Blind identification and DOA estimation for array sources in presence of scattering

A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.According to the different characters of covariance matrix and general steering vector of the array received source,a second order blind identification method is used to separate the sources,the mixing matrix could be obtained.From the mixing matrix,the type of the source is identified by using an amplitude criterion.And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.Computer simulations validate the efficiency of the method.

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.

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

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.

BLIND IDENTIFICATION OF A CLASS OF NONLINEAR SYSTEMS WITH CYCLOSTATIONARY INPUT

This letter deals with blind identification of nonlinear discrete Hammerstein system under the input signal that is cyclostationary. The first-order moment of the specific input as well as the inverse nonlinear mapping of the Hammerstein model are combined to establish a relationship between the system output and the system parameters, which implies an approach to identifying the system blindly. Simulation results demonstrate the effectiveness of this approach to blind identification of a class of nonlinear systems.

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.

Distributed Fault Detection for Consensus in Second-Order Discrete-Time Multiagent Systems with Adversary

This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output （SIMO） FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares （Bi-LS） subspace tracking method. The new algorithm requires only a computational complexity of O（md2） at each iteration, or even as low as O（md） if only equalization is necessary, where m is the dimension of the received data vector （or the row rank of channel matrix） and d is the dimension of the signal subspace （or the column rank of channel matrix）. To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.

A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

BLIND AND COMPLETE MODELING OF LINEAR SYSTEMS USING THIRD ORDER CUMULANTS

This paper presents a novel approach to structure determination of linear systems along with the choice of system orders and parameters. AutoRegressive （AR）, Moving Average （MA） or AutoRegressive-Moving Average （ARMA） model structure can be extracted blindly from the Third Order Cumulants （TOC） of the system output ts, where the unknown system is driven by an unobservable stationary independent identically distributed （i.i.d.） non-Gaussian signal. By means of the system order recursion, whether the system has an AR structure or has AR part of an ARMA structure is firstly investigated. MA features in the TOC domain is then applied as a threshold to decide if the system is an MA model or has MA part of an ARMA model. Numerical simulations illustrate the generality of the proposed blind structure identification methodology that may serve as a guideline for blind, linear system modeling.

Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

Blind Signal Processing: I-Fundamental Concepts

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Blind Signal Processing in Telecommunication Systems Based on Polynomial Statistics

The tendencies of the contemporary communication systems development are characterized by the increasingly stringent requirements for maximum channel utilization. Considering discrete communication systems in channels with intersymbol interference identification with the use of training signal is the key technology to create various types of equalizers. However, the time (from 20% to 50%) spent on training signal is increasingly attractive resource for upgrading standards TDMA, especially in mobile systems. An alternative method to training signal is blind signal processing.

BSP:Ⅱ- Blind Signals Separation

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基于盲源分离的工业谐波源负荷分类识别方法

针对传统谐波源辨识方法无法实现工业用户谐波源具体类型的非侵入式识别的问题,该文提出一种基于盲源分离的工业谐波源负荷分类识别方法。该方法仅依据工业用户进线处电压、电流数据即可实现谐波源负荷具体类型的非侵入式识别。首先,从负荷等值阻抗模型入手,建立工业用户多负荷等值阻抗并联电路模型;其次,采用集合经验模态分解与奇异值分解相结合的方法确定构成用户进线处监测点综合等值阻抗信号的源阻抗信号数目;然后,采用快速独立分量分析实现将谐波源负荷等值阻抗信号从综合负荷等值阻抗信号中分离;最后,将分离出谐波源负荷等值阻抗信号频率特征与典型谐波源负荷进行匹配,进而实现分类识别。仿真与实测实验结果均表明,所提方法能够准确识别工业用户所含多种谐波源负荷的具体类型,具有较好的可行性和实用性。

基于CEEMDAN和CDSSAICA的转向电机噪声信号识别

为解决车载转向电机噪声源识别不准确的问题。本文提出了一种基于自适应噪声的完备经验模态分解、改进樽海鞘群的独立分析方法。首先提出一种改进樽海鞘群的独立分析方法,该方法通过改进Tent混沌映射进行种群初始化,领导者及追随者更新机制分别采用Logistic混沌映射和动态学习;然后通过仿真实验验证该方法比传统的快速独立分析方法和樽海鞘独立分析方法分离效率分别提高4.38%和1.01%;最后采用该联合算法对车载转向电机单通道噪声信号进行分离识别,结果表明该联合算法能有效分离电机振动噪声信号中不同频率的特征信号,稳定工况下电机噪声的主要原因是由转子不平衡以及电磁噪声引起。

基于联合加权谱密度的结构振型频域识别方法

针对传统加权谱密度(weighted spectral density,WSD)法中加权函数指数因子选取不当导致模态振型识别精度降低的问题,建立基于联合WSD的结构振型频域识别方法,提升了WSD方法的识别精度及适用范围。根据盲源分离与振型叠加法的相似性,采用WSD方法对结构振动信号进行解耦与分离;利用预设的多个加权密度矩阵构建三维张量加权谱,并采用平行因子2模型自适应选取加权函数指数因子;在此基础之上,引入最小谱方差评判准则进行评价,从而实现模态振型分离矩阵的精确估计。三自由度系统理论模型的模态振型识别结果表明:所提方法能准确获取各阶模态振型所对应的最优加权函数指数因子,针对结构模态振型的识别精度均优于传统频域分解法和二阶盲辨识方法,即使在数据较短、频谱毛刺严重时仍具有较高的识别精度,为建筑结构模态振型识别提供了一种新的识别算法。

基于编码矩阵估计的极化码参数盲识别算法

针对当前极化码参数识别算法缺少对码字起点的识别以及识别信息位算法计算复杂的问题,提出一种基于编码矩阵估计的极化码参数盲识别算法。所提算法首先将截获的码字矩阵、相应码长下的克罗内克矩阵以及逆向重排矩阵相乘得到编码矩阵估计,然后通过编码矩阵的分布特征识别出码长和码字起点,最后使用训练好的卷积神经网络对极化码信息位以及冻结位进行识别。实验结果表明,所提方法不仅完成了码字起点的识别,而且在未知码字起点的情况下完成了对码长的识别,且码长的识别准确率优于现有算法,误比特率在0.19时,参数为(32,12)的极化码码长识别率仍然可以达到90%以上。

基于奇异谱和稳健独立分量分析的星载AIS接收信号分离算法

[目的]在高密度流量地区,船舶经常出现自动识别系统(AIS)信号碰撞的问题,故对接收机的分离性能和实时性能均提出了很高的要求。[方法]针对不同信噪比(SNR)下的混合信号,提出一种基于奇异谱分析(SSA)与稳健独立分量分析(RobustICA)的分离算法S-RICA。通过对单通道AIS信号的Hankel矩阵分别开展奇异值分解和时间序列重构,并利用奇异谱分析代替传统的独立成分分析(ICA)中的白化预处理,再采用峰度对比函数来计算分离矩阵每次迭代的最优步长,从而快速获取最优分离矩阵。[结果]仿真实验结果表明,当信号长度改变时,S-RICA的信号均方误差均可稳定在1.5左右,而快速独立分量分析(FastICA)算法则极不稳定;当SNR为0~9 dB时,S-RICA的误码率为0.97×10^(-2)~1.97×10^(-2),其性能较RobustICA和FastICA提升了1个数量级,且其在SNR为0~7 dB时比S-FICA提高了4~6 dB;S-RICA的平均计算时间和迭代次数分别为18.5 ms和13.6次左右,具有明显的优势。[结论]在样本容量和SNR变化的情况下,S-RICA均表现出更为优异的分离性能,研究成果可为S-RICA在未来星载AIS系统中工程应用提供参考。

基于复杂度追踪的模态参数识别方法对比研究

复杂度追踪(complexity pursuit, CP)是求解振动信号盲源分离(blind source separation, BSS)问题的一类经典方法。用复杂度追踪估计解混矩阵主要有基于源信号复杂度计算的梯度下降(complexity pursuit-gradient descent, CP-GD)算法和基于时间可预测度的广义特征值分解(temporal predictability-generalized eigenvalue decomposition, TP-GED)算法。当前,这两种算法的关联性与算法性能尚缺乏研究,因此对这两种算法的等价性和计算性能进行了研究。首先,给出CP-GD和TP-GED两种算法的具体理论及算法流程;其次,利用二、三自由度振动系统直观地展示并对比解混向量对应的源信号复杂度及可预测度的变化规律;最后,通过对多工况下多自由度系统的模态参数识别算例,对比研究两种算法的精度及计算量。研究结果表明:在低阻尼比及高信噪比条件下,两种方法得到的解混矩阵是相同的;考虑到计算信号复杂度和梯度下降较为耗时,CP-GD算法计算代价要高于TP-GED算法。