Blind identification of IIR systems based on special SIMO model

Based on oversampling the system output, this paper presents a deterministic approach to blind identification of fast changing infinite-impulse-response (IIR) systems. The contributions of this paper are: 1) we prove that oversampling the output of a single-input-single-output (SISO) IIR system is equal to transforming the SISO IIR system into a single-input-multiple-output (SIMO) IIR model with all subsystems have the same autoregressive (AR) coefficients. Based on this model, a new identification algorithm is proposed, which can give the least-squares approach; 2) we show that in the SIMO model, the number of subsystems can be varied and will affect the identification performance. We also discuss how to choose a proper subsystem number to guarantee the best performance; 3) we deduce the sufficient and necessary conditions for the system to be identifiable associated with the proposed algorithm. Since the proposed approach only needs a small quantity of data samples, it can be used for fast changing IIR systems. Computer simulations give some illustrative examples.     

Blind MIMO system identification based on cumulant subspace decomposition

Blind identification of multiple-input multiple-output (MIMO) linear systems can be achieved by utilizing higher order statistics of the output signals. We study the blind identification of MIMO systems whose inputs are mutually independent, temporally white, non-Gaussian source signals. Based on sub-space analysis, we develop a new linear batch algorithm to identify MIMO systems from the common space of a set of fourth-order cumulant matrices of the channel outputs. Given knowledge of the channel orders, the identifiability conditions required by the proposed algorithm are properly established. Like most subspace-based approaches, this new algorithm remains sensitive to channel order overestimation. Simulation results illustrate its performance for various channel models.     

具有维纳-哈默斯坦模型结构非线性 系统的一种辨识方法

 本文综合应用相关分析法、多维z变换、解卷积和最小二乘法提出了一种新的辨识具有维纳-哈默斯坦模型结构非线性系统的方法.采用这种方法,维纳-哈默斯坦模型中的两个线性子系统以及非线性子系统的参数均可以分别进行辨识估计,算法简单,数据量小.大量仿真结果表明,本文所提出的辨识方法是正确有效的.     

A least-squares parameter estimation algorithm for switched hammerstein systems with applications to the VOR

A "Multimode" or "switched" system is one that switches between various modes of operation. When a switch occurs from one mode to another, a discontinuity may result followed by a smooth evolution under the new regime. Characterizing the switching behavior of these systems is not well understood and, therefore, identification of multimode systems typically requires a preprocessing step to classify the observed data according to a mode of operation. A further consequence of the switched nature of these systems is that data available for parameter estimation of any subsystem may be inadequate. As such, identification and parameter estimation of multimode systems remains an unresolved problem. In this paper, we 1) show that the NARMAX model structure can be used to describe the impulsive-smooth behavior of switched systems, 2) propose a modified extended least squares (MELS) algorithm to estimate the coefficients of such models, and 3) demonstrate its applicability to simulated and real data from the Vestibulo-Ocular Reflex (VOR). The approach will also allow the identification of other nonlinear bio-systems, suspected of containing "hard" nonlinearities.     

Matrix outer-product decomposition method for blind multiplechannel identification

Blind channel identification and equalization have attracted a great deal of attention due to their potential application in mobile communications and digital TV systems. In this paper, we present a new algorithm that utilizes second-order statistics for multichannel parameter estimation. The algorithm is simple and relies on an outer-product decomposition. Its implementation requires no adjustment for single- or multiple-user systems. This new algorithm can be viewed as a generalization of a linear prediction algorithm. It is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimates. The superior performance of this new algorithm is demonstrated through simulation examples     

On identification and control of output-bilinear systems

Linear identification and control design methods yield excellent results if the behavior of the system is essentially linear. Unfortunately, most technical systems are nonlinear. We propose here to use a new bilinear model class, which is able to approximate many nonlinear systems quite accurately and can also be used immediately for well-known controller design techniques.     

激光三维成像技术及其主要应用

阐述了目前三维成像在其常见应用领域中的研究,主要致力于研究高分辨率三维成像系统。三维激光成像是一项可以应用于探测隐藏目标、地形测绘、构建虚拟环境、城市建模、目标识别等领域中的技术。在区域成像技术中,除了如立体视觉和结构化灯光等更常规的技术,实时三维传感也具有现实可操作性。当前三维激光成像技术已经发展到有能力提供厘米级波长的高分辨率三维成像,这将给许多领域提供方便,包括法律的实施和法医调查。与CCD和红外技术等传统的被动成像系统相比,激光成像技术不仅能提供强度和范围信息,还能穿透植被和窗户等特定情景元素。这意味着激光三维成像系统在目标识别与辨认等方面具备新的潜力。结果表明,激光三维成像系统可以在许多情况下得到应用。     

基于机器学习技术的直放站干扰识别方法研究

干扰识别和优化是运营商无线网络优化的重点环节和长期需求。本文提出了一种基于机器学习技术的直放站干扰识别方法,能够大幅提升直放站干扰识别的准确性和排查效率,为难以用规则识别的复杂无线干扰系统提供了一种新的技术实现途径,实验和测试结果证明了该方法的有效性。     

New microcharacters for wafer identification

Conventional identification (ID) systems use characters that are often illegible, with character recognition being difficult in as many as 20% of all processes. In contrast, new microcharacters in the V-shaped notch are clearly recognized throughout the process. The results can be explained by the following multiple effects. Marking location: These markings require only a small space. Hence, markings can even be in the beveled section of the V-shaped notch of a wafer. In the case of conventional ID systems, it is difficult to select an area that gives good readability during processes and wide enough for marking. Dot topography: A marking dot formed by conventional laser marking has a central depression due to the process of general heat distribution. In contrast, a marking dot formed by micro marking has a central peak protruding from the surface that is more easily distinguished than a dot that has a central depression. The contrast of the new dot is about two times greater than that of a conventional one. Contrast greatly influences readability     

四维直接序列扩频调制技术识别方法

针对不同扩频调制技术之间无法互通的问题,结合不同的时频分析工具,从调制信号特征参量角度出发,提出可有效区分新旧扩频调制技术的识别方法,从而达到两种不同扩频调制通信系统之间互通互联的效果。理论分析与仿真结果表明,两种扩频调制信号的时频特征区分度较小,但高阶累积量特征具备明显的区分度,考虑到处理的实时性,利用四阶累积量可以有效解决新旧扩频调制体制的识别问题。     

Fault Diagnosis of Time-Varying Parameter Systems With Application in MEMS LCRs

Multiple-model adaptive estimation (MMAE) is a well-known technique used for model matching of deterministic parameter systems. This technique can be used in fault diagnosis by allocating a model to each type of fault. In each contingency, the model that represents the behavior of the actual system can indicate the type of fault occurrence. Kalman filters are generally used in modeling and residual-signal generation of time-invariant systems. Slowly time-varying parameter systems, however, require a system identification unit in addition to the model-matching core. This paper utilizes the least square forgetting-factor technique in parameter identification of slowly time-varying systems and combines it with MMAE for fault-diagnosis applications in microelectromechanical-systems (MEMS) lateral comb resonators (LCRs). Prescheduled faults were designed for simulations and experimentally examined in real-time implementations of estimation-based diagnosis technique for two fabricated MEMS LCRs. It is shown that the application of a system identification unit significantly increases the performance of the fault diagnosis in MEMS devices.     

基于NIOSII的高速嵌入式指纹识别系统

为了提高传统指纹识别系统处理速度和安全问题,研究一种基于NIOS II处理器的高速嵌入式指纹识别系统。利用NIOS II的定制指令,FPGA实现算法的硬件设计,同时结合先进的射频识别技术(RFID),实现了指纹特征模块在非接触IC卡上的存储,在非接触IC卡采用加密算法加强了指纹数据传输的安全性。实验结果表明,系统运行速度快,有较高的安全性。     

非凸投影自适应Hammerstein滤波算法

该文研究了Hammerstein系统参数辨识和非线性系统预测问题,提出一种基于非凸投影的自适应滤波算法。论文将问题归结为具有非凸可行域的约束优化问题,并建立了基于交替方向乘子法(ADMM)和递归最小二乘相结合的算法框架。在该算法框架下,非凸约束优化问题的全局最优解可通过岭回归和欧几里得(Euclid)投影循环计算得到。将提出的算法分别应用于Hammerstein系统的参数辨识、非线性未知系统预测以及非线性声学回声消除,并进行仿真实验,结果显示所提算法具有较好的收敛性和稳定性,能够得到较准确的辨识和预测效果。     

通信化雷达探测技术

随着雷达探测逐步进入强电子对抗、隐身时代,传统雷达体制在战术主动性、能量、数量方面均处于劣势。雷达亟需从探测体制方面进行创新,充分挖掘其合作式探测的主动性、充分利用信号波形的信息维度优势,才能适应未来新型防空作战。该文提出一种新的雷达体制——通信化雷达,其通过在发射信号波形中嵌入发射站动态位置、天线扫描指向、发射时刻等辅助信息,并在接收处理中提取、利用该信息进行目标检测、定位、识别、抗干扰和多目标分辨,可提升远程、隐身、强对抗条件下的雷达探测能力和战场生存力。该文从系统架构、探测原理、性能分析等方面对通信化雷达进行了阐述。      

Blind MIMO channel identification from second order statistics using rank deficient channel convolution matrix

For multiuser systems, several direct blind identification algorithms require that the linear multiple-input multiple-output (MIMO) system have a full rank convolution matrix. This condition requires that the system transfer function be irreducible and column reduced. We show that this restrictive identification condition can be relaxed for some direct blind identification methods to accommodate more practical scenarios. Algorithms such as the outer-product decomposition algorithm only require minor length adjustment to its processing window without the column-reduced condition. This result allows direct blind identification methods to be applicable to MIMO without requiring a full-rank channel convolution matrix.     

Blind Identification and Signal Separation of Two-Input Two-Output FIR Systems

This paper presents a new kind of higher order statistics (HOS) based approaches for blind identification and signal separation of two-input two-output FIR systems. The approaches consist of eigenanalysis of higher order spectrum matrices of measurements at different frequency slices and then exploiting the structural information of the coupling system to eliminate the identification indeterminacy. It is proved that under some weak restrictions, our approaches can guarantee the uniqueness of solution and lead to the desired solution, which is quite different from other existing methods.     

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

In response to the open-loop instability of magnetic bearing systems, this paper develops a closed-loop identification method to obtain an accurate plant model for controller design. The identification method is based on a decentralized and decoupling control architecture, which allows the decoupling of the multi-input-multi-output (MIMO) system into two single-input-single-output (SISO) systems for simplifying the identification process. The identification uses a pseudo-random binary sequence as the excitation signal. A parameterized model is obtained through a parameter estimation algorithm based on the output error model. To ensure that the model prediction error can converge during the computation process, a filter is incorporated into the estimation algorithm. The filter can be synthesized systematically by solving a set of linear matrix inequalities. The performance of the proposed closed-loop identification method is firstly verified by simulations. Then it is implemented on a five-axis magnetic bearing platform and the parameterized model obtained from the identification is compared to the experimental frequency response.     

Proposal for identity-based key distribution systems

Key distribution systems based on identification information are presented. The systems are analogous to the Diffie-Hellman public key distribution systems in which the former use each user's identification information instead of public files used in the latter. The systems do not require centres to distribute work keys or users to keep directories of key-encrypting keys. Therefore, key management in cryptosystems can be simplified by the identity-based key distribution systems.     

基于多特征有效组合的说话人识别

通过分析当今说话人识别系统中常用的一些特征参数，以提高说话人识别的识别率为目的，在Matlab 6．5软件环境下提出了将Mel频率倒谱(MFCC)、线性预测倒谱(LPCC)及他们的一阶差分和基音周期等多种特征有效结合进行说话人识别的方法。采用短时自相关法提取基音周期，在识别过程中采用改进的动态规整算法，将模板的匹配过程与检验量的计算分离开，每帧给出一个说话人辨认结果，最后综合各帧的辨认结果，得出最佳匹配结果。经过多次实验证明，采用以上方法使用多特征有效结合比单个使用各种特征效果要好，能在一定程度上提高系统区分说话人的能力。     

Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.