For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Determined Reverberant Blind Source Separation of Audio Mixing Signals

Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis （FastlCA） method when noise contamination is considerable.

A Second-Order Statistics Based Algorithm for Blind Separation of Signals

We propose an information theory based objective function for measuring the statistics independent of source signals. Then, we develop a learlling algorithm for blind separation of nonstationary signals by minimizing the objective function, in which the property of nonstationary and direct architecture neural network is applied. The analysis demonstrates the equiralence of two neural architectures in some special cases. The computer simulation shows the validity of the proposed algorithm. We give the performance surface of the object function at the last of the paper.

Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

Simultaneous polarization separation and switching for 100-Gbps DP-QPSK signals in backbone networks

We propose a novel scheme of simultaneous polarization separation and switching, based on the orthogonallypolarized four-wave mixing(FWM) effect, for ultra-high-speed polarization multiplexing(Pol-MUX) fiber networks such as 100-Gbps and 400-Gbps backbone networks. We use theoretical and experimental analysis of the vector theory of FWM to successfully achieve polarization separation and all-optical switching by utilizing a 100-Gbps dual polarizationquadrature phase shift keying(DP-QPSK) signal and two orthogonally-polarized pumps. Both of the polarization-separated QPSK signals have clear constellation diagrams, with root mean square(RMS) error vector magnitudes(EVMs) of 14.32%and 14.11% respectively. The wavelengths of idlers can be created at 30 different wavelengths, which are consistent with International Telecommunication Union-Telecommunication(ITU-T) wavelengths, by flexibly changing the wavelength of the pump light. Moreover, the idlers that have distinct wavelengths have power distributed in a range from-10 dBm to-15 dBm, which can support error-free transmission. The power penaltyis 5 d B lower than that of back-to-back(BTB)signal for both the X-and Y-polarization components measured at a bit error ratio(BER) of 3.8×10^(-3). Our experimental results indicate that this scheme has promising applications in future backbone networks.

BLIND SIGNAL SEPARATION BASED ON ME AND STATISTICAL ESTIMATION

There are two major approaches for Blind Signal Separation (BSS) problem: Maximum Entropy (ME) and Minimum Mutual Information (MMI) algorithms. Based on the recursive architecture and the relationship between the ME and MMI algorithms, an Extended ME(EME) algorithm is proposed by using probability density function (pdf) estimation of the outputs to deduce the corresponding iterative formulas in BSS. Based on the simulation results, it can be concluded that the proposed algorithm has better performances than the traditional ME algorithm in convolute mixture BSS problems.

BSP:Ⅱ- Blind Signals Separation

ＢＳＰ：Ⅱ－ＢｌｉｎｄＳｉｇｎａｌｓＳｅｐａｒａｔｉｏｎ￥Ｒｕｅｙ－ｗｅｎＬｉｕ（ＵｎｉｖｅｒｓｉｔｙｏｆＮｏｉｒｅＤａｍｅ，ＮｏｉｒｅＤａｍｅ，ＩＮ４６５５６）Ａｂｓｔｒａｃｔ：ＴｈｅＰｒｏｂｌｅｍｏｆｂｌｉｎｄｓｉｇｎａｌｓｅｐａｒａｔｉｏｎａｎ...

BLIND SIGNAL SEPARATION OF LINEAR MIXTURE USING TRILINEAR DECOMPOSITION

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.

Research on the technical solution based on orthogonal frequency shift signal

By means of analyzing the frequency spectrum characteristics of domestic frequency- shift track signal we propose a technical improvement solution to it based on the orthogonal theory and demodulation technology in modern digital signal processing to guaranteed the solution accomplishes the optimized reception at the receiver, thereby enhance system reliability and quality. At same time we simulated this solution with MATLAB, and implemented it on the vc33 experimental platform of floating- point Digital Signal Processing (DSP).

APPLICATION OF FM^mlet TRANSFORM TO SIGNAL SEPARATION

Since the multicomponent signal with nonlinear time-frequency structures occupies a wide frequency band, and the spectral contents may alias, it is therefore difficult to separate the signal components and to separate the signal from background noise. In this paper, a new signal separation method using FMmlet transform is proposed by taking the advantage that the atoms of FMmlet transform can match both the linear and nonlinear time-varying structures. Theoretical predictions and numerical experiments show the feasibility of the methodology advocated.

A blind source separation algorithm based on negentropy and signal noise ratio

A novel blind source separation (BSS) algorithm based on the combination of negentropy and signal noise ratio (SNR) is presented to solve the deficiency of the traditional independent component analysis (ICA) algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastICA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastICA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.

最优尺寸参数对涡流管能量分离效应影响的研究

为确定出涡流管各因素的主次顺序,找出最优的参数组合,提出正交试验设计和极差分析法,找到涡流管能量分离效应的最优化尺寸参数。实验设定涡流管进气温度为26℃,环境温度为27.1℃,结果表明,4个尺寸参数对涡流管能量分离影响排序为:长径比>喷嘴数目>冷端直径>进气压力,每个参数的最优值各为长径比为25.0、喷嘴个数为6、冷端直径为5 mm、进气压力为0.8 MPa,并通过对试验结果的验证,发现涡流管的冷热端出口温差为95.2℃。根据正交试验设计方案找出了涡流管各部件的最优尺寸参数。

动态变化混叠模型下盲源分离中的源数估计

在进行欠定盲分离时,特别是对于源信号数目及混合矩阵动态变化的情况,常规的欠定盲分离及源数估计方法不能对源信号数目的变化时刻做出判断,因此很难实现动态变化的源信号数目实时和准确的估计。针对这个问题,提出了一种动态变化混叠模型下欠定盲源分离中的源数估计方法。首先,建立动态变化混叠情形下盲源分离的数学模型及动态标识矩阵。其次,基于构建的动态标识矩阵统计和判断动态源信号数目的变化情况。最后,通过分段时间内多维观测矢量采样点聚类区间局部峰值统计,实现动态变化混叠模型下盲源分离中的源信号数目的有效估计。仿真结果表明,该方法能有效实现动态变化混叠模型下欠定盲源分离中的源数估计,并且信号估计效果良好。

基于稀疏编码的复杂机械振动信号盲分离方法

复杂机械振动信号激励源较多,故源信号之间互为相关源,且较难满足统计独立特性,导致传统盲源分离方法分离效果不佳。对此,提出一种基于信号稀疏编码的机械振动信号盲分离方法。盲源分离的关键在于对混合矩阵的精确估计,然而机械振源中相关成分的存在严重影响混合矩阵的估计。对此,首先对观测信号进行短时傅里叶变换,增加信号稀疏性;然后利用稀疏编码筛选出具备直线聚类特性的时频观测点,利用K均值(K-means)聚类法找到聚类中心;最后利用所提筛选规则找到估计的混合矩阵,重构出源信号。通过对往复压缩机故障数据的分析,验证了所提方法有效性。

双锥形油水分离旋流器分离效率正交数值的实验研究

以双锥形油水分离旋流器为研究对象,采用计算流体力学数值模拟方法和正交实验方法,针对旋流器的含油率、进口速度、分流比、大锥段角度和小锥段角度等因素对旋流器分离效率的影响,进行了正交数值实验。结果表明,各因素对分离效率影响的顺序为:分流比>含油率>进口速度>大锥段角度>小锥段角度。含油率10%、进口速度6m·s-1、分流比25%、大锥段角度15°、小锥段角度1°为最优的参数组合,其中旋流器的大锥段角度和小锥段角度的影响不显著,可以根据实际需要进行调整。通过回归分析推导出了含油率、进口速度和分流比对分离效率影响的经验公式,可供设计时使用。