桅杆结构风振响应分析

本文根据一工程实例，建立了一个格构式桅杆的空间动力模型，利用Monte Carlo数值模拟对桅杆的非线性风振响应进行了计算。根据计算结果，对桅杆结构在风荷载作用下的响应特性进行了分析，并根据桅杆结构风振实测结果，对桅杆结构风振响应的非高斯性和宽带性进行了分析。     

Radar detection of high-energy cosmic rays in non-Gaussian background using a time-frequency technique

Cosmic rays are the highest-energy observable particles in the universe. Their study opens a new frontier for scientists to better understand the nature of the universe. This paper reports our study of a bistatic radar approach that is being developed for remote sensing of cosmic-ray induced air showers. In this context, we propose a robust detection technique based on time-frequency domain for the received radar echoes. These echoes are modeled as linear-downward chirp signals, characterized by very short sweep periods, low energies, and corrupted by non-stationary and non-Gaussian background noise. In addition, the related parameters of the received echoes are variable within some expected ranges, determined by the physical parameters of the air showers. In this paper, we explore the performance of the proposed detection method through an extensive theoretical analysis. We derive formulae for probability of the correct detection, as well as false-alarm rate. Numerical simulations and experimental results that corroborate our analysis are also presented.     

Sparse Bayesian Learning for non-Gaussian sources

In this paper, we show that in the multiple measurement vector model we can take advantage of having multiple samples to learn the properties of the distributions of the sources as part of the recovery process and demonstrate that this improves the recovery performance. We propose a method to solve the simultaneous sparse approximation problem using a mixture of Gaussians prior, inspired by existing Sparse Bayesian Learning approaches. We justify our proposed prior by showing that there are a number of signals modelled better by a mixture of Gaussians prior than the standard zero-mean Gaussian prior, such as communications signals which often have a multimodal distribution. We further show that this method can be applied to data distributed according to an alpha-stable distribution. We also show that our proposed method can be applied to compressed sensing of ultrasound images and demonstrate an improvement over existing methods.     

A novel clipping technique for filtering FM signals embedded in intensive noise

A novel adaptive clipping technique for filtering a constant amplitude frequency modulated (FM) signal embedded in non-Gaussian noise is proposed. It is based on the analysis and processing of the estimate of probability density function of a FM signal realization. As a result, modifications of two robust estimators of FM signal amplitude are proposed. It is shown that these estimators can be used for Gaussian and non-Gaussian heavy-tail environments. The proposed clipping technique can exploit one or another obtained robust estimate of the signal amplitude for adaptive setting a threshold. Analysis of signal estimate accuracy for different noise environments is carried out. Comparative analysis of the obtained methods and known approaches based on scanning window nonlinear filtering and optimal robust L-DFT form is performed. It is demonstrated that the usage of clipping-based technique leads to the considerable improvement of the FM signal filtering efficiency in comparison to the aforementioned known approaches for different noise environments and a wide range of input SNR values.     

Evaluation of bistable systems for binary signal detection in symmetric non-Gaussian noise

For detecting binary signals in symmetric noise with unknown probability density functions (PDF), a nonlinear receiver is proposed based on the bistable systems with autoregressive models of order 1 [AR(1)]. The bistable systems are utilized to pre-process the noisy observations ahead of the linear correlation (LC) detector. The permutations of the observations are utilized to bypass the design of the optimal LC vector which depends on the noise PDF. The detection performances, in the form of probabilities of error, in some non-Gaussian noise are evaluated versus the matched filter (MF) and Volterra filter (VF) through numerical simulations. The results show that the bistable receiver performs better than MF receiver when the noise deviates from Gaussian distribution, and seems more robust compared to the VF receiver.     

基于典型因果推断算法的无线网络性能优化

现有的无线网络性能优化方法主要基于指标间的相关关系分析,无法有效指导网络优化等干预行为。为此,提出典型因果推断（CCI）算法,并将其应用于无线网络性能优化。首先,针对无线网络性能由大量相关指标体现这一特性,采用典型相关分析（CCA）方法,提取指标中蕴含的原子事件;然后再采用因果推断方法,构建原子事件间的因果关系网络。通过上述两个阶段反复迭代,确定原子事件间的因果关系网络,为无线网络性能优化提出一个较为可靠和有效的依据。最后通过模拟实验验证了CCI算法的有效性,在某城市3万多个移动基站数据上发现了一批有意义的无线网络指标间的因果关系。     

一种适用于任意峰度非高斯信号的多目标盲波束形成方法

该文提出了一种新的适用于任意峰度非高斯信号的多目标盲波束形成算法。该方法在最大峰度算法(KMA)的基础上,引入信号的互相关约束定义了一种新的代价函数。通过对该代价函数的最大化,来盲估计波束形成器的权向量,从而完成对多目标的分离和定向。由于波束形成过程不需要正交化处理,避免了波束形成器锁定噪声的可能性。同时,采用一种复数编码的遗传算法来求解权向量,不但可避免陷入局部极值的可能性,而且还能有效提高运行速度。仿真实验验证了该算法的有效性和正确性。     

宽带非高斯信号的二维波达方向估计方法

该文提出了一种解决宽带非高斯信号二维波达方向估计的方法。该方法利用相干信号子空间方法把宽带的阵列频域数据四阶累积量聚焦到同一个参考频段下,然后利用基于信号空时特征结构的时空DOA 矩阵方法来进行二维DOA估计。理论上证明该方法在高斯噪声环境下对宽带非高斯信号都具有很好的估计性能,通过计算机仿真也验证了该方法的有效性。     

Computation of response moments of high dimensional MDOF structure excited by stationary non-Gaussian random fields

Output moments of a non-linear dynamical system excited by a non-Gaussian random field can be obtained in practice only by simulation techniques. When the dynamical system can be decomposed in a low dimension non-linear part acting on a high dimension linear part, the original problem reduces to calculate output moments of a high dimension linear system. The proposed method suggests that work should be directed in the frequency domain. Time trajectories are then obtained through Fourier transform. Such a procedure does not introduce any approximation errors due to the time integration numerical scheme nor does it introduce any transient state. Further quasi-static correction terms can be introduced when a truncated modal basis is utilized in order to describe the low frequency dynamic response.     

Parametric identification of systems with non-Gaussian excitation using measured response spectra

The problem of estimating parameters in dynamic systems excited by stochastic processes is addressed. Attention is focused on situations where the response processes are measurable but the excitation processes are non-Gaussian, unmeasurable and known only in terms of parameterised stochastic process models. General techniques for simultaneously estimating system and excitation process parameters are developed, based on the use of both normal, second order spectra and higher order, trispectra. The method is validated through application to some simulated data, relating to an oscillator driven by two specific kinds of non-Gaussian stochastic excitation.