音频信号的MP2压缩、噪声叠加以及滤波等操作具有很强的鲁棒性,可适用于实际数字音频广播系统。     

频谱互质重排亚奈奎斯特率采样方法

本文提出了一种基于频谱互质重排的超低速率信号采样方法.该方法将稀疏傅里叶变换从离散时间域拓展到连续时间域,首先通过互质结构傅里叶展开采样对频域稀疏信号的频谱分量进行重排和压缩,然后通过基于中国余数定理的亚线性算法对信号进行了重构.实验结果表明,本文所提的采样方法可进一步降低频域稀疏信号的采样速率.     

Combining the discrete wavelet transform and mixed-domain filtering

A novel filtering method is proposed that combines the discrete orthogonal wavelet transform (DWT) with the mixed-domain (mixed-D) filtering method. The method uses the DWT to pre- and postprocess those dimensions of the signal that are transformed to the discrete-frequency domain by mixed-D filtering. Using the DWT in this manner provides a controlled mechanism to partition the spectrum of the input signal into subband signals, which then may be selectively filtered during the linear difference equation (LDE) step of the mixed-D algorithm. It is shown that, when the DWT is computed using filters with ideal high- and lowpass frequency responses, the LDE filters used in the mixed-D filtering stage are unchanged by the introduction of the DWT (although the frequency tuple associated with each LDE filter is altered). This indicates that the mixed-D filtering scheme can be easily used in subband coding systems. Results are given for the filtering of a three-dimensional (3-D) linear trajectory signal, representing a common application in video processing.     

离散傅里叶变换误差分析与参数设置

离散傅里叶变换( Discrete Fourier Transform,DFT) 是数字信号处理教学的重点和难点,其参数设置的正确与否直接影响信号频谱分析的准确性。本文对连续与离散周期信号、非周期信号分别进行DFT运算,从時域和频域角度分析实际频谱与理想频谱之间的误差。通过MATLAB仿真,加深学生对DFT的理解,引导学生正确设置DFT参数。     

Adaptive blind calibration of timing offset and gain mismatch for two-channel time-interleaved ADCs

In this paper, we describe a blind calibration method for gain and timing mismatches in a two-channel time-interleaved low-pass analog-to-digital converters (ADC). The method requires that the input signal should be slightly oversampled. This ensures that there exists a frequency band around the zero frequency where the Fourier transforms of the ADC subchannels are alias free. Low-pass filtering the ADC subchannels to this alias-free band reduces the blind calibration problem to a conventional gain and time delay estimation problem for an unknown signal in noise. An adaptive filtering structure with three fixed FIR filters and two adaptive gain and delay parameters is employed to achieve the calibration. A convergence analysis is presented for the blind calibration technique. Numerical simulations for a bandlimited white noise input and for inputs containing several sinusoidal components demonstrate the effectiveness of the proposed method.     

A frequency domain model for `filtered' LMS algorithms-stabilityanalysis, design, and elimination of the training mode

A frequency domain model of the filtered LMS algorithm is presented for analyzing the behavior of the weights during adaptation. In particular, expressions for stable operation of the algorithm are derived as a function of the algorithm step size, the input signal power, and the transfer functions of the linear filters. The expressions show that algorithm stability can be achieved over a frequency band of interest by inserting an appropriately chosen delay in the reference input to the LMS algorithm weight update equation. This result implies that it is not necessary to use a training mode to estimate the loop transfer functions before or during adaptation if the input is limited to a band of frequencies. It is only necessary to know the approximate delay introduced by the transfer functions in the band. The single delay parameter can be estimated much more easily than the entire transfer function. Simulations of the time domain algorithm are presented to support the theoretical predictions of the frequency domain model     

改进的EEMD算法在时域航空电磁信号降噪中的研究

常规降噪方法在应用于时域航空电磁信号降噪时需根据噪声情况人为进行参数调整,自适应性较差。总体经验模态分解（EEMD）算法对非线性、非平稳信号处理具有良好的自适应特性,传统的EEMD算法进行噪声抑制是将高频本征模态分量滤除,将低频分量重构得到降噪信号,这种方法易失掉高频分量中的有效信号。本文提出一种改进的EEMD降噪算法,应用于时域航空电磁信号的处理。该方法结合时域航空电磁信号的衰减特性,将信号EEMD分解后得到本征模态分量,其中包含信号和噪声,经Savitzky Golay平滑滤波,再将高频部分进行阈值去噪,最后得到干净的本征模态分量进行重构。实验结果表明在输入信号信噪比小于等于15 dB的情况下,输出信噪比能够提高12 dB左右,在抑制噪声的同时保留了更多有效信息。      

Real-time heart rate variability extraction using the Kaiser window

A new method for real-time heart rate variability (HRV) detection from the R-wave signal, based on the integral pulse frequency modulation (IPFM) model and its similarity to pulse position modulation, is presented. The proposed method exerts lowpass filtering with a Kaiser window. It can also be used for off-line HRV analysis in both the time and frequency domains. Real-time bandpass filtering as a new HRV investigation method and as a by-product of the proposed algorithm is also introduced. Furthermore, the discrete time domain version of the French-Holden algorithm is developed, and it is thoroughly proved that lowpass filtering is an ideal method for detection of HRV     

Synchronization of MIMO OFDM systems by perfect complete generalized complementary orthogonal loosely synchronous code groups

This article proposes a time/frequency synchronization algorithm in the multiple input multiple output (MIMO) systems, in which the perfect complete generalized complementary orthogonal loosely synchronous code groups are used as the synchronization sequence. The synchronization algorithm is divided into four stages: 1) synchronization in time domain by signal autocorrelation; 2) synchronization in frequency domain by fast Fourier transform (FFT); 3) multipath dissociation using coherent detection and fine time synchronization; 4) fine frequency offset estimation by phase rotation. As per the perfect complete generalized complementary orthogonal loosely synchronous code groups, the cross-correlation and out-of-phase auto-correlation for any relative shift between any two codes is always zero. This ideal property makes the time/frequency synchronization algorithm simple and efficient. The simulation results show that even in the multipath fast fading channel with low signal noise ratio (SNR), the MIMO system can get synchronized both in the time domain and frequency domain with high stability and reliability.     

Constrained notch filtering of nonuniformly spaced samples forenhancement of an arbitrary signal corrupted by a strong FM interference

The proposed method is based on exploring the concept of constrained notch filtering (CNF) as applied to any given arbitrary signal with time varying parameters. First, it is shown that any signal with a constant envelope such as FM may be transformed to a discrete sinusoidal one by applying a nonuniform sampling strategy. Second, a signal buried under a strong FM interference is retrieved by applying CNF in the transformed time domain. The main assumption made is that there exists an auxiliary input which provides information about the instantaneous frequency of the interference     

基于二进小波变换自适应Kalman滤波反褶积

本文提出了基于二进小波变换自适应Kalman滤波反褶积(AKFD)新方法.它抛弃了传统预测反褶积对信号平稳性的假设,克服了提高分辨率反而明显降低信噪比的矛盾,其较好地压缩反射波形,但噪声并没有明显提高,所以具有很好的抗噪性能.在小波域进行的AKFD压制假反射比在时间域AKFD好,此外,该方法具有对信号分频进行AKFD的特性,增强了Kalman滤波的自适应性,所以在小波域下的分辨率明显比在时域内高.同时,该方法克服了在时域内进行的AKFD抬升低频成份的缺陷.经大量的模型及实际资料处理表明该方法具有明显的效果.