基于双谱理论的雷达辐射源指纹特征提取

针对雷达辐射源指纹特征提取困难的问题,提出了基于双谱切片法和双谱围线积分法,提取辐射源指纹特征的方法。介绍了二相编码信号的无意调制,仿真了理想情况和含有相位噪声的二相编码信号的时域波形。阐述了含相位噪声雷达信号的双谱估计,仿真了含相位噪声的二相编码信号的双谱图及等高图。最后利用双谱切片法和双谱围线积分法,提取了辐射源的指纹特征,通过计算机仿真实验验证了该方法的可实施性。     

高阶累积量在红外热像损伤检测信号分析中的应用

针对常规红外检测信号的脉冲相位法没有考虑测量噪音,简单的Fourier变换对数据信息的挖掘不足、利用率不高的问题,提出在处理中引入非高斯特性分析,应用三阶累积量和双谱估计对信号进行分析,提取发生损伤时信号的特性信息,得到分辨完好区域与损伤区域的特征判据。给出信号分析中三阶累积量和双谱的定义,分析其幅值和相位的对角切片谱在提取损伤特征方面的优势,最后对比分析两者在实际检测中的效果。结果表明:利用该方法具有良好的抗高斯和非高斯对称分布型噪音的优点,且运算量小、计算速度快。同时,弥补了传统脉冲相位法中幅值谱不宜使用的缺点。     

超宽带雷达信号处理技术和实验研究

针对探测空中目标的超宽带(UWB)/冲激雷达,本文讨论了UWB/冲激雷达的信号处理技术,主要是信号检测和目标特性分析,首先讨论了目标检测技术,提出了用小波变换和高阶谱估计技术在变换域内进行检测的算法;其次讨论了目标特性分析技术,采用了高阶谱估计,提出了一种时域双谱估计算法,它可精确估计复杂形体目标的局部散射中心的分布.最后,结合作者等人研制的冲激雷达实验系统,对上述信号处理方法进行了实验研究,验证了上述信号处理方法的有效性。     

超宽带信号时延估计算法研究

研究定位中的超宽带(Ultra Wide-band,简称UWB)信号时延估计问题,给出UWB信号的模型,在研究传统的相关法、双谱法及MUSIC算法的基础上,提出一种改进的MUSIC算法.通过仿真实验,给出估计性能曲线,和传统的相关法、双谱法及MUSIC算法相比,仿真结果表明改进的MUSIC算法具有更好的估计性能.     

Bispectrum Reconstruction Method of UWB Echoes

Estimation of transient signal in additive noise is very important in radar object detection and recognition. This paper presents a new method for transient signal reconstruction based on bispectrum estimation techniques. The third-order cumulants of the received noisy ultra-wide band echoes are acquired first and an ARMA model is then fitted. The bispectrum of the output signal of the ARMA model will be used to reconstruct the transient signal. Simulation results show that the effect is very good even in lower signal-to-noise (SNR) situation.     

Ultrawide band radar signal detection, estimation and experiment

With reference to the air target detection of ultra-wide band (UWB)/impulse radar(IR), the transient signal processing techniques was discussed. In weak UWB signal detection, the wavelet transforms and high order spectrum estimation techniques were preferred. In target characteristic analysis, a time domain bispectrum estimation algorithm was used to analyze the target impulse response, which could estimate accurately local scattering distribution of complex target. A free field IR experimental system installed in an anechoic chamber was used. With this system, experiments to several target models were made. The results of these experiments verified the signal processing method efficiency.     

The bispectrum of sampled data. II. Monte-Carlo simulations ofdetection and estimation of the sampling jitter

For pt.I see IEEE Trans. Acoust. Speech, Signal Process., vol.41, no.1, p.296-312 (1993). The effect of jitter in sampling on the spectrum and bispectrum of the sampled data has been considered previously. Methods of detecting the presence of jitter in a uniform sampling process and of estimating its variance based on a test statistic calculated from the bispectrum estimates have been proposed. The present authors demonstrate, by means of Monte-Carlo simulations, how these results can be applied in an actual case. For this purpose, samples taken from a stationary band-limited process in sampling times given by a random jitter process are generated by computer. The authors then apply the jitter detection and estimation methods that have been developed in previous work and study how their performance depends on signal duration and on jitter variance. They examine the actual simulation results concerning detection probability, estimation bias, and estimation variance in comparison with the theoretical results. This comparison indicates that the bispectrum is a domain where jitter detection and estimation with high performance can be achieved, provided that a signal with sufficiently long duration and high skewness is available     

双谱估计及其在心音信号分析中的应用

应用高阶统计量技术对心音信号进行了AR模型双谱估计，分析结果表明，与传统的功率谱分析方法相比较，双谱分析方法可以提供更多更客观的信息，是分析和处理心音信号的实用而有效的工具。     

Performance Study of Adaptive Filtering in Bispectrum Signal Reconstruction

The problem of reconstructing a signal waveform when the observed realizations are corrupted by intensive noise and random shifts is considered in this paper. Several ways of performing bispectrum filtering are proposed and investigated. First, it is shown that the signal reconstruction is more efficient if one applies smoothing to the recovered real and imaginary parts of the Fourier spectrum separately instead of filtering the magnitude and phase spectra recovered from a bispectrum estimate. Second, several nonadaptive filters are studied, and it is demonstrated that the proper choice of the filter type and its parameters is critical. Some adaptive filtering techniques based on the Z-parameter and on local polynomial approximation (LPA)-intersection of confidence intervals (ICI) are discussed. The performances of nonadaptive and adaptive filtering techniques in the bispectrum-based signal reconstruction are studied using the mean-squared error as the criterion. It is shown that the use of LPA-ICI and other adaptive filters provides improvement of signal reconstruction in comparison to the conventional bispectrum method and the combined bispectrum filtering methods proposed earlier for nonadaptive filters. The benefits achieved are mainly observed for low (smaller than unity) signal-to-noise ratios.     

A NEW METHOD OF BISPECTRAL ESTIMATION FOR TRANSIENT SIGNAL

Estimation of model parameter for transient signal is very important in many aspects. This paper presents a new Markov ARMA model Q-slice estimation algorithm for transient signal based on bispectrum. Simulation results show that this new method has some special features, such as higher estimation precision, lower amount of calculation, higher fitting effect even in lower signal-to-noise ratio (SNR) situation.     

瞬态信号双谱估计的一种新方法

瞬态信号的模型参数估计在许多领域占据相当重要的地位。本文利用高阶矩的性质,提出一种Q-slice马尔可夫ARMA模型双谱估计算法。仿真结果表明,该方法估计精度高,计算量小,即使在低信噪比的情况下,对瞬态信号也有较好的拟合效果.     

基于分数低阶统计量的双谱及其估计方法

稳定分布可更好地描述实际中所遇到的具有显著脉冲特性的随机噪声.为了更好地抑制信号背景中的非高斯噪声,本文提出了基于分数低阶的双谱定义,并给出在分数低阶有色噪声背景下双谱非参数和参数模型的估计方法.仿真结果表明,同传统的双谱估计相比较,非参数法分数低阶双谱估计能有效的识别信号,保留了信号的幅度和相位信息,但存在较大的估计方差.基于AR模型的分数低阶双谱估计具有最大的谱平坦度,能够有效地抑制噪声,具有良好的韧性.     

单目标一维成像及特征提取方法性能分析

为后续目标识别作准备,针对采集并且存储之后的飞机目标雷达回波信号,完成后端雷达信号处理.首先,基于MATLAB提取波门内采样的回波信号并且实现目标的一维成像.然后,采用传统的傅里叶变换模值、双谱奇异值分解法对目标的一维距离像进行特征提取.为了获得较为稳定的目标特征,针对以上两种方法,主要从特征相似性的测量角度分析单一目标特征提取方法的性能优劣.最后通过实验,从相关系数概率密度分布情况得出双谱奇异值分解特征提取法性能较佳,所得到的目标特征较稳定.     

Nonparametric bispectrum-based time-delay estimators for multiplesensor data

When estimating time-delays of signals from sensor outputs, the bispectrum has advantages when the signal has a skewed distribution. The authors provide nonparametric estimation approaches which exploit the two-dimensional nature of the bispectrum. When dealing with observations from more than a pair of sensors, the new algorithms have computational advantages and, in some cases, show improved performance compared to the conventional nonparametric, bispectrum-based time-delay estimation approach     

On computing and implementing the running bispectra

The paper extends recursion to bispectrum estimation problems and presents the systolic array implementation of the recursive higher order spectrum in which the bispectrum estimate is updated every data sample. Forward and reverse sequence running Fourier transforms are first systolically realized. The results are then used to drive a second systolic array, whose outputs represent the FT of the data third-order moment. The proposed systolic arrays have no global communications with a number of processing elements independent of the size of the employed 2D lag window     

Detection of quadratic phase coupling from human EEG signals using higher order statistics and spectra

Interactions among neural signals in different frequency components have become a focus of strong interest in biomedical signal processing. The bispectrum is a method to detect the presence of quadratic phase coupling (QPC) between different frequency bands in a signal. The traditional way to quantify phase coupling is by means of the bicoherence index (BCI), which is essentially a normalized bispectrum. The main disadvantage of the BCI is that the determination of significant QPC becomes compromised with noise. To mitigate this problem, a statistical approach that combines the bispectrum with an improved surrogate data method to determine the statistical significance of the phase coupling is introduced. The method was first tested on two simulation examples. It was then applied to the human EEG signal that has been recorded from the scalp using international 10–20 electrodes system. The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occurring in the observed EEG.     

Matrix-based higher order spectral analysis for three-wave couplingprocesses

Composite linear and quadratic systems produce three-wave coupling when stimulated by random phase input sinusoids. Due to the nonlinearity of the system, the output frequencies are arithmetically related to the input. Using third-order cumulant statistics and their associated bispectrum, techniques are devised based on phase-insensitive matrix structures for detection and frequency estimation of coupling frequencies. The separation of the third-order cumulant series into symmetric and skew-symmetric portions allows one to exploit their characteristic eigendecompositions for frequency estimation. After symmetrization, biphases can be easily extracted as coefficients of the cumulant sequence. Using a generalized eigenvector representation, one can relate symmetric and skew-symmetric bases by a subspace rotation algorithm. Biphases can be estimated directly from generalized eigenvalues of the matrix pencil formed by symmetric and skew-symmetric matrices. The dimensionality of the matrices can be reduced through the use of cumulant projections that yield a slice of the bispectrum. The Radon transform procedure is related to bispectral processing through an isotropic radial-slice Volterra filter. The compact third-order Kronecker product matrix formulation and algorithms for coupling frequency estimation can also be converted for use in biphase estimation. Simulations showing the performance of the above procedures are also presented for both synthetic and biomedical time series     

Blind reconstruction of non-minimum-phase systems from 1-D obliqueslices of bispectrum

The author considers the problem of identifying a non-minimum-phase signal from one-dimensional slices of its output bispectra. It is proved that any single slice of the bispectrum carries sufficient information to estimate the impulse response of a complex valued system within a time shift, as long as the chosen slice is not parallel to any one of the frequency axes or to the diagonal at 135 degrees. The author also derives identifiability criteria associated with complex valued signals that admit finite-dimensional ARMA representations. One-dimensional techniques are proposed for signal reconstruction from bispectrum slices and their performance is investigated through Monte-Carlo simulations. The slices required for the proposed method can be estimated on a polar raster directly from observations, avoiding the heavy computational burden associated with cumulant estimates. The freedom to choose arbitrarily oriented and shifted slice(s) allows bispectrum regions dominated by larger estimation variance and higher noise to be avoided     

Feasibility study of parameter estimation of random sampling jitter using the bispectrum

An actual sampling process can be modeled as a random process, which consists of the regular (uniform) deterministic sampling process plus an error in the sampling times which constitutes a zero-mean noise (the jitter). In this paper we discuss the problem of estimating the jitter process. By assuming that the jitter process is an i.i.d. one, with standard deviation that is small compared to the regular sampling time, we show that the variance of the jitter process can be estimated from thenth order spectrum of the sampled data,n=2, 3, i.e., the jitter variance can be extracted from the 2nd-order spectrum or the 3rd-order spectrum (the bispectrum) of the sampled data, provided the continuous signal spectrum is known. However when the signal skewness exceeds a certain level, the potential performance of the bispectrum-based estimation is better than that of the spectrum-based estimation. Moreover, the former can also provide jitter variance estimates when the continuous signal spectrum is unknown while the latter cannot. This suggests that the bispectrum of the sampled data is potentially better for estimating any parameter of the sampling jitter process, once the signal skewness is sufficiently large.     

Analysis of UWB Echoes Using Bispectrum Model

According to the features of the echoes of the ultra-wide band radar, this paper analyses the estimating effects of choosing the AR model, MA model and ARMA model based on bispectrum. It shows that ARMA model is much better than others. Simulations verify this result, also demonstrate the estimation precision has been improved. ARMA model parametric bispectrum estimation is very efficient to echoes of ultra-wide band radar.