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.

Robust Blind Separation for MIMO Systems against Channel Mismatch Using Second-Order Cone Programming

To improve the deteriorated capacity gain and source recovery performance due to channel mismatch problem,this paper reports a research about blind separation method against channel mismatch in multiple-input multiple-output(MIMO) systems.The channel mismatch problem can be described as a channel with bounded fluctuant errors due to channel distortion or channel estimation errors.The problem of blind signal separation/extraction with channel mismatch is formulated as a cost function of blind source separation(BSS) subject to the second-order cone constraint,which can be called as second-order cone programing optimization problem.Then the resulting cost function is solved by approximate negentropy maximization using quasi-Newton iterative methods for blind separation/extraction source signals.Theoretical analysis demonstrates that the proposed algorithm has low computational complexity and improved performance advantages.Simulation results verify that the capacity gain and bit error rate(BER) performance of the proposed blind separation method is superior to those of the existing methods in MIMO systems with channel mismatch problem.

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.

Criterion for Blind Signals Separation Based on Correlation Function

Blind separation of source signals usually relies either on the condition of statistically independence or involving their higher-order cumulants. The model of two channels signal separation is considered. A criterion based on correlation functions is proposed. It is proved that the signals can be separated, using only the condition of noncorrelation. An algorithm is derived, which only involves the solution to quadric nonlinear equations.

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.

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 FastlCA 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 FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.

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.

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A robust clustering algorithm for underdetermined blind separation of sparse sources

In underdetermined blind source separation, more sources are to be estimated from less observed mixtures without knowing source signals and the mixing matrix. This paper presents a robust clustering algorithm for underdetermined blind separation of sparse sources with unknown number of sources in the presence of noise. It uses the robust competitive agglomeration （RCA） algorithm to estimate the source number and the mixing matrix, and the source signals then are recovered by using the interior point linear programming. Simulation results show good performance of the proposed algorithm for underdetermined blind sources separation （UBSS）.

A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a set of correlation matrices is generated using time-shifted, analytic data and assembled into several Hankel matrices. Dissimilar left and right matrices are found, which diagonalize the set of nonhermetian Hankel matrices. The complex-valued modal matrix is obtained from this decomposition. The modal responses, modal auto-correlation functions and discrete-time plant matrix (in state space modal form) are subsequently identified. System eigenvalues are computed from the plant matrix to obtain the natural frequencies and modal fractions of critical damping. Joint Approximate Diagonalization (JAD) of the Hankel matrices enables the under determined (more modes than sensors) problem to be effectively treated without restrictions on the number of sensors required. Because the analytic signal is used, the redundant complex conjugate pairs are eliminated, reducing the system order (number of modes) to be identified half. This enables smaller Hankel matrix sizes and reduced computational effort. The modal auto-correlation functions provide an expedient means of screening out spurious computational modes or modes corresponding to noise sources, eliminating the need for a consistency diagram. In addition, the reduction in the number of modes enables the modal responses to be identified when there are at least as many sensors as independent (not including conjugate pairs) modes. A further benefit of the algorithm is that identification of dissimilar left and right diagonalizers preclude the need for windowing of the analytic data. The effectiveness of the new modal identification method is demonstrated using vibration data from a 6 DOF simulation, 4-story building simulation and the Heritage court tower building.

BLIND SPEECH SEPARATION FOR ROBOTS WITH INTELLIGENT HUMAN-MACHINE INTERACTION

Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.

A TIME-FREQUENCY BLIND SEPARATION METHOD FOR UNDERDETERMINED SPEECH MIXTURES

The proposed Blind Source Separation method(BSS),based on sparse representations,fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources.The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique(DUET) which assumes the number of sources a priori.In the proposed algorithm,the Short-Time Fourier Transform(STFT) is used to obtain the sparse rep-resentations,a clustering method called Unsupervised Robust C-Prototypes(URCP) which can ac-curately identify multiple clusters regardless of the number of them is adopted to replace the histo-gram-based technique in DUET,and the binary time-frequency masks are constructed to separate the mixtures.Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio(SIR),and maintains good speech quality in the separation results.

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。

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

基于双阵元天线的ADS-B解交织投影算法

随着广播式自动相关监视(Automatic Dependent Surveil lance-Broadcast,ADS-B)技术的普及,交通密集区域内多条ADS-B信号的交织问题难以避免。为了降低设备成本并提高系统在低成本条件下的可用性,针对双阵元天线三条ADS-B信号交织问题,提出了双阵元天线投影算法。首先,上述方法基于频域插值理论与最小描述长度准则进行信源数估计;然后,采用功率倒置算法进行空域滤波,在未知信号波达方向的条件下抑制非期望信号,从而将双天线三信号的解交织问题转化为单天线双信号的解交织问题;最后,由虚拟多通道法扩维后,基于改进的投影算法分离交织信号。仿真验证了以上算法的有效性。

基于两步单源点筛选的改进退化解混和估计算法

退化解混和估计(Degenerate unmixing estimation technique,DUET)算法是一种典型的欠定盲源分离算法,其采用的二进制时频掩蔽会保留部分干扰信号。提出了基于两步单源点筛选的改进DUET算法,首先使用余弦角算法进行单源点筛选,再采用计算相似度的方法进行第二步单源点筛选。通过两步单源点筛选获得更精确的目标信号和干扰信号后,设计用于抵消干扰信号的滤波器取代DUET中的二进制时频掩蔽,达到抑制干扰信号和提取目标信号的目的。仿真实验结果表明,该方法在正定盲源分离和欠定盲源分离两种情况下都有较优的盲源分离性能。

一种卫星隐蔽通信信号盲分离算法

重构抵消算法是卫星隐蔽通信信号分离的关键技术,算法的性能主要依赖于参数估计的精度。然而在实际环境中,参数估计误差带来的算法性能损失无法被避免。此外,对信号的重构使得该算法计算复杂度较高。针对这个问题,首先分析了参数估计误差对分离性能的影响,然后提出基于盲均衡算法的协作分离算法,提升信号分离性能的同时降低了算法的计算量。仿真实验表明,新算法相较于重构抵消算法,降低了对参数估计精度的依赖,当参数估计误差大于0.05时,信号的解调误码率降低了一个数量级左右。

基于JADE-斜投影的鲁棒波束形成算法

针对矩阵重构类波束形成算法对阵列幅相误差敏感的问题,提出一种基于盲源信号分离和斜投影的矩阵重构鲁棒波束形成算法。首先,依靠盲源分离技术得到接收信号和混合矩阵,结合期望信号先验信息完成混合矩阵中信号导向矢量的搜索。然后,利用盲源分离得到的信号协方差矩阵完成阵列幅相误差估计。最后,基于幅相误差校准的混合矩阵和斜投影思想,构建各干扰的斜投影算子,将接收数据分别向干扰斜投影空间进行投影,得到对应的干扰信号,完成干扰噪声协方差矩阵重构。仿真结果表明,所提方法对阵列幅相误差具有较好的鲁棒性,验证了算法的有效性。