一种DTMB系统信道估计改进算法

为减小拖尾效应和加性噪声对数字电视地面广播(DTMB)系统的信道估计精度的不利影响,针对DTMB系统帧头模式2下的信道估计,提出一种改进的最小二乘(LS)信道估计算法.该算法采用试凑法从DTMB系统信号帧中PN帧头序列内选取最佳的一段PN序列,利用所选取最佳PN序列构造用于改进的LS信道估计算法的最优频域子载波,使用改进的LS信道估计算法获取信道的脉冲响应估计初值;并根据信号的正交振幅调制(QAM)方式,选取最佳噪声门限对信道的脉冲响应估计初值进行时域滤波去噪,以获得信道脉冲响应终值.仿真结果表明,该算法可有效减小拖尾效应和加性噪声对信道估计精度的影响,提高DTMB系统在帧头模式2下信道估计的精度.     

Identification/reduction to a balanced realization via the extendedimpulse response gramian

A new identification/reduction algorithm for linear, discrete time-invariant (LDTI) systems is proposed which is based on the extended impulse response gramian first defined here for LDTI systems. The reduction algorithm employs singular value decomposition to retain states corresponding to dominant singular values of these gramians. The proven properties of the reduced order models include convergence to a balanced realization with conditional controllability, observability, and asymptotic stability. A suboptimal property of the model (in minimizing an impulse response error norm) is also demonstrated. The proposed technique can handle impulse response data of deterministic or stochastic nature for system identification application     

基于检测的脉冲噪声滤除方法

为了将柴油机瞬时转速信号中的脉冲噪声滤除,使用了基于检测的脉冲噪声滤除方法。在已有的检测方法基础上,计算被检测点与中值之差将已有的检测方法可能漏掉的脉冲噪声检测出来,改进了脉冲噪声的检测方法来保护信号的细节信息。而且此方法对脉冲噪声干扰较为严重的信号特别有效。使用本方法对含有大量脉冲噪声的柴油机瞬时转速信号的滤波结果表明,改进的脉冲噪声检测法能够更准确地检测出脉冲噪声,更好地保护信号中的细节信息。     

混响环境下基于倒谱BRIR的双耳互相关声源定位算法

在实际封闭环境中,针对存在混响而导致声源定位性能下降的问题,提出一种基于倒谱双耳房间脉冲响应（Binaural room impulse response,BRIR）的双耳互相关声源定位方法.该方法通过从倒谱BRIR中减去混响分量,然后反变换到时域得到估计的脉冲响应,再与数据库中的头部脉冲响应（Head related impulse response,HRIR）进行互相关运算,最大互相关值相对应的位置就是所估计的声源位置.仿真实验结果表明,提出的算法能减少混响环境中带来的定位误差,提高声源定位的精度.     

A new control method of nonlinear systems based on impulseresponses of universal learning networks

A new control method of nonlinear dynamic systems is proposed based on the impulse responses of universal learning networks (ULNs), ULNs form a superset of neural networks. They consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm is derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. One of the distinguished features of the proposed control method is that the impulse response of the systems is considered as an extended part of the criterion function and it can be calculated by using the higher order derivatives of ULNs. By using the impulse response as the criterion function, nonlinear dynamics with not only quick response but also quick damping and small steady state error can be more easily obtained than the conventional nonlinear control systems with quadratic form criterion functions of state and control variables.     

A family of gain-combined proportionate adaptive filtering algorithms for sparse system identification

The classical proportionate adaptive filtering (PAF) algorithms achieve a fast initial convergence for sparse impulse response. But the small coefficients receive very little gain so that the time needed to reach steady-state misalignment is increased. In addition, the PAF algorithms converge much slower than the original adaptive filtering (OAF) algorithms when the impulse response is dispersive. In order to address these problems, this paper proposes a family of gain-combined PAF (GC-PAF) algorithms. The gain-combined matrix of the proposed GC-PAF algorithms is implemented by using a sigmoidal activation function to adaptively combine the proportionate matrix and identity matrix, which can retain the advantages of both the PAF algorithms in the context of sparse impulse response and the OAF algorithms in the context of dispersive impulse response. Meanwhile, to be also applicable to the family of sign algorithms against impulsive noise, a general framework for the update of the sigmoidal activation function is obtained by using the gradient descent method to minimize the $L_1$-norm of the system output error. Simulations in the contexts of three different sparsity impulse responses have shown that the proposed GC-PAF algorithms perform much better than the OAF, PAF and improved PAF (IPAF) algorithms.     

Modeling of transient bending wave in an infinite plate and its coupling to arbitrary shaped piezoelements

Estimating the location and energy of impacts is of primary importance for assessing the condition of structures. Particularly, such estimation can be easily obtained from the energy flow in the structures, which is usually derived from the Poynting vector. In order to measure the Poynting vector in a thin plate using piezoelements bonded on the plate, an analytical formulation of the impulse response in thin infinite plates is presented. The knowledge of the impulse response of any linear time invariant (LTI) system is precious information for the determination of its behavior under arbitrary inputs. When dealing with propagation, and especially mechanical wave propagation, a common approach consists in using numerical methods that are often time-consuming, especially for multi-coupled systems. This paper proposes a new approach for modeling the impulse response of an infinite plate with surface-bonded piezoelectric elements. The proposed analytical formulation allows bypassing numerical analysis drawbacks, in particular instabilities occurring at high frequencies, case-dependent systems and computational requirements, while giving the response for any time and space domain values using a simple convolution. The proposed model relies on flexural wave decomposition over the spatial frequency domain and corresponds to a time generalization of the angular spectrum theory, thus introducing flexural wave propagation as a time-varying spatial filter. Once the impulse is know in the spatial frequency domain, the inverse Fourier transform is applied and leads to the impulse response in the physical domain. From this model, an analytical expression of the impulse voltage response of the piezoelectric transducers and the Poynting vector can be derived quite easily. The predicted impulse response is then compared to FEM simulation results and experimental measurements in order to assess the model.     

高分辨率SAR辐射定标中两种角反射器响应测量方法的应用对比研究

利用角反射器进行SAR图像辐射定标是一种简单、实用、精度较高的绝对定标方法。其关键技术包括角反射器响应测量、天线方向图拟合和定标常数确定。针对机载分米级高分SAR影像定标问题,在同一场景中布设了30和10cm两组三面角反射器,依据雷达中心入射角估计值、均方根误差和变异系数等指标,分析了已有的两种角反射器响应测量方法,即峰值法和积分法的适用性,并提出了一种新的天线方向图拟合方法。最后给出了两种方法对高分辨率SAR辐射定标的适用性结论。     

基于脉冲响应的输出误差模型的辨识

基于系统脉冲响应参数, 利用相关分析方法, 提出了一种辨识输出误差模型参数的方法. 该方法是利用有限脉冲响应模型逼近输出误差模型, 通过依次递增脉冲响应参数的数目N来提高逼近精度. 理论分析表明, 只要N足够大, 模型的辨识精度可以满足实际要求. 提出的辨识方法可以在假设阶次N =1的条件下, 依次递增计算N较大时的脉冲响应参数和目标函数值, 从而根据脉冲响应确定系统的参数. 仿真试验说明提出的方法估计输出误差模型的参数是有效的.     

1/2分数阶线性随机动力系统的非平稳响应解析解

提出随机激励作用下1/2分数阶线性系统非平稳响应解析解的一种新方法.首先,利用特征向量展开得到1/2分数阶阻尼系统的脉冲响应函数解析表达;之后,基于Laplace变换计算得到响应功率谱密度的解析表达式和系统均方响应.通过白噪声、调制白噪声和调制修正金井清谱三种不同随机激励类型的数值算例,利用与蒙特卡洛模拟所得结果对比证明该方法的准确性和适用性.     

基于盲辨识和盲均衡的直扩信号伪码估计方法

为了解决多径信道下直接序列扩频信号的伪码盲估计问题,在直扩信号等效联合信道模型的基础上,提出一种信道盲辨识与数据重用盲均衡相结合的伪码盲估计方法。该方法利用信道盲辨识方法估计得到等效联合信道,通过数据重用盲均衡进一步估计出伪码。理论分析与仿真验证表明,该方法在低信噪比条件下仍然可实现对多径直扩信号伪码序列的准确盲估计,并且对于m、Gold、M序列等不同伪码具有广泛适应性。     

MIMO双线性系统稳定性研究的新方法

基于Volterra级数理论,计算出MIMO双线性系统的n阶冲激响应函数矩阵(或n阶 Volterra核矩阵).通过研究它的n阶冲激响应函数矩阵的性质,给出了一些简单的开环稳定性 判据;并深入研究了它们的闭环稳定性,给出了直接利用其子系统的开环稳定性来判别其闭环 系统稳定性的判据;最后,用仿真实例来验证其有效性.     

辨识传递函数的相关——最小二乘数

本文提出一种由脉冲响应序列g(?)(kT)估计传递函数W(s)的最小二乘法。将系统微分方程积分,得到积分方程。由输入σ(t)、输出g_0(kT)用梯形积分法可求出各采样时刻的各积分项的值,便可用最小二乘法估计系统的参数及W(s)。系统的阶数可由是否出现零点、极点对消而确定。本文给出对一阶网络及对电热丝加热炉辨识的实验结果,它们表明本文所提的方法是有效的。     

一种脉冲噪声图像复原算法

针对被脉冲噪声污染的观测图像提出了一种基于自相似灰度校正的自适应图像复原算法．该算法首先用有选择的中值滤波器对脉冲噪声进行抑制,然后利用图像的自相似性对像素值进行校正以克服中值滤波器造成的图像局部区域像素相关性增加对图像复原处理产生的不利影响．用规整化方法进行图像复原处理,并且使用在灰度校正过程中得到的竞争因子对图像局部区域的统计量进行加权来产生规整化参数,并使其在迭代过程中自适应的更新．实验结果表明,该算法获得的复原图像具有良好的客观评价指标和主观视觉效果．     

一种求解雷达目标冲激响应的并行算法

雷达目标的冲激响应是目标识别中的一种重要手段，可以从冲激响应中提取目标的各种特征，如目标的谐振频率等。为了实时地获得目标的冲激响应，本文提出了一种基于共轭梯度法的并行算法，同时改进了共轭梯度法的终止选代条件。经计算机仿真及对实测数据的计算，证明了这种方法的有效性。     

基于近似偏最小一乘的闭环系统辨识新方法

本文基于近似最小一乘准则和主成分分析,针对反馈通道模型阶次低于前向通道模型阶次且反馈通道不存在噪声的闭环系统,进行了近似偏最小一乘递推辨识算法的推导.为解决最小一乘准则函数不可微的问题,本文算法用确定性可导函数近似代替残差绝对值.近似偏最小一乘辨识算法可以克服基于最小二乘准则的辨识算法在受到满足(SαS)分布的尖峰噪声干扰时残差平方项过大的缺点,具有目标函数可导,计算简单的优点.同时,通过主成分分析去除数据向量各元素之间的线性相关,可以得出模型参数的唯一解.仿真实验表明,本文算法可以对反馈通道模型阶次低于前向通道模型阶次的闭环系统进行直接辨识,抑制了尖峰噪声对辨识结果的影响,具有优良的稳健性,可以更好地应用于闭环系统辨识.     

Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.     

Direct data-driven filter design for uncertain LTI systems with bounded noise

This paper investigates the filter design problem for linear time-invariant dynamic systems when no mathematical model is available, but a set of initial experiments can be performed where also the variable to be estimated is measured. Instead of using the initial experimental data to identify a model on the basis of which a filter is designed, these data are used to directly design a filter. Assuming norm-bounded disturbances and noises, a Set Membership formulation is followed. For classes of filters with exponentially decaying impulse response, approximating sets are determined that guarantee to contain all the solutions to the optimal filtering problem, where the aim is the minimization of the induced norm from disturbances to the estimation error. A method is proposed for designing almost-optimal linear filters with finite impulse response, whose worst-case filtering error is at most twice the lowest achievable one. In the H_∞ SISO case, an efficient technique is presented, that allows the evaluation of bounds on the guaranteed worst-case filtering error of the designed filter. Numerical examples illustrate the effectiveness of the proposed solution.     

Fractional-order impulse response of the respiratory system

This paper presents the measurement, frequency-response modeling and identification, and the corresponding impulse time response of the human respiratory impedance and admittance. The investigated adult patient groups were healthy, diagnosed with chronic obstructive pulmonary disease and kyphoscoliosis, respectively. The investigated children patient groups were healthy, diagnosed with asthma and cystic fibrosis, respectively. Fractional order (FO) models are identified on the measured impedance to quantify the respiratory mechanical properties. Two methods are presented for obtaining and simulating the time-domain impulse response from FO models of the respiratory admittance: (i) the classical pole-zero interpolation proposed by Oustaloup in the early 90s, and (ii) the inverse discrete Fourier Transform (DFT). The results of the identified FO models for the respiratory admittance are presented by means of their average values for each group of patients. Consequently, the impulse time response calculated from the frequency response of the averaged FO models is given by means of the two methods mentioned above. Our results indicate that both methods provide similar impulse response data. However, we suggest that the inverse DFT is a more suitable alternative to the high order transfer functions obtained using the classical Oustaloup filter. Additionally, a power law model is fitted on the impulse response data, emphasizing the intrinsic fractal dynamics of the respiratory system.     

一种新型的非参数模型及其在系统辨识中的应用

本文给出了一个求解广义正交多项式的微分运算矩阵的新方法，应用对连续线性系统的脉冲响应函数进行正交逼近的方法来讨论脉冲响应函数的实现问题，得到了一类新型的非参数模型，并导出了利用该模型来辨识连续线性系统的脉冲响应函数的算法，最后给出了例子证实本文所给方法的有效性。